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# From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing
Technical Committee

Selin Aviyente, , Alejandro Frangi, , Erik Meijering, , Arrate Muñoz-Barrutia,
, Michael Liebling, , Dimitri Van De Ville, , Jean-Christophe Olivo-Marin, ,
Jelena Kovačević, , Michael Unser The authors are the present and past BISP TC
Chairs in reverse chronological order.S. Aviyente is with the Department of
Electrical and Computer Engineering, Michigan State University, East Lansing,
MI, 48824 USA, Email<EMAIL_ADDRESS>F Frangi is with the
Centre for Computational Imaging and Simulation Technologies in Biomedicine
(CISTB), Schools of Computing and Medicine, University of Leeds, LS2 9JT
Leeds, UK; the Alan Turing Institute, London, UK; and the Departments of
Electrical Engineering (ESAT) and Cardiovascular Sciences, KU Leuven, Leuven,
Belgium, Email<EMAIL_ADDRESS>Meijering is with the School of
Computer Science and Engineering, University of New South Wales, Sydney, NSW
2052, Australia. Email<EMAIL_ADDRESS>Muñoz-Barrutia is
with the Bioengineering Department, Universidad Carlos III de Madrid, Leganés,
Madrid, Spain. Email<EMAIL_ADDRESS>Liebling is with the Idiap
Research Institute, Martigny, Switzerland. Email:
<EMAIL_ADDRESS>Van De Ville is with the Neuro-X Institute,
School of Engineering, EPFL, Geneva and Faculty of Medicine, University of
Geneva. Email<EMAIL_ADDRESS>Olivo-Marin is with
the Cell Biology and Infection Department, Institut Pasteur, Paris, France.
Email<EMAIL_ADDRESS>Kovačević is with the New York University
Tandon School of Engineering, Brooklyn, NY. Email<EMAIL_ADDRESS>Unser is with the School of Engineering, EPFL, Lausanne, Switzerland. Email:
<EMAIL_ADDRESS>
##  I Introduction

The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the
IEEE Signal Processing Society (SPS) promotes activities within the broad
technical field of biomedical image and signal processing. Areas of interest
include medical and biological imaging, digital pathology, molecular imaging,
microscopy, and associated computational imaging, image analysis, and image-
guided treatment, alongside physiological signal processing, computational
biology, and bioinformatics. BISP has 40 members and covers a wide range of
EDICS, including CIS-MI: Medical Imaging, BIO-MIA: Medical Image Analysis,
BIO-BI: Biological Imaging, BIO: Biomedical Signal Processing, BIO-BCI:
Brain/Human-Computer Interfaces, and BIO-INFR: Bioinformatics. BISP plays a
central role in the organization of the IEEE International Symposium on
Biomedical Imaging (ISBI) and contributes to the technical sessions at the
IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), and the IEEE International Conference on Image Processing (ICIP). In
this paper, we provide a brief history of the TC, review the technological and
methodological contributions its community delivered, and highlight promising
new directions we anticipate.

##  II Historical context

Until 2002, the signal processing activities related to biomedical imaging
were overseen by the Image and Multidimensional Digital Signal Processing
(IMDSP) committee of IEEE SPS, and typically presented in topical sessions at
ICIP and ICASSP. IEEE SPS also co-sponsored the IEEE Transactions on Medical
Imaging. Yet, at the turn of the century, the importance of imaging in
medicine and biology was becoming increasingly apparent. At the same time,
advanced signal processing played an ever-increasing role in the
reconstruction and analysis of the vast volume of images produced. This
realization was reinforced by the creation of the National Institute of
BioImaging and Bioengineering (NIBIB) by the NIH and US Congress in December
2000, as an agency solely dedicated to the advancement of imaging technology
and bioengineering. The latter was an official recognition of the crucial role
of engineering in biomedical research and of the necessity to fund such
research activities. This motivated the IEEE SPS and the IEEE Engineering in
Medicine and Biology Society (EMBS) to join forces and demonstrate leadership
in biomedical imaging research. Accordingly, it was decided to launch a new
regular meeting on biomedical imaging: the IEEE International Symposium on
Biomedical Imaging (ISBI) (Figure 1), in close collaboration with NIBIB. The
task of organizing this conference was given to Michael Unser (SPS
representative) and Zhi-Pei Liang (EMBS representative). The fact that Prof.
Unser had spent the larger part of his career at the NIH facilitated the
interaction with NIBIB, which committed to supporting the first edition of
ISBI that took place in Washington, DC, USA, in July 2002. The unique aspect
of ISBI was to cover the whole spectrum and range of imaging, from nano
(electron and optical microscopy) to macro (medical imaging modalities) [1].

###  II-A Creation of a Dedicated Technical Committee

With the creation of ISBI and its establishment as the IEEE flagship
conference in biomedical imaging, the next step was to put in place a
structure to promote the conference, and ensure its scientific quality. Since
Prof. Unser with his team had formulated the vision for ISBI, he was
instructed to form an SPS TC on Bio Image and Signal Processing and to make
suggestions for its initial membership. In addition to its strategic role in
bioimaging, BISP was given the mission to oversee the SPS activities in
biomedical signal processing (e.g., analysis of physiological signals) and
bioinformatics—in short, to be responsible for all signal processing
activities in medicine and biology, and to maintain a liaison with its sister
TC in EMBS on Biomedical Imaging and Image Processing (BIIP). Since the
inception of the TC, BISP members have also actively participated in cross-
society activities, such as the IEEE Life Science Technical Community (LSTC)
and the IEEE BRAIN Technical Community.

Figure 1: Original ISBI logo designed by Annette Unser (graphic artist and
sister of the founding chair), with the eye projecting a distinctive vision
for ISBI. Observers have suggested that the central motif illustrates the
Fourier slice theorem; or for the more pessimistic ones, the typical artifacts
of the filtered back-projection reconstruction algorithm.

###  II-B Workshops and Conferences

The inaugural ISBI was held between July 7-10, 2002, at the Ritz-Carlton
Hotel, Washington, DC. The meeting was organized jointly by the SPS and the
EMBS. Significant support was provided by both NIH and NIBIB ($\$40$K in
grants and approximately 50 paid registrants). The conference was a huge
success, providing a venue for interdisciplinary exchange with researchers
from both medical and biological imaging areas. It was also well attended by
NIH representatives. Dr. Elias Zerhouni gave the opening address, the then
newly appointed NIH director. This attracted many observers, including members
of the press. Dr. Roderic Pettigrew also delivered a speech—the very first in
his new function as director of NIBIB. Both directors expressed a strong
interest in the conference and commented on the need to strengthen the links
between the engineering and biomedical research communities.

ISBI 2002 had two parts to the scientific program: 1) the contributed papers
reviewed by the technical program committee, and 2) the invited papers. Out of
355 submitted papers, 73 were accepted for oral presentation and 142 for
poster presentation. The invited program consisted of 10 special sessions that
were organized by leading researchers in the field.

Despite a very active and growing community, BISP membership has only
increased moderately, yet the TC has always strived for a well-balanced
representation across the broad range of sub-fields it covers. A key task for
BISP members was to ensure that papers submitted to ISBI (or to dedicated
tracks at ICASSP and ICIP) would benefit from the availability of a highly
qualified pool of reviewers and editors. BISP members also participated in the
many activities related to ISBI, as members of the organizing committee. Since
2006, ISBI has been held regularly as an annual four-day conference. Figure 2
summarizes with a word cloud the keywords from the keynote titles since ISBI’s
inception. Outstanding clinical and technical speakers delivered their visions
for the field, relevant trends, or challenges ahead. Among our distinguished
speakers, there were Nobel Prize winners and top NIH officers. In 2022, ISBI
was held for the first time as a fully hybrid conference in Kolkata, India,
with every session having both physical and online speakers and audiences. Out
of 785 submitted papers, 309 were accepted. In addition to the regular paper
sessions, there were 5 special sessions, 5 plenary talks, 6 challenges, and 6
tutorials. In addition to ISBI, BISP has been an active contributor to ICASSP
since 2006, with the number of submitted papers increasing from 100 in 2006 to
222 in 2020.

Figure 2: Word cloud with the most frequent keywords from the titles of all
the ISBI keynotes in the past 20 years. Clinicians, Nobel Prize Winners, and
NIH Officials have given these talks, among other contributors.

##  III Biomedical Image and Signal Acquisition Across Scales

Biomedical data comes in many shapes and forms. BISP focuses on digital images
and signals, which can be automatically processed and analyzed by advanced
computational methods. To study biological processes in health and disease,
many images and signal acquisition techniques have been developed in the past
century, reflecting the fact that biological phenomena occur at different
spatial and temporal scales (Figure 3). Before discussing methodological
advances, we survey some of the most prominent modalities for molecular and
cellular imaging, tissue imaging, anatomical and functional medical imaging,
neuroimaging, physiological signal recording, and several data types in
bioinformatics.

###  III-A Molecular and Cellular Imaging

Molecular and cellular imaging has undergone multiple revolutions in the past
three decades, moving from a mainly qualitative to a mostly quantitative field
thanks to advances in molecular probes as well as imaging modalities [2, 3,
4]. With the advent of the green fluorescent protein (GFP), pioneered by Osamu
Shimomura, Martin Chalfie, and Roger Y. Tsien (Nobel Prize in Chemistry 2008),
microscopy has become one of the key tools in biological research [3].
Fluorescence microscopy became a fast-growing field to study, quantitatively
and often within a high throughput/content setup, processes, and organelles
within living cells and organisms. More recently, a vast leap has been made
with the invention of super-resolution techniques, based on seminal work by
Eric Betzig, Stefan W. Hell, and William E. Moerner (Nobel Prize in Chemistry
2014). Another recent development is selective plane illumination (light
sheet) microscopy, which allows long-term biological studies of living
organisms with a rapid acquisition, high resolution, and minimal
phototoxicity. Classical image and signal processing methods, as well as
modern deep learning-based methods, are increasingly used not only for
reconstruction and deconvolution of the data produced by advanced microscopy
imaging modalities, but also for enabling downstream tasks such as
segmentation, classification, tracing, and tracking [4, 5, 6]. Fluorescence
microscopy has enabled the study of dynamic processes within cells and
complements structural and static imaging modalities, such as scanning probe
microscopy, electron microscopy (Nobel Prize in Physics 1986), cryo-electron
microscopy (Nobel Prize in Chemistry 2017), which have become part of the vast
arsenal of tools for the life sciences. In recent years, several new journals
or sections in established publications, e.g., Biological Imaging, Frontiers
in Bioinformatics, Cell Reports Methods, have been launched to host the
increasing number of publications in this domain.

Figure 3: Examples of the many data acquisition modalities in biomedical image
and signal processing operating at various spatial scales. Abbreviations: BFM
= bright-field microscopy, CT = computed tomography, ECG =
electrocardiography, EEG = electroencephalography, EMG = electromyography, FCM
= flow cytometry, FM = fluorescence microscopy, GE = gene expression, MA =
micro array, MRI = magnetic resonance imaging, US = ultrasound, WSI = whole-
slide imaging.

###  III-B Tissue Imaging

Microscopy can also be employed to study biological phenomena at the tissue
level. Rather than imaging individual cells (molecular/cellular imaging) for
basic research, or anatomical structures and entire organs (medical imaging)
for clinical diagnostics, imaging of tissue slides prepared from biopsies
enables characterizing and grading disease processes ex vivo as revealed by
abnormal cell arrangements and tissue architectures. This is especially
important in researching and diagnosing pathologies, notably the many types of
cancer (the field of oncology), known to manifest themselves first at the cell
and tissue level (histopathology). Recent advances in digital whole-slide
imaging (WSI) systems (sometimes referred to as virtual microscopy) have
created unprecedented opportunities for computer-aided diagnosis (CAD) in
histopathology. Image and signal processing play a prominent role in
histopathological image analysis, especially for breast cancer, prostate
cancer, lung cancer, tumor pathology in many other forms of cancer, and cancer
prognosis. Review papers have summarized and commented on the challenges and
opportunities in this domain [7, 8]. Typical tasks include the detection and
segmentation of cell nuclei, glands, and lymphocytes, and computing various
quantitative morphological features for classification. This, in turn,
requires effective techniques for image normalization as well as feature
selection and dimensionality reduction. Analysis of the spatial arrangements
of tissues is often facilitated by graph-based representation and topological
modeling. The challenges in histopathological image analysis are not only due
to the high complexities of the image structures, but also to the typically
large image sizes, on the order of tens of thousands by tens of thousands of
pixels, at multiple magnifications. Traditionally, tissue classification has
been performed using handcrafted features and machine learning methods, such
as support vector machines (SVM) and random forests (RF). Still, there is now
growing evidence that deep artificial neural networks provide fast and
reliable image analysis on par with seasoned pathologists and can serve as a
synergistic tool for the latter to improve accuracy and throughput. However,
the full adoption of deep learning methods in pathology is hindered by the
lack of large and reliably annotated image cohorts documenting the large
diversity of diseases and the high variability of disease traits, calling for
efficient automated annotation methods.

###  III-C Medical Imaging

Medical imaging refers to the imaging techniques and processes to gain
insights into the interior of a body for clinical diagnosis or medical
intervention, as well as visual representation of the function of organs or
tissues. Medical imaging can be divided into structural or anatomical imaging
and functional or physiological imaging. Many medical imaging techniques have
been invented since the discovery of X-rays by Wilhelm Conrad Röntgen in 1895,
which form the basis of projection X-rays and computed tomography. In 1946,
Bloch and Purcell (Nobel Prize 1952) independently discovered nuclear magnetic
resonance, which formed the basis of MRI (Paul Lauterbur and Sir Peter
Mansfield in the 70s, Nobel Prize 2003). MRI developed into a platform
technology with many specialized techniques, providing insights into anatomy,
perfusion, diffusion, or deformation. Ultrasound was used in medicine since
World War II, but it was not until the late 70s that ultrasound imaging was
popularized as a clinical imaging modality. In 1963, David Kuhl and Roy
Edwards introduced emission reconstruction tomography, a method that later
became single-photon emission computed tomography (SPECT). Sir Godfrey
Hounsfield (Nobel Prize 1979) developed the first prototype of a computed
tomography (CT) scanner in 1963, thanks to the availability of modern
computers to solve the complex image reconstruction problems that ensued.
Michael Hoffman, Michel Ter-Pogossian and Michael E. Phelps built the first
positron emission tomography (PET) camera in 1974. Underpinning these
techniques, there are considerable signal and image processing problems,
ranging from image reconstruction, image deconvolution, image denoising and
restoration, image transformation, and multimodal image co-registration. Over
the last 3-4 decades, several signal processing developments had major impacts
on medical imaging. For example, wavelets and splines played a major role in
medical image interpolation, denoising, and filtering. Mutual information and
other information theoretic metrics revolutionized multimodal image
registration. Compressed sensing provided a novel approach to find solutions
to under-determined linear equations with major impact in image reconstruction
from projections (CT), from k-space (MRI) or from sensors (US), and, of
course, the latest developments of deep learning and their impact across the
board.

###  III-D Neuroimaging: From Images to Connectomes

Another flourishing outlet for biomedical signal and image processing has been
the processing of structural and functional MRI (fMRI) data [9]. The concept
of establishing connectivity between brain regions has been fundamental in
many emerging methodologies [10] (Figure 4). Structural connectivity is
defined by the strength of inter-regional axonal fiber pathways that can be
revealed using tractography methods applied to diffusion-weighted MRI (dMRI)
data. Functional connectivity relates to the statistical interdependency
between two time series of blood oxygenation-level dependent (BOLD) activity.
When established for all possible pairs of regions, defined by a brain atlas,
this leads to the structural and functional connectomes, respectively. While
hemodynamic imaging has been the most commonly used modality for constructing
the functional connectome, neurophysiological signals such as
magneto/electroencephalography (M/EEG) have also been adopted thanks to their
high temporal resolution. Both model-based and data-driven methods have been
developed to quantify functional connectivity, including multivariate auto-
regressive models, graphical models, phase synchrony and information-theoretic
metrics. Functional connectivity is also intimately related to blind source
separation. This, in turn, relies on decomposing the data matrix into
components driven by maximizing covariance (using techniques such as principal
component analysis (PCA), singular value decomposition (SVD), or higher-order
SVD (HOSVD)) or statistical independence (using independent component analysis
(ICA)), which has become part of the pipeline in fMRI software suites. During
the past decade, dynamic functional connectivity has been introduced to
acknowledge the changing patterns of co-fluctuations, either by sliding-window
functional connectivity, instantaneous activation patterns, or auto-regressive
models.

The resulting connectomes are commonly represented by graphs and analyzed to
reveal organizational principles using, for instance, local clustering
coefficient, efficiency, small-worldness, centrality, and phenotype behavior
and disorder. These approaches have been applied to other species’ connectomes
obtained using different modalities, leading to a new field, network
neuroscience, which further branched out to machine learning, information
theory, and computational neuroscience. Finally, the emergence of graph signal
processing has found its way into the neuroimaging field [11], providing a way
to combine brain structure (i.e., graph defined by the structural connectome)
and brain function (i.e., graph signals obtained by fMRI snapshots of brain
activity).

Figure 4: Structural and functional connectomes play a key role in
representing relationships between brain regions. Top: From diffusion-weighted
MRI (dMRI), the orientation of axonal bundles in white matter can be extracted
and processed by tractography to obtain the strength of structural
connectivity between all pairs of regions. Bottom: Functional MRI (fMRI)
provides a series of volumes where the blood oxygenation-level dependent
(BOLD) signal is related to neuronal activity. Time series analysis exists in
large diversity, but the functional connectome that reflects the statistical
inter-dependencies between pairs of time series is one of them.

###  III-E Physiological Signal Processing (M/EEG, EMG, ECG)

With the advent of wearable sensors, physiological signals are collected for
various applications and are central to multiple new technologies, including
brain computer interfacing (BCI) / human-machine interfacing (HMI),
neurorehabilitation, neuroprosthetics, in addition to medical diagnosis and
monitoring [12]. The most employed physiological signals include
electromyogram (EMG), respiration, speech, heart rate variability,
photoplethysmography (PPG), electrocardiogram (ECG) and
magneto/electroencephalogram (M/EEG). Some prominent application areas that
routinely rely on physiological signals include emotion recognition,
autonomous driving, mental health and assistive technologies. For example,
physiological changes such as heart rate, skin conductance and PPG signals are
monitored for measuring human emotions as they are more reliable and harder to
alter compared to explicit behaviors such as facial expressions and speech.
Similarly, the design of human-machine interfacing systems requires the
consistent and accurate decoding of motor intent with minimal training and
calibration. The multimodal, high-density sensing technology coupled with the
nonstationary and nonlinear nature of biological signals requires the
development of innovative signal processing and machine learning techniques to
process, decompose, and decode these signals. Some methodologies employed in
this area of research include blind source separation, time-frequency
analysis, multimodal data fusion, supervised (or semi-supervised) learning and
deep learning. Different tasks, such as event detection, prediction and
diagnosis, have been addressed using these tools.

###  III-F Bioinformatics

Since the turn of the century, major advances in molecular biology, along with
advances in genomic data acquisition technologies, led to to the growth of
biological data generated and shared by the scientific community; e.g., The
Cancer Genome Atlas (TCGA). This data brings with itself significant
challenges in the identification of gene expression mechanisms, the
determination of proteins encoded by the genes, understanding how these
interact; i.e., gene regulatory networks, and marker identification.

BISP has contributed to this area by introducing a new line of research:
genomic and proteomic signal processing [13]. While biomolecular sequence
analysis has been addressed by computer scientists, physicists, and
mathematicians, it was only at the turn of the century that signal processing
has started to play a role in this area. Genomic and proteomic data can be
modeled as noisy, continuous or discrete signals that represent the molecular
structure and activities in cells. The high dimensionality, variability, and
complexity of this data require the development of new signal processing
methodologies that effectively deal with these challenges. By mapping the
character strings corresponding to gene sequences into numerical sequences,
signal processing offers a set of tools for solving highly relevant problems.
For example, the magnitude and the phase of properly defined Fourier
transforms can be used to predict important properties of protein coding
regions in DNA. Similarly, concepts from digital filtering can be employed to
analyze the mapping of DNA into proteins and the interdependence of two
sequences. These and other signal processing methodologies, such as frequency
domain analysis, high-dimensional data analysis, compressed sensing, and
network inference, have played important roles in the advancement of this
field. Genomic and proteomic signal processing have had a substantial impact
on different applications areas, including sequence analysis, microarray
analysis, structure identification and regulatory networks.

From 2005 to 2013, the IEEE International Workshop on Genomic Signal
Processing and Statistics (GENSiPS) was organized annually and sponsored by
IEEE SPS. These workshops covered topics related to high-dimensional genomic
data analysis, gene regulatory network inference, marker identification, drug
screening and proteomics.

##  IV Methodological Advances in Biomedical Image and Signal Processing

The field of biomedical image and signal processing has seen major
methodological advances, not only in how data are recorded, stored, and
transmitted, but also in how they are best represented, processed, analyzed,
and modeled, depending on the application domain. Many paradigms have been
proposed in recent decades by various schools of thought, resulting in a wide
range of theories and methods for challenging problems such as image and
signal restoration, reconstruction, detection, segmentation, classification,
pattern recognition, and statistical analysis, as documented in numerous
textbooks and reviews. Given the limited space in this article, we only
briefly discuss some of the most impactful developments in recent years,
including methods for computational imaging, deep learning-based image and
signal analysis, and efforts to stimulate reproducible research.

###  IV-A Biomedical Computational Imaging

(a) Ground Truth |  (b) SNR = 24.06 dB |  (c) SNR = 29.06 dB |  (d) SNR = 35.38 dB
---|---|---|---
Figure 5: Comparison of tomographic reconstruction algorithms for compressed
sensing (CS) with a reduction of the number of views by 7. (a) Ground truth
(high quality reconstruction from 1000 views). (b) Conventional reconstruction
(filtered backprojection) from a subset of 143 views. (c) CS reconstruction
using total variation regularization. (d) CS reconstruction using a
convolution neural network (FBPConvNet). The middle panel displays the image
residuals with the same contrast. The magnified images in the lower panel
represent the corresponding ROI overlaid in (a). The figure is adapted from
[14].

Most biomedical imaging modalities have a strong computational component, as
they systematically rely on signal processing to reconstruct the images from
the raw imaging data. The data can take the form of: (i) 2D projections of a
3D object as in X-ray tomography, PET, and cryo-electron microscopy, (ii) a
series of blurred 2D slices of a specimen as in fluorescence microscopy, or
(iii) samples of the Fourier transform of an object, as in MRI and optical
diffraction tomography. By capitalizing on the knowledge of the imaging
physics (linear forward model), the reconstruction task can then be formulated
as an inverse problem. Until recently, classical imaging (MRI, CT) relied on a
direct inversion of this forward model. This is achieved, for instance, by
inverse Fourier transformation in MRI (with uniform sampling in k-space) or by
inverse Radon transformation (the celebrated filtered-back-projection
algorithm) in CT. This works well when the measurements are sufficiently
numerous and diverse, and when the noise is negligible. Besides streamlining
of the reconstruction process itself (improved non-uniform (NU) fast Fourier
transform (FFT), optimization of sampling parameters, etc.), the earlier
involvement of the SP community was to combat the effect of noise with the
help of advanced statistical methods. One notable example of such success is
the method of ordered subsets in PET and SPECT [15]. Another fruitful approach
inspired by Wiener filtering is to inject prior information in a stochastic
model (e.g., generalized Gaussian in a transformed domain), which makes a
direct link between maximum a posteriori (MAP) reconstruction and
regularization/energy minimization techniques [16]. The more significant
revolution in imaging came with compressed sensing (CS) with theorists [17,
18] and then experimentalists [19, 20] showing the feasibility of image
reconstruction from a reduced set of measurements. A milestone in this line of
research was the development of efficient minimization methods under sparsity
constraints, in particular the (fast) iterative soft thresholding algorithm
(ISTA) and alternating direction method of multipliers (ADMM) [21]. The main
benefit of CS is to enable faster imaging, which reduces not only cost but
also radiation exposure (in the case of X-ray or PET/SPECT). This has led to a
major revolution in MRI, with fast (CS-based) imaging protocols now offered by
most vendors of MRI technology. While CS kept SPS researchers busy from
2005-2017, another wave then overtook the field—the incorporation of neural
networks in the image reconstruction pipeline. This led to further significant
improvement in image quality (Figure 5), especially in extreme scenarios,
e.g., low signal-to-noise ratio and CS [14]. While image reconstruction based
on convolutional neural networks (CNNs) still has shortcomings—they are poorly
understood and can behave erratically (lack of stability, hallucination)—they
demonstrate the potential of better reconstruction quality [22]. It is
noteworthy that it took SP pioneers less than a year to tune their new CNN-
based methods to the point where they would outperform sparsity-based methods
for CS in public imaging challenges by the same margin (typically $>4$dB) than
the latter had achieved over classical reconstruction during a whole decade of
intense research activity.  CNNs and learning-based techniques are presently
at the center of attention of the researcher community. Recent trends include
the development of more sophisticated iterative reconstruction schemes that
rely on CNNs to regularize the solution—as enabled by the Plug-and-play
framework [23]—as well as the use of deep learning for the resolution of more
challenging non-linear inverse problems such as diffuse optical tomography
(DOT) [24] and diffraction tomography.

###  IV-B Deep Learning in Biomedical Image and Signal Processing

Traditionally, methods for image and signal processing have been based on
carefully designed mathematical models of the phenomena and anomalies of
interest, and their translation into efficient rules-based computational
algorithms. Illustrative examples of this are mathematical point-spread
function (PSF) models of widefield or confocal microscopes based on physical
(optical) principles, serving as the basis for various image restoration
methods (in particular deconvolution) [25] and object detection methods (such
as single molecule localization) [26]. However, as in many other fields, the
demand for new and better methods from practitioners in biology and medical
diagnostics outstrips the supply of researchers and developers in image and
signal processing. That is, there are many more biologists and physicians in
the world looking for tools to facilitate their data processing workflows than
there are scientists and engineers looking to develop mathematical models and
image/signal processing algorithms specifically for biomedical applications.
Moreover, especially in the biomedical field, many image and signal analysis
tasks are notoriously difficult to model mathematically, due to the complex
nature of the problem, the high ambiguity of the data, and the subjectivity of
human experts who define the gold standards for interpreting the data. Thus,
as imaging and measurement devices improved over the years, and the number of
potentially automated data processing tasks grew, the need for more generic,
data-driven, learning-based methods also increased.

In the past decade, the rapidly growing availability of large datasets,
powerful computing capabilities, and open-access software libraries and
frameworks have accelerated the development and adoption of machine learning
and deep learning methods in biomedical image and signal processing [27, 28,
29, 6]. These methods show increasingly superior performance in benchmarking
studies for various tasks, including reconstruction, restoration, detection,
segmentation, classification, and tracking. In particular, deep learning of
artificial neural networks has become a popular approach for solving data
analysis problems where multimodal, multidimensional, multiparametric datasets
need to be jointly processed, posing a clear challenge to traditional analysis
methods. For the processing of biomedical images, especially CNNs have become
mainstream, a prominent example of this being the U-Net architecture [30], of
which many variants exist for various tasks and applications, such as
segmentation (Figure 6). For biomedical signal processing, especially for
dealing with time series, recurrent neural networks (RNNs) such as the long
short-term memory (LSTM) unit have seen widespread adoption. However, despite
promising results, many challenges remain to be addressed before deep learning
solutions can be integrated with full confidence and accountability into the
workflows of biomedical practitioners, such as developing ways to incorporate
expert knowledge and improving the explainability and generalizability of the
models (see the discussion of future directions in the last section).

Figure 6: Examples of cell segmentation using deep neural networks in diverse
types of microscopy images. From top-left to bottom-right, the shown images
were captured using bright-field microscopy, phase-contrast microscopy
($2\times$), differential interference contrast (DIC) microscopy, and
fluorescence microscopy ($4\times$), and contain distinct types of cells in
different spatial arrangements (densities and confluences). The segmentation
results are the overlaid colored cell contours (arbitrary colors). These
results were produced using a single deep learning framework with a U-Net-like
macro-architecture consisting of various layers/blocks whose micro-
architectures were optimized automatically using a neural architecture search
approach [31]. The examples illustrate the power of deep learning and the
level of automation that can be achieved nowadays in optimizing image
segmentation results without requiring expert user input, other than manual
annotations, to learn from.

###  IV-C Reproducible Research, Open Access, Code

Reproducing the results presented in a research paper can be very challenging.
For a computational algorithm, details such as the exact dataset,
initialization or termination procedures, and precise parameter values are
often omitted in the publication for various reasons. This makes it difficult,
if not impossible, for someone else to obtain the same results [32]. In the
early 2000s, the need to boost research by implementing reproducible research
practices became apparent. Vandewalle et al. [32] published a seminal
manuscript in the IEEE Signal Processing Magazine in 2009, which defines
reproducible research: “A research work is called reproducible if all
information relevant to the work, including, but not limited to text, data,
and code, is made available, so that an independent researcher can reproduce
the results.” The authors also distinguish six levels of reproducibility, from
Level 5 (an independent researcher can easily reproduce results with at most
15 minutes of user effort, requiring only standard, freely available tools—C
compiler, etc.) to Level 0 (an independent researcher can reproduce results).

Figure 7: The IEEE International Symposium on Biomedical Imaging (ISBI) is the
premier scientific venue for the BISP TC. Since 2012, our community has
organized over 60 challenges, where open datasets, well-specified tasks, and
evaluation metrics have been made available for multiple groups to
participate, compete, and learn from each other. Challenges have covered many
imaging modalities and scales, image computing tasks, and organ systems.

The issue of reproducibility has been raised many times in the past few
decades. In the 1980s, there was a growing awareness of poor building on the
previous work of others [33]. Published algorithms were frequently evaluated,
with only select data and _ad hoc_ metrics. The comparison of algorithms and
software performance was difficult. In the 1990s, the first benchmark
initiative in biomedical imaging, ‘Retrospective Image Registration Experiment
(RIRE)’, appeared at Vanderbilt University [34]. We needed to wait until the
early 2010s for bioimaging reference datasets and challenges (benchmarks
associated with competitions) to appear. ISBI 2012 in Barcelona was the first
edition of the symposium holding challenges, on the following topics: 1)
Particle tracking [35]; 2) Segmentation of neuronal structures in EM stacks;
3) Vessel segmentation in the lung [36]; 4) Cardiac delayed-enhancement
magnetic resonance image segmentation; 5) High angular resolution diffusion
imaging; and 6) Challenge US: Biometric measurements from fetal ultrasound
images. At ISBI 2015, Prof. Ronneberger’s team won the Cell Tracking Challenge
(third edition) [37] and the dental X-ray image segmentation challenge with
their U-Net [30]. Figure 7 shows a word cloud of the challenge titles over the
years; detection, images and tracking occupy a prominent place in the cloud.

In the bioimaging community, the push for reproducibility led to several open
software platforms, such as Cell Profiler (https://cellprofiler.org/), Fiji
(https://fiji.sc/) and Icy (https://icy.bioimageanalysis.org/). They were made
available to the community in the early 2000s to share the then state-of-the-
art analytical methods, which are now used for integrated deep learning
framework deployment [38].

Finally, imaging challenges foster collaboration between institutions and
continents. Since 2012, over 60 challenges have been organized, led by
multiple institutions. Of these, 31 were organized by European institutions,
12 by organizations in the Americas, 10 by Asia or Oceania, and 6 involving
cross-continental collaboration from the Americas, Asia, and Europe. These
collaborations drove our community to learn from the strengths and
pitfalls[39] in organizing challenges and interpreting their results [40] and
thus developed best-practice guidelines for transparent reporting [41].

##  V Future Directions

Advanced technologies for capturing biomedical images and signals have made a
growing and lasting positive impact on clinical diagnostics and therapeutics,
medical research, and life sciences. They will continue to help improve our
understanding of the conditions underlying human health and how to prevent and
treat disease. Modern biomedical image and signal acquisition systems are
based on a wide range of physical phenomena (electricity, magnetism, light,
sound, force, etc.) capable of providing complementary information about the
anatomical and functional properties of the human body and living organisms in
general. Also, the sensitivity, resolution, and quality of these systems have
improved dramatically over the years, to the point where automated image and
signal processing are now indispensable in virtually all clinical and
biomedical research applications. At this point in time, unlike in the past
century, the chances of discovering totally new physical principles that could
ultimately be used in biomedical practice have diminished, yet the challenges
of fully exploiting existing technologies are far from having been solved. One
of the main problems for the image and signal processing community in the
years ahead will be to develop effective methods for data fusion and
integration [42] to maximize the potential of multimodal and correlative
imaging, as well as combining imaging and non-imaging (e.g. “omics”) data.
This requires finding solutions to dealing with the fundamentally different
nature of different data sources, and the inevitable imbalances in the data,
but also with the huge volumes (terabytes and no doubt soon petabytes) of
multimodal datasets.

Despite being comparatively young, the BISP community has already seen and
contributed to major paradigm shifts in biomedical image and signal
processing. Still, in addition to the data challenges mentioned above, many
fundamental technical challenges remain. Examples include some of the problems
caused by the increasing emphasis on learning-based approaches. For starters,
these approaches are typically very data-hungry, while the human and time
resources to produce high-quality annotated datasets are usually severely
limited, especially in the biomedical domain, not to mention additional
limiting factors due to privacy regulations. This requires the development of
semi/unsupervised learning approaches, data modeling and simulation methods
that can generate high-fidelity ground-truth data for training, and ways to
integrate expert domain knowledge into the learning framework. Furthermore,
even if sufficient annotated data can be collected to train a machine or deep
learning-based method for a given application, the resulting model is
considered a black box in the eyes of practitioners, who remain fully
accountable for any decisions based on the model’s predictions. Hence, there
is a great need for explainable and interpretable machine and deep learning
solutions. This is a fantastic opportunity for BISP researchers, many of whom
traditionally are used to developing mathematical models based on sound
physical principles, which by design are much more explainable and
interpretable. Another challenge stemming from limited training data is the
typically poor generalizability of the learned models. While organized
competitions in the field have done a great service by providing public
datasets and benchmarks, it is now well-known that models based on them do not
always work on private datasets. This calls for continuing efforts to make
public datasets less selective and more representative.

Given these and many other open challenges, the BISP TC will continue to play
an important role in developing ever more advanced image and signal processing
methodologies underpinning the next generation technologies needed to improve
the efficacy of biomedical practice and research. In this endeavor, we believe
future advances will not only come from continuing research efforts, but also
from innovations in education and how we train the next generation of
scientists and engineers in our field. Clearly, biomedical image and signal
processing has become increasingly multidisciplinary, requiring a deep
understanding of not only the mathematics and algorithms of how to model and
process digital images and signals, but also of the underlying physical
principles and limitations of data acquisition using various systems, the
biomedical knowledge to properly interpret the data, the data science and
informatics expertise to handle large datasets, and the experimental and
statistical know-how to validate methods thoroughly. To this end, we envision
the BISP TC to strengthen ties with the relevant bodies in the respective
disciplines, and to become more multidisciplinary in the future.

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