High-quality Extragalactic Legacy-field Monitoring (HELM) with DECam

0000-0001-5105-2837]Ming-Yang Zhuang (庄明阳)
Department of Astronomy, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

0000-0002-6893-3742]Qian Yang
Center for Astrophysics $\vert$ Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA
Department of Astronomy, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

0000-0003-1659-7035]Yue Shen
Department of Astronomy, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

0000-0002-6904-359X]Monika Adamów
Center for AstroPhysical Surveys, National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

0000-0002-3632-7668]Douglas N. Friedel
National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

0000-0002-4588-6517]R. A. Gruendl
National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

0000-0003-0049-5210]Xin Liu
Department of Astronomy, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Center for Artificial Intelligence Innovation, University of Illinois at Urbana-Champaign, 1205 West Clark Street, Urbana, IL 61801, USA

0000-0002-4279-4182]Paul Martini
Department of Astronomy, The Ohio State University, Columbus, Ohio 43210, USA
Center of Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, Ohio 43210, USA

0000-0003-1587-3931]Timothy M. C. Abbott
CTIO, NSF’s NOIRLab, Casilla 603, La Serena, Chile

0000-0002-6404-9562]Scott F. Anderson
Astronomy Department, University of Washington, Box 351580, Seattle, WA 98195, USA

0000-0002-9508-3667]Roberto J. Assef
Núcleo de Astronomía de la Facultad de Ingeniería y Ciencias, Universidad Diego Portales, Av. Ejército Libertador 441, Santiago, Chile

0000-0002-8686-8737]Franz E. Bauer
Instituto de Astrofísica, Facultad de Física, Pontificia Universidad Católica de Chile, Campus San Joaquín, Av. Vicuña Mackenna 4860, Macul Santiago, Chile, 7820436
Centro de Astroingeniería, Facultad de Física, Pontificia Universidad Católica de Chile, Campus San Joaquín, Av. Vicuña Mackenna 4860, Macul Santiago, Chile, 7820436
Millennium Institute of Astrophysics, Nuncio Monseñor Sótero Sanz 100, Of 104, Providencia, Santiago, Chile

0000-0001-9070-9969]Rich Bielby
Centre for Extragalactic Astronomy, Department of Physics, Durham University, South Road, Durham, DH1 3LE, UK

0000-0002-0167-2453]W. N. Brandt
Department of Astronomy & Astrophysics, 525 Davey Lab, The Pennsylvania State University, University Park, PA 16802, USA
Institute for Gravitation and the Cosmos, The Pennsylvania State University, University Park, PA 16802, USA
Department of Physics, 104 Davey Laboratory, The Pennsylvania State University, University Park, PA 16802, USA

0000-0001-9947-6911]Colin J. Burke
Department of Astronomy, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

Jorge Casares
Instituto de Astrofísica de Canarias, E-38205 La Laguna, Tenerife, Spain
Departamento de Astrofísica, Universidad de La Laguna, E-38206 La Laguna, Tenerife, Spain

0000-0002-9932-1298]Yu-Ching Chen
Department of Astronomy, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

0000-0003-3242-7052]Gisella De Rosa
Space Telescope Science Institute, 3700 San Martin Dr., Baltimore, MD 21218, USA

0000-0001-8251-933X]Alex Drlica-Wagner
Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, USA
Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
Department of Astronomy and Astrophysics, University of Chicago, Chicago IL 60637, USA

0000-0002-4459-9233]Tom Dwelly
Max-Planck Institute for Extraterrestrial Physics, Giessenbachstrasse 1,  Garching, 85748, Germany

Alice Eltvedt
Centre for Extragalactic Astronomy, Department of Physics, Durham University, South Road, Durham, DH1 3LE, UK

0000-0003-0042-6936]Gloria Fonseca Alvarez
NSF’s NOIRLab, 950 N. Cherry Ave., Tucson, AZ 85719, USA

0000-0002-3767-299X]Jianyang Fu(傅健洋)
Department of Astronomy, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

0000-0002-5211-0020]Cesar Fuentes
Departamento de Astronomía, Universidad de Chile, Camino del Observatorio 1515, Las Condes, Santiago, Chile.
Centro de Excelencia en Astrofísica y Tecnologías Afines (CATA), Chile
Millennium Institute of Astrophysics (MAS), Chile

0000-0002-9154-3136]Melissa L. Graham
DIRAC Institute, Department of Astronomy, University of Washington, 3910 15th Avenue NE, Seattle, WA 98195, USA

0000-0001-9920-6057]Catherine J. Grier
Department of Astronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
Steward Observatory, The University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721, USA

0000-0002-2582-0190]Nathan Golovich
Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA 94550, USA

0000-0002-1763-5825]Patrick B. Hall
Department of Physics & Astronomy, York University, 4700 Keele St., Toronto, ON M3J 1P3, Canada

0000-0003-0049-5210]Patrick Hartigan
Department of Physics and Astronomy, Rice University, 6100 S. Main St., TX 77005-1892, USA

0000-0003-1728-0304]Keith Horne
University of St Andrews, SUPA School of Physics & Astronomy, North Haugh, St Andrews KY16 9SS, Scotland, UK

0000-0002-6610-2048]Anton M. Koekemoer
Space Telescope Science Institute, 3700 San Martin Dr., Baltimore, MD 21218, USA

Mirko Krumpe
Leibniz Institute for Astrophysics Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany

0000-0002-0311-2812]Jennifer I. Li
Department of Astronomy, University of Michigan, Ann Arbor, MI, 48109, USA

0000-0003-1731-0497]Chris Lidman
Research School of Astronomy and Astrophysics, Australian National University, Canberra, ACT 2611, Australia
Centre for Gravitational Astrophysics, College of Science, The Australian National University, Canberra ACT 2601, Australia

0000-0002-0036-1696]Umang Malik
Research School of Astronomy and Astrophysics, Australian National University, Canberra, ACT 2611, Australia

0000-0003-2385-6904]Amelia Mangian
Information Trust Institute, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

0000-0002-0761-0130]Andrea Merloni
Max-Planck Institute for Extraterrestrial Physics, Giessenbachstrasse 1,  85748, Garching, Germany

0000-0001-5231-2645]Claudio Ricci
Núcleo de Astronomía de la Facultad de Ingeniería, Universidad Diego Portales, Av. Ejército Libertador 441, Santiago, Chile
Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing 100871, China

0000-0001-7116-9303]Mara Salvato
Max-Planck Institute for Extraterrestrial Physics, Giessenbachstrasse 1,  85748, Germany

0000-0003-4877-7866]Rob Sharp
Research School of Astronomy and Astrophysics, Australian National University, Canberra, ACT 2611, Australia

0000-0002-8501-3518]Zachary Stone
Department of Astronomy, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Center for AstroPhysical Surveys, National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

0000-0003-4580-3790]David E. Trilling
Department of Astronomy and Planetary Science, Northern Arizona University, Flagstaff, AZ 86011, USA

0000-0002-4283-5159]Brad E. Tucker
Research School of Astronomy and Astrophysics, Australian National University, Canberra, ACT 2611, Australia
National Centre for the Public Awareness of Science, Australian National University, Canberra, ACT 2601, Australia
The Australian Research Council Centre of Excellence for All-Sky Astrophysics in 3 Dimension (ASTRO 3D), Australia

0000-0003-2812-8607]Di Wen
Department of Astronomy, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

0000-0001-8171-5507]Zachary Wideman
Department of Physics and Astronomy, Texas A&M University, 4242 TAMU, College Station, TX 77843, USA

0000-0002-1935-8104]Yongquan Xue
CAS Key Laboratory for Research in Galaxies and Cosmology, Department of Astronomy, University of Science and Technology of China, Hefei 230026, China

0000-0003-0644-9282]Zhefu Yu
Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, 452 Lomita Mall, Stanford, CA 94305, USA

0000-0002-2250-730X]Catherine Zucker
Center for Astrophysics $\vert$ Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA

High-quality Extragalactic Legacy-field Monitoring (HELM) is a long-term observing program that photometrically monitors several well-studied extragalactic legacy fields with the Dark Energy Camera (DECam) imager on the CTIO 4m Blanco telescope. Since Feb 2019, HELM has been monitoring regions within COSMOS, XMM-LSS, CDF-S, S-CVZ, ELAIS-S1, and SDSS Stripe 82 with few-day cadences in the $(u)gri(z)$ bands, over a collective sky area of  $\sim 38$ deg${\rm ^2}$. The main science goal of HELM is to provide high-quality optical light curves for a large sample of active galactic nuclei (AGNs), and to build decades-long time baselines when combining past and future optical light curves in these legacy fields. These optical images and light curves will facilitate the measurements of AGN reverberation mapping lags, as well as studies of AGN variability and its dependences on accretion properties. In addition, the time-resolved and coadded DECam photometry will enable a broad range of science applications from galaxy evolution to time-domain science. We describe the design and implementation of the program and present the first data release that includes source catalogs and the first $\sim 3.5$ years of light curves during 2019A–2022A.

§ INTRODUCTION

Large-scale optical time-domain imaging surveys such as Sloan Digital Sky Survey  <cit.> supernova survey <cit.>, Pan-STARRS1 Medium Deep Survey <cit.>, Zwicky Transient Facility <cit.>, and Wide Field Survey Telescope <cit.>

are rapidly shaping our understanding of the dynamic and variable universe. In addition to transients and explosive events from stellar systems, these time-domain surveys provide extremely valuable information to probe the physical mechanisms that drive the persistent variability from variable stars and active galactic nuclei (AGNs). Variability studies of AGNs also enable the measurements of their black hole masses via the reverberation mapping (RM) technique that measures the time lag between the driving continuum variability and the delayed response in the broad-line emission <cit.>. In particular, high-quality photometric light curves with long time baselines and adequate cadences are necessary to secure a RM lag measurement and periodicity searches for binary supermassive black hole candidates when combined with dedicated spectroscopic monitoring data <cit.>.

The SDSS-V Black Hole Mapper (BHM) program <cit.> is conducting a multi-year (2020–2027), multi-object spectroscopic RM campaign (BHM-RM) in several legacy extragalactic fields. In order to provide the required photometric light curves that sample the AGN continuum variability, we are conducting a long-term photometric monitoring campaign with the Dark Energy Camera <cit.> on the 4m CTIO Blanco telescope. The fields chosen by the SDSS-V BHM-RM program coincide with several well-studied extragalactic fields, each with ample multi-wavelength data and/or earlier and future photometric light curves therein <cit.>. For example, most of these fields coincide with deep monitoring fields in the Pan-STARRS1 survey <cit.> and the Dark Energy Survey supernovae program <cit.>. The combined optical light curves for most of these monitoring fields will span more than 25 years once combined with 10-yr light curves from the Legacy Survey of Space and Time (LSST) with the Vera C. Rubin Observatory. Therefore these DECam monitoring data will provide legacy value for broad science in these fields.

In this paper we provide an overview of the DECam monitoring program and its current status, and present the first data release[<https://ariel.astro.illinois.edu/helm/>] from the first $\sim 3.5$ years of observations (2019-2022). In Section <ref> we provide a technical overview of the High-quality Extragalactic Legacy-field Monitoring (HELM) program. In Section <ref> we describe the data products, with examples to showcase the science applications. We summarize and provide an outlook in Section <ref>. We adopt AB magnitude system throughout the paper.

HELM observation summary

Field     Pointing     Pointing center     ProgID     Filter     Nexp     $5\sigma$ depth (mag)

(1)     (2)     (3)     (4)     (5)     (6)     (7)

CDF-S     C1     03h37m06s $-$27d06m43s     C     $griz$     54-36-36-18     22.9-23.3-23.1-22.6

C2     03h37m06s $-$29d05m18s     A-C     $griz$     54-36-36-18

C3     03h30m36s $-$28d06m01s     A-C     $griz$     248-190-187-34

COSMOS     CO1     10h00m00s $+$03d05m56s     A-C-E-F-G     $gri$     362-295-295     22.8-23.1-22.9

CO2     09h56m53s $+$01d44m58s     A-E-F-G     $gri$     278-232-235

CO3     10h03m07s $+$01d44m59s     A-E-F-G     $gri$     267-226-232

ELAIS-S1     E1     00h31m30s $-$43d00m35s     A-C-E-F-G    $griz$     387-340-349-28     22.8-23.2-23.1-22.5

E2     00h38m00s $-$43d59m53s     A-C-E-F-G     $griz$     365-332-322-302

S-CVZ     S-CVZ     06h00m00s $-$66d33m39s    A-C     $ugriz$     33-223-196-196-24     21.1-22.8-22.9-22.7-22.0

Stripe 82     S1     02h51m17s $-$00d00m03s     A-B-C-H     $griz$     87-50-47-16     22.7-23.5-23.4-22.8

S2     02h44m47s $-$00d59m20s     A-B-C-H     $griz$     82-47-47-17

XMM-LSS     X1     02h17m54s $-$04d55m48s     A-C     $griz$     127-107-102-20     22.6-22.9-22.7-22.4

X2     02h22m40s $-$06d24m46s     A-C     $griz$     127-107-102-20

X3     02h25m48s $-$04d36m04s     A-C     $griz$     127-107-102-20

Col. (1) Field name. Col. (2) Pointing name. Col. (3) Median coordinate of pointing center. Typical pointing accuracy of each exposure is $\sim$10. Col. (4) Program ID. A: 2019A-0065; B: 2019B-0219; C: 2019B-0910; D: 2021A-0037; E: 2021A-0113; F: 2021B-0149; G: 2022A-724693; H: 2022B-175073. Col. (5) Filter used. Col. (6) Number of successfully reduced exposures in each band, separated by dashes. Most of the nightly epochs in the HELM program consist of two back-to-back exposures per pointing per filter. Col. (7) Median $5\sigma$ depth of individual exposures for all pointings of a given field estimated from $m_{\rm PSF}$.

§ OBSERVATIONS AND DATA PROCESSING

HELM started observations in the 2019A semester (Feb-Jul, 2019) and is currently ongoing. During the early phase of the program (2019A-2020B), a cadence of $\sim 6$ days was adopted. Since 2021A, the cadence was increased to $\sim 3$ days and a new field (South-Continuous Viewing Zone; S-CVZ) was added to the field list. For each epoch, a set of consecutive exposures in the $gri$ bands are taken, with 2 back-to-back exposures for each band. The nominal per-epoch exposure times are 160 s, 140 s, and 180 s in the three bands, respectively. A total of 14 DECam pointings (each with a $2.7\,{\rm deg^2}$ field-of-view) received regular monitoring, most of which are for the BHM-RM program. However, some pointings are included in different programs (PIs: X. Liu, P. Martini) that have slightly different science goals, including periodicity searches of binary supermassive black hole candidates and RM lag measurement. We consolidated these different DECam monitoring programs with AGN science focuses and processed these data with the same pipeline. Table <ref> summarizes the details of the monitoring fields. Three fields (CDF-S, XMM-LSS, and COSMOS) contain three pointings, while two fields (ELAIS-S1 and SDSS Stripe 82) contain two pointings. The layouts of the HELM fields are shown in Figure <ref>. These particular pointings are aligned with past monitoring within the DES supernovae program <cit.>.

The layout of HELM pointings in (a) CDF-S, (b) COSMOS, (c) ELAIS-S1, (d) S-CVZ, (e) SDSS Stripe 82, and (f) XMM-LSS fields. Pointings 1, 2, and 3 are indicated in black, blue, and red colors, respectively.

The first 2 semesters of HELM (2019A-2019B) were carried out on shared nights with other non-monitoring DECam programs. Since 2020A, HELM has been part of a collection of regular DECam monitoring programs that pool observing time and observers. In a few subsequent semesters, to improve the overall observing efficiency, some pointings of HELM were observed under the public DECam monitoring program that also targeted the COSMOS and ELAIS-S1 fields <cit.>, but to greater depths than those of the HELM program. These public DECam data are included in our data reduction and data products. The monitoring program suffered significant time loss in 2020A due to the COVID-19 pandemic, and substantial loss in the second half of 2020B due to DECam instrumental problems.

Cadence of the HELM in different fields, with vertical red lines indicating individual exposures. Moon illumination (illuminated fraction of the moon) is indicated by the blue gradient (white$=$no moonlight; dark blue$=$maximum moonlight) in the background.

At the beginning of the program during 2019A, we observed some fields with additional filters to provide more wavelength coverage. The S-CVZ field was observed in the $ugriz$ filters. For these additional imaging, $griz$ has 61 available CCD chips (except CCD #61), while $u$ has 60 chips with an additional CCD #2 not available. Table <ref> summarizes the HELM observations including fields, pointings, observing program IDs, filters, and numbers of exposures up to 13 September 2022. Figure <ref> illustrates the cadence of each field.

§.§ Data Reduction

The raw HELM observations were processed using the Dark Energy Survey (DES) “Final Cut” pipeline <cit.> with the same configuration of  <cit.> as that used in the production campaigns for the second data release (DR2) of DES <cit.> and DECam Local Volume Exploration-DR2 <cit.>. During the years that the DES Collaboration was actively taking data, the calibration products (e.g., bias, flat, sky-subtraction templates, secondary flat-field or “star-flats”, and the relative astrometric offsets among the CCDs) were monitored and updated as needed as part of that program.  Processing of observations taken after the DES observations ended in January 2019 have, thus far, relied on the DES legacy calibration products from that period.  In Section <ref> we assess the overall systematic astrometric and photometric performance to understand the limitations this might have on the temporal monitoring by HELM.

§.§.§ Magnitude Zeropoint

A magnitude zeropoint (zp) pipeline is run separately for all of the CCD images. We use the  (S. Allam & R. Gruendl, private communication) code to match objects in the Final Cut catalogs to the APASS 9 catalog <cit.>. A $1\arcsec$-radius match was performed for each CCD in one exposure. If the number of matched objects is low or a match was not found, the average zp value among all CCDs is adopted. The expCalib will not return a match (or will return a low number of matches) in two cases:  - APASS catalog has no coverage in the requested region of the sky, or the magnitude range of stars in the APASS catalog does not overlap with the brightness of objects detected in the CCD  (APASS sources are brighter and appear saturated in the DECam data). Among 7816 successfully reduced exposures (corresponding to 476743 CCD individual images), magnitude zero points are available from the expCalib for all but 124 exposures (2, 49, 28, 44, and 1 in the $ugriz$ bands, respectively) and 7562 CCD images ($1.6\%$).

Using four fields associated with 10 pointings of our HELM DECam monitoring program (CDF-S, ELAIS-S1, SDSS Stripe 82, and XMM-LSS) that are covered by the footprint of 6 years of DES science operations of $\sim5000$ deg$^2$ from DES-DR2 <cit.>, we derive independent magnitude zeropoints for all exposures by cross-matching sources detected in our observations with those from the DES-DR2 source catalog. The DES-DR2 catalog is constructed using coadded images, which has typically $\sim10$ times longer exposure time than our single-exposure images. Based on the extent of the source, we derive two versions of magnitude zeropoints, one based on point sources using $m_{\rm PSF}$ and another based on all the sources using $m_{\rm AUTO}$ to increase the number of available objects. We first select all the reliably extracted (,  ), high signal-to-noise ratio (SNR; $m_{\rm PSF}$ error $<0.1$ mag, which corresponds to an SNR of 10) sources from our observations. We use a relatively loose criterion of  to select point-like sources, which ensures we do not miss a significant number of point sources and avoids including many extended sources. For the DES-DR2 catalog, we apply the same selection criteria of object quality (,   in $i$ band) and select point-like sources using  following <cit.>.

The magnitude zp of each CCD chip is derived using the 3-$\sigma$ clipped median difference between magnitudes of objects from our single-exposure catalog and those from DES-DR2 catalog, with associated error equal to the standard deviation divided by the root of the number of the remaining objects. Figure <ref> compares the number of objects used to derive the magnitude zps. Magnitude zps derived from all sources make use of on average 2.7 times more objects compared to those derived from point sources only (295 versus 108). Limiting to CCD images with at least 10 applicable objects for zp calibration, we find a small difference of 0.009 mag with 16th and 84th percentiles of the distribution of $-$0.008 and 0.019 mag between the two sets of zp, respectively (Figure <ref>). We find that the difference between two zps is correlated with seeing, with larger $m_{\rm PSF}$-based zp in exposures with better seeing. This may be related to slight difference in aperture loss at different seeing conditions. We also compare the zp from the DECam pipeline with that from DES-DR2 using all sources (Table <ref>). The median differences are $<0.01$ mag (slightly larger with 0.014 mag in the E2 field) with a scatter of $\la 0.04$ mag. The final zp of a CCD image is determined with the priority decreasing from DES-DR2 AUTO magnitude to DES-DR2 PSF magnitude to DECam pipeline. We only adopt DES-DR2-based zp if the number of objects used to calibrate zp is greater or equal to 10. Finally, we successfully obtain zp for 99.9% of the CCD images, with DES-DR2 AUTO contributing to the vast majority of them (99.4%).

Comparison of HELM zp, $m_{\rm PSF}$, and astrometry to those from DES-DR2

Pointing     $\Delta$zp     $\Delta m_{\rm PSF}$ [$g$]     $\Delta m_{\rm PSF}$ [$r$]     $\Delta m_{\rm PSF}$ [$i$]     $\Delta m_{\rm PSF}$ [$z$]     $\Delta d$ [$g$]     $\Delta d$ [$r$]     $\Delta d$ [$i$]     $\Delta d$ [$z$]

Field     mag      (mag)     (mag)     (mag)     (mag)     ()     ()     ()     ()

C1     -0.007^+0.017_-0.032     -0.019_-0.042^+0.038     -0.026_-0.042^+0.036    -0.011_-0.029^+0.026     -0.015_-0.029^+0.024     0.059_-0.032^+0.054     0.052_-0.028^+0.050     0.050_-0.027^+0.047     0.050_-0.027^+0.045

C2     -0.006^+0.015_-0.024    -0.015_-0.036^+0.035     -0.020_-0.039^+0.034     -0.013_-0.028^+0.031     -0.015_-0.028^+0.028     0.059_-0.031^+0.054     0.052_-0.028^+0.050     0.051_-0.027^+0.047     0.051_-0.027^+0.046

C3     -0.005^+0.018_-0.031     -0.016_-0.041^+0.036     -0.018_-0.041^+0.032     -0.012_-0.030^+0.028     -0.018_-0.030^+0.025     0.066_-0.036^+0.063     0.060_-0.033^+0.060     0.058_-0.032^+0.057     0.052_-0.028^+0.048

E1     0.004^+0.029_-0.043     -0.029_-0.042^+0.037     0.020_-0.042^+0.039     0.007_-0.033^+0.032     -0.004_-0.028^+0.028     0.075_-0.040^+0.072     0.068_-0.036^+0.066     0.065_-0.035^+0.061     0.053_-0.028^+0.048

E2     -0.014^+0.024_-0.035     -0.037_-0.039^+0.036     -0.005_-0.034^+0.040     -0.014_-0.034^+0.032     -0.011_-0.025^+0.026     0.073_-0.039^+0.069     0.067_-0.036^+0.065     0.064_-0.034^+0.061     0.054_-0.029^+0.048

S1     -0.008^+0.044_-0.029     -0.023_-0.041^+0.045     -0.009_-0.032^+0.047     0.028_-0.043^+0.051     0.009_-0.026^+0.037     0.066_-0.036^+0.063     0.058_-0.032^+0.061     0.057_-0.031^+0.058     0.050_-0.027^+0.047

S2     0.000^+0.026_-0.024     0.009_-0.053^+0.052     -0.010_-0.033^+0.038     -0.009_-0.044^+0.032     -0.013_-0.037^+0.030     0.066_-0.036^+0.064     0.058_-0.032^+0.062     0.058_-0.032^+0.059     0.052_-0.028^+0.049

X1     -0.002^+0.020_-0.035     -0.008_-0.048^+0.041     -0.015_-0.045^+0.039     -0.008_-0.035^+0.031     -0.012_-0.034^+0.030     0.068_-0.036^+0.066     0.062_-0.034^+0.064     0.061_-0.033^+0.059     0.055_-0.030^+0.051

X2     0.004^+0.019_-0.033     -0.005_-0.051^+0.044     -0.008_-0.044^+0.041     -0.009_-0.037^+0.032     -0.015_-0.035^+0.029     0.070_-0.038^+0.068     0.064_-0.035^+0.064     0.062_-0.034^+0.061     0.056_-0.030^+0.051

X3     0.003^+0.019_-0.030     -0.009_-0.047^+0.042     -0.006_-0.041^+0.038     -0.003_-0.033^+0.031     -0.011_-0.029^+0.028     0.070_-0.038^+0.066     0.062_-0.034^+0.063     0.060_-0.033^+0.059     0.053_-0.028^+0.049

$\Delta \equiv $ our DECam data $-$ DES-DR2. Statistics are 50th percentile with upper (84th $-$ 50th) and lower (16th $-$ 50th) 1$\sigma$ of the distribution as the superscript and subscript, respectively.$\Delta d$ represents the angular distance difference between coordinates.

Histograms of objects used to calibrate the magnitude zeropoint for each CCD image of every exposure. Histogram for point sources using $m_{\rm PSF}$ is in red (PSF), while histogram for all sources using $m_{\rm AUTO}$ is in black.

Comparison of magnitude zeropoints (zp) calibrated against the DES-DR2 catalog using point sources (PSF) and all sources (AUTO). Only CCD images with at least 10 sources used for calibration are included. Red vertical dashed lines indicate 16th, 50th, and 84th percentiles of the distribution, with statistics shown in the lower-left corner.

Differences in $m_{\rm PSF}$ (top) and coordinates (middle) between our measurements of all observations and those from the DES-DR2 catalog ($\Delta\equiv$ HELM $-$ DES-DR2) as a function of $m_{\rm PSF}$ error for all high SNR ($m_{\rm PSF}$ error $<0.1$ mag) point sources in pointing C1. Normalized source number density ($N/N_{\rm max}$) of $m_{\rm PSF}$ error is shown in the bottom panel. $m_{\rm PSF}$ error is the propagated error by combining our measurement error and DES-DR2 measurement error in quadrature sum. Median values in the $griz$ filters are shown as blue, green, orange, and red solid curves, respectively. Dashed curves in top and middle panels indicate values at 84th and 16th percentiles. Statistics of all 10 pointings are shown in Table <ref>.

§.§ Photometry and Astrometry Cross-Calibration

Following the same recipe as in Section <ref>, we check the accuracy of the HELM pipeline photometry and astrometry independently for the fields that are not covered by the official DES data products (COSMOS and S-CVZ). The DES-DR2 catalog is based on coadded images, with a photometric accuracy of $\sim0.01$ mag and astrometric precision of $\sim 0\farcs03$. We select all the point sources detected in our observations and crossmatch with the DES-DR2 catalog for all the reliably extracted (,  ), high SNR ($m_{\rm PSF}$ error $<0.1$ mag) sources. We select point sources from the DES-DR2 catalog using . For our observations, we use a relatively loose criterion of , which ensures we do not miss a significant number of point sources and avoids including many extended sources.

Figure <ref> presents an example of comparison of $m_{\rm PSF}$ and coordinate difference ($\Delta d$) as a function of $m_{\rm PSF}$ error in pointing C1. The statistics for all 10 pointings are presented in Table <ref>. Although various degrees of differences are found in different fields, the source magnitude (median $\Delta m_{\rm PSF} \approx 0.02$ mag) and astrometry ($\Delta d\approx0\farcs06$) of our HELM pipeline are in good agreement with DES-DR2 for high SNR sources. The overall good performances in zp, photometry, and astrometry lend support to the reliability of the HELM pipeline for use in fields not covered by the footprint of DES-DR2 (COSMOS and S-CVZ).

Histograms of inverse variance-weighted mean $m_{\rm AUTO}$ in all detected exposures ($m_{\rm AUTO}$_w; see Equation <ref>) in $g$ (blue), $r$ (green), and $i$ (orange) bands for objects in all 6 fields with detection in at least 5 individual exposures.

Example light curves of RR Lyrae stars (left; in folded phase) and AGNs (right) in the COSMOS field. Blue, green, and red dots represent the $g$, $r$, and $i$ bands, respectively, with error bar indicating the certainty. The periods of RR Lyrae stars are shown at the top of the left panels.

§ DATA PRODUCTS

For this first HELM data release, we include the source catalog covering all compiled fields, as well as the light curves for these sources from the first 3.5 years (2019A-2022A). Our source catalog is built by cross-matching detected sources in our individual images with the DES-DR2 <cit.> source catalog for the CDF-S, XMM-LSS, ELAIS-S1, and SDSS Stripe 82 fields, and with the source catalog from the third data release <cit.> of the Hyper Suprime-Cam (HSC) Subaru Strategic Program <cit.> for the COSMOS field. In total, there are 5,404,721, 4,714,784, and 4,059,093 sources with detections in at least 3, 5, and 10 exposures in any band, respectively. Figure <ref> shows histograms of inverse variance-weighted mean $m_{\rm AUTO}$ ($m_{\rm AUTO}$_w; see Section <ref> for definition) in the $gri$ bands for objects in all 6 fields with detections in at least 5 individual exposures. The peak $m_{\rm AUTO}$_w is $\sim24.0$, 23.6, and 23.4 mag for the $gri$ bands, respectively, which are deeper compared to those of typical single exposures (Table <ref>) and representing the best seeing condition and longest exposure time. In future data releases, we will provide individual images, deeper photometric catalogs built from the coadded images, and extended light curves (see Section <ref>). Below we describe in detail our source catalog and light curve compilation.

§.§ Source catalogs and light curves

The majority of our DECam pointings in the COSMOS field are covered by Deep- and UltraDeep-depth images of HSC SSP. For regions outside the Deep and UltraDeep coverage, we use instead the source catalog from Wide-depth images of HSC SSP. We extract the source catalog with $i$ band  magnitude brighter than 25.5 mag from the Hyper Suprime-Cam Legacy Archive[<https://hscla.mtk.nao.ac.jp/doc/>] ( for the Deep and UltraDeep catalogs and  for the Wide catalog) using example script . For the Wide catalog, we further apply the criteria presented in  script to the $i$ band to keep only “clean” sources. For all the other fields except S-CVZ (i.e., CDF-S, ELAIS-S1, Stripe 82, and XMM-LSS), we use DES-DR2 as the base source catalog for catalog construction, as it is deeper than individual exposures of our program. A radius of 05 is adopted for crossmatching.

For the S-CVZ field, we construct the source catalog from scratch as no deep optical catalog in this field is currently available. We first randomly pick an individual exposure catalog as the reference catalog and crossmatch other catalogs in the $griz$ bands to it one by one using a matching radius of 0.5. We then iteratively build the preliminary master catalog by adding new sources whose separations from existing sources are larger than 0.5. The coordinates of the preliminary master catalog obtained after the first run are determined as the median value of those classified as the same source in individual catalogs. We then perform a second run by crossmatching individual exposure catalogs to the preliminary master catalog and obtain a final catalog in the $griz$ bands. Finally, we crossmatch the $u$ band individual exposure catalogs with the $griz$ catalog, as the $u$ band depth is much shallower compared to that in the $griz$ bands.

Table <ref> presents the table format of our median source catalog, including source index, coordinate, magnitudes (PSF and AUTO), extraction flags, star-galaxy classifier (spread_model and class_star), and number of exposures. For magnitude, we also provide inverse variance weighted mean (mag_w), defined as

\begin{equation}\label{eq1}
{\rm mag\_w} \equiv \frac{\Sigma(m_i/e_i^2)}{\Sigma(1/e_i^2)},
\end{equation}

where $m_i$ and $e_i$ represent an individual measurement and its error in the $i$th exposure, respectively. Its corresponding uncertainty is

\begin{equation}\label{eq2}
{\rm mag\_w\_err} = \frac{\sqrt{\Sigma(e_i/e_i^2)^2}}{\Sigma(1/e_i^2)}=\frac{1}{\sqrt{\Sigma(1/e_i^2)}},
\end{equation}

after error propagation. For the S-CVZ field, we provide separate source indices and coordinates for $griz$ and $u$ bands, as the $u$ band is much shallower compared to the other bands. Separation between the median coordinate in the $griz$ bands and that in the $u$ band is also provided. Table <ref> describes the format of the light curve files of individual objects. The light curves of individual sources can be extracted from our online database[<https://ariel.astro.illinois.edu/helm/>] using source indices provided in Table <ref>.

Comparison of DECam $r$ band (open blue circle) and ZTF $r$ band (open red square) light curves of a quasar in the COSMOS field. The inset shows a zoom-in view around MJD$=$59300 day. The error bars indicate 1$\sigma$ uncertainty.

§.§ Example light curves

Figure <ref> shows example light curves of three RR Lyrae stars and three quasars in the COSMOS field. For RR Lyrae stars, we have modeled their light curves using  and presented phase-folded light curves. The high-quality photometry and dense time sampling ensure excellent recovery of period information. The overall clean phase-folded light curves of RR Lyrae stars also demonstrate the good performance of our zp calibration (Section <ref>). The long time baseline, decent $\sim3$-day cadence, and multi-band coverage make it possible to qualitatively investigate both long and short timescale, as well as multi-band AGN variability. In particular, as shown in Figure <ref> and its inset, the epoch magnitude uncertainty is $\sim5$ times smaller in HELM than in the ZTF DR19 – median uncertainty of 0.014 mag from HELM $r$ band versus 0.068 mag from ZTF $r$ band. Therefore the resulting optical light curves from HELM are of much better quality than ZTF light curves, albeit with sparser cadences.

§.§ Caveats

In this paper, our source catalogs for the CDF-S, ELAIS-S1, Stripe 82, and XMM-LSS fields are constructed based on the DES-DR2 source catalog, which has a median coadded catalog depth for a 195 diameter aperture at SNR=10 of $g$=24.7, $r$=24.4, $i$=23.8, and $z$=23.1 mag. However, we may miss transients that were not detected in previous surveys. Given the number of exposures (up to 387 s) and total exposure time (up to 4480 s) in these fields, together with ongoing exposures that are not included in this data release, we would eventually achieve much deeper source detection by coadding all individual exposures. For the S-CVZ field, our source catalog is constructed based on catalogs of individual exposures. Therefore, the depth is limited by the deepest exposure. For the COSMOS field, we notice a source splitting issue for saturated objects in the HSC catalog.  As we are using deeper catalogs for source cross-matching (in particular for the COSMOS field), our source catalog may contain some spurious sources with low $N_{\rm exp}$ and close to or even below the typical depth of DECam individual exposures (relatively steep increase of source number toward the faint end in the $g$ and $i$ bands as seen in Figure <ref>). Moreover, as the saturation magnitudes are quite faint for the HSC survey (18.0, 18.2, and 18.6 mag for the $gri$ bands; ), this issue may affect the sources that are above their saturation limits. Another major caveat is that there is no forced photometry or upper limits, which may give a false sense of the overall variability especially for sources close to the detection limit. The caveats mentioned above would be largely mitigated by building source catalogs directly from coadded DECam images and performing forced photometry on each source in our future data releases. On the other hand, any variable / transient sources that are not associated with a static object in the matched catalogs will be missed (e.g., off-axis transients and moving objects). These objects will be included in our future data releases after implementing dedicated pipeline.

§ SUMMARY

In this work we present an overview of the HELM long-term monitoring program in extragalactic legacy fields, with the DECam imager on the 4m CTIO Blanco telescope. With a multi-year baseline and relatively high cadence, HELM aims to provide high-quality optical light curves for a large sample of AGNs in these fields, as part of the effort to obtain RM measurements for distant AGNs and quasars in the SDSS-V BHM-RM program. This long-term photometric monitoring program will also bridge past light curves and future LSST/WFST observations to compile decades-long light curves to facilitate investigations of long-term variability of astronomical objects (particularly AGNs). Given the ample multi-wavelength data in these legacy fields, the HELM light curve data will also provide legacy value to time-domain studies. At the time of writing, HELM has been extended to continue DECam monitoring in these fields through at least 2025B, given the delayed start of the LSST.

In this work we also present the first data release from HELM, which includes source catalogs and light curves from the first 3.5 years of observations. In future data releases, we will provide fully calibrated individual and coadded images, as well as extended light curves.

FITS table format for the median source catalog

Column Name     Format     Units     Description

Index     LONG          Source index

ra     DOUBLE     degree     Right Ascension (J2000)

dec     DOUBLE     degree     Declination (J2000)

mag_psf     DOUBLE     mag      PSF magnitude

mag_auto     DOUBLE     mag      AUTO magnitude

flags     LONG           extraction flags

spread_model     DOUBLE          model-based star-galaxy classifier

class_star     DOUBLE          neural network-based star-galaxy classifier

Nexp_psf     LONG         Number of exposures with successful mag_psf measurement

Nexp_auto     LONG         Number of exposures with successful mag_auto measurement

Separation     DOUBLE     arcsec     Distance between median coordinate in $griz$ bands and $u$ band (only in S-CVZ field)

All columns except Separation can have prefix, while all columns except Index, Nexp_psf, Nexp_auto, and Separation have suffix.

Prefix: “*_” indicates the associated band for the parameter, with * = one or a combination of $ugriz$ bands (such as g_ in all fields or griz_ only for S-CVZ). If not specified (for Index, ra, and dec in all fields except S-CVZ), it represents the value for all bands combined.

Suffix: “_m” and “_err” indicate unweighted median (50%) and its corresponding error of Nexp measurements with _err $= 0.5 \times ({\rm 84th\ percentile - 16th\ percentile})/\sqrt{\rm Nexp}$; “_w” indicates inverse variance weighted mean ($\Sigma(m_i/e_i^2)/\Sigma(1/e_i^2)$, where $m_i$ and $e_i$ represent individual measurement and its error at $i$th exposure, respectively); ”_w_err” indicates the error associated with _w after error propagation ($1/\sqrt{\Sigma(1/e_i^2)}$). Specifically, “_err” = “_w_err” = error of individual measurement when Nexp=1.

Value = “-99” indicates no data or non-detection.

FITS table format for the light curve

Column Name     Format     Units     Description

ra     DOUBLE     degree     Right Ascension (J2000)

dec     DOUBLE     degree     Declination (J2000)

expnum     LONG         DECam exposure number

mjd     DOUBLE     day     Modified Julian date of exposure

band     Char         Filter (one of $ugriz$)

mag_psf     DOUBLE     mag      PSF magnitude

mag_e_psf     DOUBLE     mag     Uncertainty of  PSF magnitude

mag_auto     DOUBLE     mag      AUTO magnitude

mag_e_auto     DOUBLE     mag     Uncertainty of  AUTO magnitude

flags     LONG           extraction flags

spread_model     DOUBLE          model-based star-galaxy separation

spread_model_e     DOUBLE         Uncertainty of  model-based star-galaxy separation

class_star     DOUBLE          neural network-based star-galaxy separation

We thank Tamara M. Davis for initial discussions. This work is supported by NSF grants AST-2206499 and AST-2308077. F.E.B. acknowledges support from ANID - Millennium Science Initiative Program - ICN12_009, CATA-BASAL - FB210003, and FONDECYT Regular - 1200495. Y.Q.X. acknowledges support from the National Key R&D Program of China (2023YFA1608100), NSFC grants (12025303, 12393814), and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0550300).

CTIO Blanco telescope


# HemSeg-200: A Voxel-Annotated Dataset for Intracerebral Hemorrhages
Segmentation in Brain CT Scans

Changwei Song, Qing Zhao, Jianqiang Li, Xin Yue, Ruoyun Gao, Zhaoxuan Wang, An
Gao and Guanghui Fu* Changwei Song, Jianqiang Li, Qing Zhao, Xin Yue, Ruoyun
Gao, Zhaoxuan Wang are with School of Software Engineering, Beijing University
of Technology, Beijing, China.An Gao is with Department of Radiology, Tianjin
Medical University Cancer Institute and Hospital, Tianjin, China.Guanghui Fu
is with Sorbonne Université, Institut du Cerveau – Paris Brain Institute -
ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris,
France (e-mail: guanghui.fu@inria.fr).Corresponding author: Guanghui Fu
(guanghui.fu@inria.fr)This work was supported by The National Natural Science
Foundation of China under Grant No.72204169.This work has been submitted to
the IEEE for possible publication. Copyright may be transferred without
notice, after which this version may no longer be accessible.

###### Abstract

Acute intracerebral hemorrhage is a life-threatening condition that demands
immediate medical intervention. Intraparenchymal hemorrhage (IPH) and
intraventricular hemorrhage (IVH) are critical subtypes of this condition.
Clinically, when such hemorrhages are suspected, immediate CT scanning is
essential to assess the extent of the bleeding and to facilitate the
formulation of a targeted treatment plan. While current research in deep
learning has largely focused on qualitative analyses, such as identifying
subtypes of cerebral hemorrhages, there remains a significant gap in
quantitative analysis crucial for enhancing clinical treatments. Addressing
this gap, our paper introduces a dataset comprising 222 CT annotations,
sourced from the RSNA 2019 Brain CT Hemorrhage Challenge and meticulously
annotated at the voxel level for precise IPH and IVH segmentation. This
dataset was utilized to train and evaluate seven advanced medical image
segmentation algorithms, with the goal of refining the accuracy of
segmentation for these hemorrhages. Our findings demonstrate that this dataset
not only furthers the development of sophisticated segmentation algorithms but
also substantially aids scientific research and clinical practice by improving
the diagnosis and management of these severe hemorrhages. Our dataset and
codes are available at https://github.com/songchangwei/3DCT-SD-IVH-ICH.

###### Index Terms:

Medical image segmentation, Brain CT, Intracerebral hemorrhage.

##  I Introduction

Intracerebral hemorrhage (ICH) is one of the most lethal types of stroke, with
a one-month mortality rate exceeding 40% [1]. ICH encompasses various forms of
bleeding within the skull, which can occur either outside or within the brain
tissue [2]. The quantification of intraparenchymal hemorrhage (IPH) and
intraventricular hemorrhage (IVH) is crucial for clinical doctors, as these
measurements are often particularly relevant to surgical planning [3, 4]. The
high mortality rate associated with intracranial hemorrhage highlights the
urgent need for research into innovative diagnostic methods. Diagnostic tools
based on deep learning show great promise as critical tools to assist
clinicians in accurately diagnosing such conditions. [5, 6]. For example, Fu
et al. [7] utilized convolutional neural networks with attention mechanisms to
qualitatively classify intracranial hemorrhages. Their method was applied to
five types of hemorrhages across the RSNA (RSNA Intracranial Hemorrhage
Detection) [8, 9] and CQ500 [10] datasets. They also provided interpretive
analyses of their results by attention mechanism to highlight suspicious areas
in the imaging data. Building on this work, Wang et al. [11] developed a
method that replicates the reading behaviors of expert radiologists. Their
approach involves selectively magnifying original images from the full
sequence scans to focus on and diagnose suspicious lesions. They conducted
human-computer interaction experiments which demonstrated that with the
assistance of deep learning models, junior physicians improved their
diagnostic capabilities. Specifically, these physicians increased their
diagnostic accuracy by 16% points F1-score, reduced their average diagnostic
time by 10 seconds, and reported higher levels of confidence in their clinical
assessments. To date, most research in this area has focused on qualitative
categorical studies, there has been limited focus on the problem of hemorrhage
segmentation, with the majority of these studies utilizing private datasets
[6]. Deep learning-based medical image segmentation algorithms can
automatically find anatomical structures or lesion areas, providing a
foundation for quantitative disease research [12]. Pioneering studies in this
area have demonstrated a variety of innovative techniques. Yu et al. [13] used
dimensionality reduction in conjunction with the U-net model [14] to segment
and measure brain hemorrhages. Nijiati et al. [15] improved the accuracy of
segmentation and classification of ICH lesions by integrating the Transformer
[16] with the traditional U-Net [14] using symmetry-based prior knowledge. In
addition, Peng et al. [17] advanced CT image segmentation and ICH volume
measurement by incorporating attention gate mechanisms along with a focus
structure for improved accuracy. Finally, Chang et al. [18] refined class-
specific feature extraction using channel attention in the U-net encoder and
employed both spatial and channel attention in the decoder to better delineate
shapes and classify types. Chang et al. [19] highlighted the importance of
tissue deformation, such as perihematomal edema (PHE), as an indicator of
hemorrhagic regions. They introduced a novel center-surround differential
U-net (CSD U-net) that detects areas of tissue deformation as potential
hemorrhagic sites while also predicting actual hemorrhage. Tong et al. [20]
improved the segmentation of intracerebral and intraventricular hemorrhage in
CT images by integrating a 3D U-net with a multi-scale boundary awareness
module, which improved the delineation of hematoma boundaries. Their model
also includes a consistency loss feature to reduce the misclassification of
pixels into multiple categories. However, current research suggests that
algorithmic performance improvements under identical data conditions are
modest [21], with the emphasis increasingly shifting to dataset construction.
Unfortunately, there are very few publicly available datasets for segmentation
tasks, and most are neither comprehensive nor perfect [22].

For tasks related to identifying subtypes of brain hemorrhage, there are
established datasets such as CQ500 [10] and the RSNA 2019 Brain CT Hemorrhage
Challenge dataset (referred to as the RSNA dataset) [8]. The CQ500 dataset
includes 491 patients represented by 1,181 head CT scans, while the RSNA
dataset includes a significantly larger cohort of 16,900 patients with 19,336
head CT scans, providing extensive resources for deep learning model
development. However, neither dataset provides pixel/voxel-level annotations
for hemorrhage region segmentation, which poses a challenge for detailed
analysis and model training. Currently, the PhysioNet [23] and INSTANCE22 [24]
datasets are public resources for brain hemorrhage segmentation tasks.
However, these datasets are limited in terms of sample size; the PhysioNet
dataset contains 82 CT scans, while the INSTANCE22 dataset contains 130 CT
scans. The limited availability of samples in public datasets for brain
hemorrhage segmentation is primarily due to the labor-intensive and time-
consuming process required for pixel-level annotation.

In this paper, we focus on the segmentation of intraparenchymal hemorrhage
(IPH) and intraventricular hemorrhage (IVH) lesions that are useful for
quantitative analysis by medical doctors. We annotated a dataset of 222 brain
CT scans called HemSeg-200 (IPH: 114 scans, IVH: 108 scans). We selected the
relevant data from the RSNA dataset and annotated them at the voxel level. To
address the labor-intensive and time-consuming nature of pixel-level
annotation in 3D CT datasets, this study presents a semi-automated approach
that progresses from coarse to fine-grained annotation. We have developed a
multistep annotation process in which initial coarse segmentations are
gradually refined using a combination of automated techniques and manual
corrections, and experienced physicians corrected again the annotation to
ensure the quality of the labels. We evaluated the performance of the dataset
by evaluating seven SOTA 3D medical image segmentation models. The
experimental results showed that the nnU-net model yielded the highest
performance, achieving a Dice score of 78.99%. Our annotations and codes are
openly accessible, providing valuable resources for the development of deep
learning models within the community.

##  II Datasets

###  II-A Semi-automated data annotation

Our dataset originates from the RSNA dataset [8, 9], which was initially
stored in DICOM format. We utilized the ITK tools [25] for the batch
conversion of the DICOM data into the NIfTI format [26]. We applied windowing
techniques to enhance image contrast, ensuring the visibility and clarity of
features relevant to our study. Furthermore, to augment the efficiency of our
data annotation process, we adopted a semi-automated, coarse-to-fine method.

The first round of annotations was generated by TotalSegmentator [27], a tool
developed on top of the nnU-net framework [28] and trained on a wide range of
public and proprietary datasets. In the second round, we randomly selected 20
scans to be independently labeled by four junior annotators. The inter-rater
variability ranged from 75.33% to 81.24% (mean 78.45%) based on Dice scores.
They continued their annotations under the supervision of an expert.
Subsequently, all annotations were thoroughly reviewed and verified by a
board-certified neuroradiologist (A.G.) with two years of experience in neuro-
oncology imaging to ensure the accuracy and reliability of the labels.
Throughout this process, we utilized 3D Slicer [29] as our annotation tool.

###  II-B Dataset information

The dataset consists of 222 CT volumes, with 114 volumes diagnosed with
intraparenchymal hemorrhage (IPH) and 108 volumes identified as
intraventricular hemorrhage (IVH). All volumes share a consistent cross-
sectional resolution of $512\times 512$, derived from the standardized DICOM
file format of the RSNA source. These CT scans contain different numbers of
slices, from 24 to 56 (mean 33 slices). In-plane resolution ranges from 0.39
mm to 0.63 mm and slice thickness ranges from 3.00 mm to 6.50 mm. Figure 1
provides some examples of the dataset and overlap with its annotation.

Figure 1: Examples of intraparenchymal hemorrhage (IPH): case (A) and (B) and
intraventricular hemorrhage (IVH): case (C) and (D). We shown the original CT
slices and their overlaid annotations.

##  III Methods

We evaluated seven commonly used 3D medical image segmentation models in the
field, which can facilitate the understanding of the performance of these
commonly used algorithms on this dataset.

  * •

3D U-net [30]: 3D U-net features a symmetric U-shaped architecture, comprising
a contracting (encoding) path and an expanding (decoding) path, designed to
focus on preserving and recovering the fine structure of input images. The
contracting path downsamples through standard convolution and pooling layers,
while the expanding path upsamples through transpose convolutions. Skip
connections directly concatenate feature maps from each layer of the
contracting path with the corresponding layer in the decoding path, enhancing
the preservation of details such as edges.

  * •

3D V-net [31]: V-net is a 3D fully convolutional neural network akin to U-net,
utilizing an encoder-decoder structure with skip connections to merge low and
high-level features. However, V-net differs by incorporating residual learning
to facilitate gradient flow and enhance training stability; uses a combination
of upsampling and convolution for feature map magnification, in contrast to
U-net’s typical use of transpose convolutions; and introduces a novel deep
supervision mechanism that allows loss calculation at different network depths
during training, aiding in more effective learning.

  * •

SegResNet [32]: SegResNet consists of an encoder and a decoder, with the
encoder employing $3\times 3\times 3$ convolutional kernels and GroupNorm
normalization for downsampling. The decoder features two branches: one
predicts the mask, and the other mirrors a variational autoencoder’s decoder.
Upsampling in the decoder is achieved through interpolation and $1\times
1\times 1$ convolutional kernels.

  * •

Attention U-net [33]: Attention U-net enhances the standard U-net model with a
novel attention gate module, integrated into the U-net’s skip connections to
heighten the model’s sensitivity to foreground pixels.

  * •

UNETR [34]: UNETR merges the Transformer architecture with the U-Net design,
featuring an encoder-decoder structure. In the encoder, it leverages self-
attention to capture long-range dependencies, crucial for medical image
segmentation where contextual information across the entire volume is vital
for accurate delineation. The model also employs convolutional kernels to
extract multi-scale features from various Transformer layers. In the decoder,
it utilizes skip connections to merge these multi-scale features from the
encoder with upsampled features in the decoder to enhance segmentation
performance.

  * •

Swin UNETR [35]: The Swin UNETR model extends the UNETR architecture by
substituting its Transformer module with a Swin Transformer [36], employing a
shifted window mechanism to compute self-attention across multiple resolutions
for feature representation.

  * •

nnU-net [28]: nnU-Net is an automated framework built upon 2D U-Net, 3D U-Net,
and cascaded U-Net models. It utilizes heuristic rules to tailor data
preprocessing, augmentation, network architecture, and post-processing to the
dataset, establishing a key benchmark in medical image segmentation.

We employed the Dice loss [31, 37] as the loss function, which is commonly
applied in medical image segmentation tasks.

##  IV Experiments and results

###  IV-A Data preprocessing

Initially, we aligned all images and masks with the RAS (Right, Anterior,
Superior) coordinate system to establish a uniform orientation. Following
this, we resampled both images and masks to unify the spatial resolution
across each dimension according to the dataset’s mean. For images, bilinear
interpolation was utilized to maintain quality, whereas masks underwent
nearest-neighbor interpolation to ensure the possibility of inverse
transformations. Furthermore, we normalized the intensity values of the
images. This entailed collecting intensity values for foreground classes
(excluding background and ignored categories) from all training instances to
compute the mean, standard deviation, and the 0.05 and 99.5 percentiles. The
intensity values were then clipped to these percentile values, with subsequent
subtraction of the mean and division by the standard deviation, applying this
normalization method consistently to each training case for the respective
input channel. Additionally, to augment data variability and boost the model’s
ability to generalize, we incorporated random flip transformations along the x
and y axes. During the train and inference phase, we utilized a sliding window
strategy, extracting patches of size $256\times 256\times 16$ for processing.
To ensure comprehensive coverage, four patches were extracted from each image,
with at least one patch containing foreground elements. Note that since we
employed a sliding window strategy, there was no need to perform uniform
operations such as resampling or resizing on CT images with inconsistent
resolutions. The dataset was stratified splited into training, validation, and
testing subsets in a 3:1:1 ratio, resulting in 133, 44, and 45 volumes for
each respective set.

###  IV-B Implementation details

Training and evaluation were conducted using a NVIDIA 24GB RTX 4090 GPU. Our
model was developed using PyTorch framework [38], and the MONAI library [39],
tailored for medical image processing. Regarding the model’s hyperparameters,
we opted for training over 200 epochs with a batch size of 2. We employed the
AdamW optimizer [40] with learning rate as 0.01 with a matching decay rate to
ensure steady model convergence. In selecting performance metrics, we adhered
to the guidelines set forth in Metrics Reloaded [41], specifically employing
the 3D Dice coefficient (Dice), intersection over union (IoU), and the 95% 3D
Hausdorff distance (HD95). For each metric, we report both the mean value and
the corresponding 95% confidence interval (CI), which was computed using
bootstrap methods over the independent test set. We openly share our detailed
configurations, source codes, and pre-trained model at:
https://github.com/songchangwei/3DCT-SD-IVH-ICH.

###  IV-C Results

TABLE I: The performance of IPH and IVH segmentation on brain CT scans from RSNA dataset. Results presented as mean with 95% bootstrap confidence interval computed on the independent test set. Models | Dice(%) | IoU(%) | HD95
---|---|---|---
U-net | 63.70 [55.82, 71.10] | 51.40 [44.26, 58.48] | 83.48 [51.66, 88.37]
V-net | 61.19 [52.81, 69.34] | 49.39 [41.79, 56.98] | 48.88 [33.30, 66.18]
SegResNet | 32.66 [25.96, 39.74] | 22.02 [16.99, 27.57] | 141.16 [124.20, 158.99]
Attention U-net | 66.77 [58.90, 73.86] | 54.98 [47.42, 62.10] | 49.62 [33.44, 67.26]
UNETR | 55.98 [48.94, 62.69] | 42.43 [36.10, 48.72] | 111.10 [90.80, 131.54]
Swin UNETR | 48.50 [40.79, 56.13] | 35.92 [29.19, 42.59] | 164.85 [143.83, 186.99]
nnU-net | 78.99 [73.21, 83.86] | 68.49 [62.05, 74.25] | 22.16 [9.19, 38.20]

The experimental results, summarized in Table I, show that nnU-Net
outperformed other models in segmentation performance on the dataset,
achieving a Dice coefficient of 78.99% with no overlap in the confidence
interval with other models. These superior results can be attributed to nnU-
Net’s comprehensive approach, which includes effective automated
preprocessing, data augmentation, optimized network architecture, and
postprocessing techniques. In contrast, under identical data preprocessing
conditions (as described in section IV-A), the Attention U-Net model performed
best for Dice and IoU with values of 66.77% and 54.98%, respectively, while
the V-Net model excelled in HD95 with a score of 48.88. However, it was noted
that Transformer-based models, specifically UNETR and Swin UNETR, did not
perform as well on this dataset. For a clearer comparison, Fig. 2 displays the
segmentation results from various models alongside ground truth.

Figure 2: Ground-truth label vs predictions on six representative patients
with hemorrhages.

The U-net architecture generally performs well, accurately identifying brain
hemorrhage regions in most examples. However, finer details such as edges may
not be well recognized, as seen in examples (A) and (C). In cases where
multiple hemorrhages are present, U-net may miss some of the hemorrhages, as
seen in examples (B), where the hemorrhage in the right ventricle is not
detected, and (E) where the hemorrhage in the third ventricle. For V-net, the
performance follows a similar trend to U-net, although in example (B) most of
the major hemorrhage is missing. This could be due to inconsistencies in the
contrast of this bleed region, resulting in the inability of the model to
accurately detect it. Attention U-net achieves the second best performance
next to nnU-net, but also misses the bleed in the right ventricle in example
(B). SegResNet performs poorly, missing lesions as shown in examples (B), (E)
and (F). In addition, it generates topology errors outside of the region as
shown in example (D), suggesting that the model struggles to accurately detect
the contrast of the hemorrhage and misidentifies high signal regions of the
skull. For the transformer-based architecture models, UNETR and Swin UNETR
exhibit similar errors, often producing out-of-region errors as shown in
examples (A), (C), and (F). This indicates their inability to capture and
learn the features of the disease in this dataset, even though they cover most
of the important regions. Due to the principles and characteristics of brain
hemorrhage, our data may exhibit a mixture of ICH and IVH, as shown in example
(C). Additionally, our dataset may include a mixture of new hemorrhage and old
hemorrhage, as depicted in examples (B) and (C), which could represent stale
brain hemorrhage. These characteristics pose challenges for the algorithm,
leading to several models missing some regions. However, nnU-net performs
consistently well in these cases, with excessive segmentation observed only in
some regions shown in example (F). This highlights the model’s powerful
ability to produce optimal results by learning from a combination of training
strategies.

##  V Discussion

Compared to the current brain hemorrhage segmentation dataset, our dataset has
the most extensive annotations. However, the performance achieved by the
models is still limited. While nnU-Net achieves relatively good performance,
reaching a Dice score of 78.99%, the vision transformer-based framework fails
to achieve satisfactory performance, suggesting the need for more data.
Therefore, the next step is to continue the labeling process to develop larger
datasets.

Although the best performance was achieved with nnU-net based on the auto-
configuration method, we have not compared it with other models that use
autoconfiguration, such as nnFormer [42] and CoTR [43]. Comparing it with a
broader range of models is also one of our next steps.

##  VI Conclusion

Our study employed a semi-automatic annotation method to annotate 222 brain
hemorrhage datasets obtained from the RSNA dataset. To our knowledge, this
represents the largest publicly available dataset for brain hemorrhage
segmentation studies. To evaluate the efficacy of deep learning algorithms on
this dataset, we tested seven widely-used deep learning models. The findings
indicate that nnU-net outperformed the others, achieving a Dice score of
78.99% with confidence intervals that do not overlap with those of the other
models. This research effectively supports the advancement of deep learning
models in this area, offering promising potential for future clinical
applications.

## References

  * [1] H. B. Brouwers, Y. Chang, G. J. Falcone, X. Cai, A. M. Ayres, T. W. Battey, A. Vashkevich, K. A. McNamara, V. Valant, K. Schwab _et al._ , “Predicting hematoma expansion after primary intracerebral hemorrhage,” _JAMA neurology_ , vol. 71, no. 2, pp. 158–164, 2014.
  * [2] J. M. Gebel and J. P. Broderick, “Intracerebral hemorrhage,” _Neurologic clinics_ , vol. 18, no. 2, pp. 419–438, 2000.
  * [3] B. A. Gross, B. T. Jankowitz, and R. M. Friedlander, “Cerebral intraparenchymal hemorrhage: a review,” _Jama_ , vol. 321, no. 13, pp. 1295–1303, 2019.
  * [4] D. F. Hanley, “Intraventricular hemorrhage: severity factor and treatment target in spontaneous intracerebral hemorrhage,” _Stroke_ , vol. 40, no. 4, pp. 1533–1538, 2009.
  * [5] M. Yeo, B. Tahayori, H. K. Kok, J. Maingard, N. Kutaiba, J. Russell, V. Thijs, A. Jhamb, R. V. Chandra, M. Brooks _et al._ , “Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging,” _Journal of neurointerventional surgery_ , vol. 13, no. 4, pp. 369–378, 2021.
  * [6] A. Neethi, S. K. Kannath, A. A. Kumar, J. Mathew, and J. Rajan, “A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection,” _Engineering Applications of Artificial Intelligence_ , vol. 133, p. 108192, 2024.
  * [7] G. Fu, J. Li, R. Wang, Y. Ma, and Y. Chen, “Attention-based full slice brain ct image diagnosis with explanations,” _Neurocomputing_ , vol. 452, pp. 263–274, 2021.
  * [8] A. E. Flanders, L. M. Prevedello, G. Shih, S. S. Halabi, J. Kalpathy-Cramer, R. Ball, J. T. Mongan, A. Stein, F. C. Kitamura, M. P. Lungren _et al._ , “Construction of a machine learning dataset through collaboration: the rsna 2019 brain ct hemorrhage challenge,” _Radiology: Artificial Intelligence_ , vol. 2, no. 3, p. e190211, 2020.
  * [9] S. M. Anouk, W. Carol, C. Chris, S. George, K.-C. Jayashree, E. k. Julia, P. Luciano, K. M. Marc, L. Matt, C. Phil, B. Robyn, and H. M. Safwan, “Rsna intracranial hemorrhage detection,” 2019. [Online]. Available: https://kaggle.com/competitions/rsna-intracranial-hemorrhage-detection
  * [10] S. Chilamkurthy, R. Ghosh, S. Tanamala, M. Biviji, N. G. Campeau, V. K. Venugopal, V. Mahajan, P. Rao, and P. Warier, “Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study,” _The Lancet_ , vol. 392, no. 10162, pp. 2388–2396, 2018.
  * [11] R. Wang, G. Fu, J. Li, and Y. Pei, “Diagnosis after zooming in: A multilabel classification model by imitating doctor reading habits to diagnose brain diseases,” _Medical physics_ , vol. 49, no. 11, pp. 7054–7070, 2022.
  * [12] K. Ramesh, G. K. Kumar, K. Swapna, D. Datta, and S. S. Rajest, “A review of medical image segmentation algorithms,” _EAI Endorsed Transactions on Pervasive Health and Technology_ , vol. 7, no. 27, pp. e6–e6, 2021.
  * [13] N. Yu, H. Yu, H. Li, N. Ma, C. Hu, and J. Wang, “A robust deep learning segmentation method for hematoma volumetric detection in intracerebral hemorrhage,” _Stroke_ , vol. 53, no. 1, pp. 167–176, 2022.
  * [14] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in _Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18_. Springer, 2015, pp. 234–241.
  * [15] M. Nijiati, A. Tuersun, Y. Zhang, Q. Yuan, P. Gong, A. Abulizi, A. Tuoheti, A. Abulaiti, and X. Zou, “A symmetric prior knowledge based deep learning model for intracerebral hemorrhage lesion segmentation,” _Frontiers in Physiology_ , vol. 13, p. 977427, 2022.
  * [16] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” _Advances in neural information processing systems_ , vol. 30, 2017.
  * [17] Q. Peng, X. Chen, C. Zhang, W. Li, J. Liu, T. Shi, Y. Wu, H. Feng, Y. Nian, and R. Hu, “Deep learning-based computed tomography image segmentation and volume measurement of intracerebral hemorrhage,” _Frontiers in Neuroscience_ , vol. 16, p. 965680, 2022.
  * [18] C. Shuo Chang, T. Sheuan Chang, J. Lin Yan, and L. Ko, “All attention u-net for semantic segmentation of intracranial hemorrhages in head ct images,” in _2022 IEEE Biomedical Circuits and Systems Conference (BioCAS)_ , 2022, pp. 600–604.
  * [19] J. Chang, I. Choi, and M. Lee, “Pesa r-cnn: Perihematomal edema guided scale adaptive r-cnn for hemorrhage segmentation,” _IEEE Journal of Biomedical and Health Informatics_ , vol. 27, no. 1, pp. 397–408, 2022.
  * [20] G. Tong, X. Wang, H. Jiang, A. Wu, W. Cheng, X. Cui, L. Bao, R. Cai, and W. Cai, “A deep learning model for automatic segmentation of intraparenchymal and intraventricular hemorrhage for catheter puncture path planning,” _IEEE journal of biomedical and health informatics_ , 2023.
  * [21] F. Isensee, T. Wald, C. Ulrich, M. Baumgartner, S. Roy, K. Maier-Hein, and P. F. Jaeger, “nnu-net revisited: A call for rigorous validation in 3d medical image segmentation,” _arXiv preprint arXiv:2404.09556_ , 2024.
  * [22] N. Tajbakhsh, L. Jeyaseelan, Q. Li, J. N. Chiang, Z. Wu, and X. Ding, “Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation,” _Medical image analysis_ , vol. 63, p. 101693, 2020.
  * [23] M. Hssayeni, M. Croock, A. Salman, H. Al-khafaji, Z. Yahya, and B. Ghoraani, “Computed tomography images for intracranial hemorrhage detection and segmentation,” _Intracranial hemorrhage segmentation using a deep convolutional model. Data_ , vol. 5, no. 1, p. 14, 2020.
  * [24] X. Li, K. Wang, J. Liu, H. Wang, M. Xu, and X. Liang, “The 2022 intracranial hemorrhage segmentation challenge on non-contrast head ct (ncct),” 2022.
  * [25] M. McCormick, X. Liu, J. Jomier, C. Marion, and L. Ibanez, “Itk: enabling reproducible research and open science,” _Frontiers in neuroinformatics_ , vol. 8, p. 13, 2014.
  * [26] M. Larobina and L. Murino, “Medical image file formats,” _Journal of digital imaging_ , vol. 27, pp. 200–206, 2014.
  * [27] J. Wasserthal, H.-C. Breit, M. T. Meyer, M. Pradella, D. Hinck, A. W. Sauter, T. Heye, D. T. Boll, J. Cyriac, S. Yang _et al._ , “Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images,” _Radiology: Artificial Intelligence_ , vol. 5, no. 5, 2023.
  * [28] F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,” _Nature methods_ , vol. 18, no. 2, pp. 203–211, 2021.
  * [29] S. Pieper, M. Halle, and R. Kikinis, “3d slicer,” in _2004 2nd IEEE international symposium on biomedical imaging: nano to macro (IEEE Cat No. 04EX821)_. IEEE, 2004, pp. 632–635.
  * [30] Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” in _Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19_. Springer, 2016, pp. 424–432.
  * [31] F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in _2016 fourth international conference on 3D vision (3DV)_. Ieee, 2016, pp. 565–571.
  * [32] A. Myronenko, “3d mri brain tumor segmentation using autoencoder regularization,” in _Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4_. Springer, 2019, pp. 311–320.
  * [33] O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, B. Kainz _et al._ , “Attention u-net: Learning where to look for the pancreas. arxiv 2018,” _arXiv preprint arXiv:1804.03999_ , 1804.
  * [34] A. Hatamizadeh, Y. Tang, V. Nath, D. Yang, A. Myronenko, B. Landman, H. R. Roth, and D. Xu, “Unetr: Transformers for 3d medical image segmentation,” in _Proceedings of the IEEE/CVF winter conference on applications of computer vision_ , 2022, pp. 574–584.
  * [35] A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, H. R. Roth, and D. Xu, “Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,” in _International MICCAI Brainlesion Workshop_. Springer, 2021, pp. 272–284.
  * [36] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in _Proceedings of the IEEE/CVF international conference on computer vision_ , 2021, pp. 10 012–10 022.
  * [37] C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. Jorge Cardoso, “Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,” in _Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings 3_. Springer, 2017, pp. 240–248.
  * [38] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga _et al._ , “Pytorch: An imperative style, high-performance deep learning library,” _Advances in neural information processing systems_ , vol. 32, 2019.
  * [39] M. J. Cardoso, W. Li, R. Brown, N. Ma, E. Kerfoot, Y. Wang, B. Murrey, A. Myronenko, C. Zhao, D. Yang _et al._ , “Monai: An open-source framework for deep learning in healthcare,” _arXiv preprint arXiv:2211.02701_ , 2022.
  * [40] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” _arXiv preprint arXiv:1412.6980_ , 2014.
  * [41] L. Maier-Hein, A. Reinke, P. Godau, M. D. Tizabi, F. Buettner, E. Christodoulou, B. Glocker, F. Isensee, J. Kleesiek, M. Kozubek _et al._ , “Metrics reloaded: recommendations for image analysis validation,” _Nature methods_ , pp. 1–18, 2024.
  * [42] H.-Y. Zhou, J. Guo, Y. Zhang, L. Yu, L. Wang, and Y. Yu, “nnformer: Interleaved transformer for volumetric segmentation,” _arXiv preprint arXiv:2109.03201_ , 2021.
  * [43] Y. Xie, J. Zhang, C. Shen, and Y. Xia, “Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation,” in _Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part III 24_. Springer, 2021, pp. 171–180.