# Towards Automated Semantic Segmentation in Mammography Images

Cesar A. Sierra-Franco, Jan Hurtado, Victor de A. Thomaz,
Leonardo C. da Cruz, Santiago V. Silva, and Alberto B. Raposo  Department of
Informatics and Tecgraf Institute
Pontifícia Universidade Católica do Rio de Janeiro
Rio de Janeiro, Brazil
<EMAIL_ADDRESS>rio.br

###### Abstract

Mammography images are widely used to detect non-palpable breast lesions or
nodules, preventing cancer and providing the opportunity to plan interventions
when necessary. The identification of some structures of interest is essential
to make a diagnosis and evaluate image adequacy. Thus, computer-aided
detection systems can be helpful in assisting medical interpretation by
automatically segmenting these landmark structures. In this paper, we propose
a deep learning-based framework for the segmentation of the nipple, the
pectoral muscle, the fibroglandular tissue, and the fatty tissue on standard-
view mammography images. We introduce a large private segmentation dataset and
extensive experiments considering different deep-learning model architectures.
Our experiments demonstrate accurate segmentation performance on variate and
challenging cases, showing that this framework can be integrated into clinical
practice.

###### Index Terms:

mammography, semantic segmentation, deep learning, dataset

##  I Introduction

Mammography is a type of exam based on X-rays, whose main objective is to
evaluate the breast tissue. This type of exam is highly recommended to prevent
breast cancer because it is useful for visually identifying non-palpable
lesions or nodules. Generally, mammography exams include two types of view,
i.e., Medio-Lateral Oblique (MLO) and Cranio-Caudal (CC), applied to both
breasts. These modalities present top-bottom and side views of the breast,
allowing a multi-perspective simultaneous analysis. Both views capture a set
of anatomical structures of interest that are useful to locate abnormalities
and evaluate image acquisition adequacy. The reference structures consider but
are not limited to the nipple, the pectoral muscle, the fibro-glandular
tissue, and the fatty tissue. The automatic segmentation of these structures
can assist the medical interpretation or help less experienced operators to
understand the image content.

In this paper, we introduce a study for the application of deep learning
models on the mammography image segmentation task. More precisely, on the
segmentation of the structures of interest mentioned above and considering MLO
and CC views. We present a large private dataset that was created for this
purpose and experiments with different segmentation models. Our experiments
show that it is possible to automate the segmentation task on mammography
images with acceptable performance.

The rest of this paper is structured as follows. Section II explains some
related work for mammography image segmentation. Section III introduces the
dataset used for the segmentation task. Section IV shows our experiments and
results using deep learning models for segmentation. Finally, Section V
presents our conclusion and future work.

##  II Related work

Although the segmentation task on mammography images is typically related to
the segmentation of abnormalities [1], such as masses or nodules, we focus on
those methods that estimate landmark structures useful to define the spatial
distribution of the breast tissues.

The pectoral muscle is an important landmark structure used in the MLO view
assessment to define the risk of abnormalities or the adequacy of the image.
This structure is usually presented in the corner of the image as a triangular
shape. Its segmentation is challenging because it can present variate shapes
due to the multiple anatomical conditions, the occlusion caused by the
fibroglandular tissue, the inclusion of the minor pectoral muscle, and the
inclusion of skin folds, among others. Several methods were proposed in the
literature considering conventional signal processing and statistical analysis
[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. Rampun et al. propose a multi-step
method that uses a deep learning model for pectoral muscle boundary
segmentation and post-processing steps to obtain an accurate delimitation
[13]. Similarly, Soleimani et al. propose a deep learning model to segment the
pectoral muscle boundary and a graph-based analysis to improve it [14].
Instead of segmenting the boundary, Ali et al. present full pectoral muscle
shape segmentation using a U-Net-based deep learning model [15]. Also focusing
on the segmentation of the full shape of the pectoral muscle, Guo et al.
propose a two-step method that uses a U-Net to estimate pectoral muscle
confident regions and a GAN to estimate the final pectoral muscle shape [16].
Rubio and Montiel present a comparative study considering multiple deep
learning models and metrics for pectoral muscle segmentation [17]. In addition
to the pectoral muscle, they also consider the breast shape for segmentation.
Yu et al. propose a novel deep learning model that includes an attention
mechanism to achieve better results than simple encoder-decoder models.

Differently from the MLO view, the pectoral muscle in the CC view is not
always present, and its recognition is more challenging. The delimitation of
the pectoral muscle in the CC view is essential because it serves as a
reference for the posterior limit of the breast tissues. However, its
automated segmentation did not receive enough attention in the literature. Ge
et al. propose a shape-guided region-growing method that uses ellipse fitting
to approximate the pectoral muscle shape [18]. This approach is redesigned and
improved in [19] by using Markov random fields.

The nipple is another important landmark structure because it is useful to
register multiple views or modalities. This allows the operator to match
regions of interest and perform anatomical measurements. Some methods rely on
shape and texture analysis over different regions of the breast boundary [20,
21, 22, 23]. The methods proposed in [24] and [25] are based on the assumption
that the fibroglandular tissue converges at the nipple position. Thus, the
authors designed geometric descriptors to find the optimal convergence point.
Casti et al. propose a Hessian-based method that considers geometric
descriptors and constraints to define the nipple position [26]. Jiang et al.
propose a random forest classifier to detect subtle nipples using quantitative
radiomic features to define regions of interest [27]. Lin et al. propose a
deep learning classification model applied to a set of candidate patches
extracted from the mammography image [28]. The region that presents more
intersections of patches classified as possible nipples is selected as the
nipple position. These methods focus on defining a specific nipple position
instead of segmenting it.

The fibroglandular tissue is a risk region that requires a special focus
during the medical evaluation. Depending on the patient’s anatomy, this tissue
can be dense or scattered, where higher density presents a higher risk.
Several methods were proposed in the literature to segment dense
fibroglandular tissue regions [29], considering handcrafted [30, 31, 32, 33]
and data-driven [34, 35, 36, 37, 38] models. Although dense regions are the
most critical, clinical experts should also take care of scattered areas where
abnormalities can be found. Thus, segmenting dense and scattered regions is
important for the spatial description of the breast.

Other methods integrate the segmentation of different landmark structures in a
single solution. Tiryaki et al. present some experiments using multiple U-Net-
based models for the segmentation of the pectoral muscle, dense fibroglandular
tissue regions, and fatty tissues [39]. Considering these structures and
adding the nipple, Dubrovina et al. introduce a novel deep learning-based
framework for the segmentation task [40]. Using multiple deep learning models,
Bou presents segmentation results considering more granular structures, such
as vessels, calcifications, and skin, among others [41]. All of these methods
consider small datasets, making their evaluations less robust and confident
for their consideration in a real-world application. Also, the first two focus
on the MLO view only, as most of the segmentation methods described in this
section.

This paper presents a method for the integrated segmentation of the main
landmark structures, i.e., the pectoral muscle, the nipple, the fibroglandular
tissue, and the fatty tissue. Unlike most segmentation methods, we focus on
both standard views, MLO and CC. Thus, we are incorporating novel solutions
for the segmentation of the pectoral muscle and the nipple on CC view images.
In the case of fibroglandular tissue segmentation, our method considers dense
and scattered areas as regions of interest, making it useful for spatial
description instead of density analysis. Also, to the best of our knowledge,
we are introducing the largest dataset for the segmentation of multiple
landmark structures in mammography images. The latter allows us to show robust
experiments using multiple deep-learning models.

##  III Dataset

TABLE I: Data stratification subsets Subset | MLO | CC
---|---|---
|  A |  B |  C |  D |  N/D |  Total |  A |  B |  C |  D |  N/D |  Total
Train |  903 |  663 |  347 |  14 |  1523 |  3450 |  1000 |  810 |  502 |  64 |  1361 |  3737
Validation |  209 |  231 |  229 |  130 |  407 |  1206 |  133 |  119 |  112 |  108 |  471 |  943
Test |  84 |  96 |  95 |  90 |  192 |  557 |  47 |  57 |  55 |  58 |  240 |  457

###  III-A Raw data

To construct a mammography image segmentation dataset, we collected a set of
2581 mammography examinations performed in a specialized hospital using
different General Electric equipment. These examinations include 5213 MLO view
images and 5137 CC view images in DICOM format, where each examination can
repeat or lack any of the views for any of the patient’s breasts. All the
images were acquired using full-field digital mammography technology and
present pixel spacing equal to 0.1mm or 0.094mm.

This raw data do not consider images that present breast implants, complex
surgeries, or image artifacts that complicate the visualization of breast
tissues. Some cases with abnormalities are included but without a
corresponding clinical evaluation and categorization.

###  III-B Annotation guidelines

We consider four landmark structures for the segmentation problem, i.e.,
nipple, pectoral muscle, fibroglandular tissue, and fatty tissue. Because we
need a consistent segmentation dataset, we defined a set of guidelines for the
delimitation of these structures that was discussed with clinical experts.

For the nipple structure, we focus on the external and internal nipple
tissues, avoiding the inclusion of the areola. If the nipple location is not
clear, we can consider the fibroglandular tissue convergence region to guide a
deeper assessment. The external limit can be easily differentiated from the
image background in cases where the nipple is in profile and is not inverted
or flat. For inverted and flat in-profile nipples, we define an external limit
parallel to the breast contour. In both scenarios, the internal limit is
defined on the intersection with the milk ducts. The delimitation of nipples
that are not in profile presents a higher complexity. In this case, we
delineate the bright region generated by the nipple overlapping projection.
This region is usually presented as a clear rounded shape, but it can be
confused with fibroglandular tissue or abnormalities, needing clinical expert
assistance in some cases. The same methodology is applied in both standard
views.

The pectoral muscle in the MLO view usually presents a bright triangular shape
with noticeable muscle texture and certain curvature, where the boundary with
the fatty tissue is mostly clear. However, in cases where the fibroglandular
tissue is dense, the bottom region of the pectoral muscle can be occluded,
making its delineation difficult. Thus, we delineate the clear boundary and
its corresponding projection on the diffuse regions.

In the CC view, the pectoral muscle is not always present and can be in
different sizes. We delineate this structure if the shape is visible by
manipulating the image contrast and if it represents a considerable area
within the image. We avoid the inclusion of very thin structures that can be
confused with an image boundary artifact. This is the most challenging
structure to segment, requiring intensive clinical expert support.

Our fibroglandular tissue delineation is not limited to the segmentation of
dense regions; we also include scattered and fatty regions where we can find
agglomeration of ducts or glands. Thus, our structure represents a region of
interest for the fibroglandular tissue instead of an accurate selection of
tissues.

We consider fatty tissue every breast content that is outside the structures
defined previously. Its segmentation can be simplified by just delineating the
breast boundary, including a portion of the abdomen, and then subtracting the
other structure shapes automatically.

###  III-C Annotation tool

We implemented a sketch-based contour drawing annotation tool to delineate the
structures of interest. This tool allows the user to draw a closed contour for
a given structure and edit it if necessary, considering an intuitive
interaction for deformation. The user can also manipulate the image by
adjusting the standard window/level parameters to obtain a clearer
visualization and by zooming and translating it to focus on the target
structure. The contours are saved as dense high-resolution polygons defined in
the image space.

The annotation tool allows an automatic initialization of the breast contour
for the fatty tissue to speed up the annotation process. This breast contour
initialization is computed by segmenting the image using the Otsu binary
thresholding [42] and then computing the external contour of the largest
connected component.

For the other structures, the tool includes deep learning-based
initializations performed by two segmentation models trained on partial
versions of the annotated dataset. These models follow the same idea presented
in Section IV, considering each standard view as an independent domain.
Because these initializations are predictions, they should be edited to match
the appropriate boundaries.

###  III-D Annotation process

A team of 8 annotators was trained by two clinical experts for the
identification and delimitation of the structures of interest. These clinical
experts resolved the annotators’ doubts about challenging cases during the
entire annotation process. Unlike identifying abnormalities that require high
expertise, the structures of interest for our problem are relatively easy to
identify in most cases.

The raw data was split and assigned to the annotators, such that each image
was annotated by a single person. The entire annotation process was realized
in approximately one year, including multiple refinements based on the
feedback of the clinical experts.

At the end of the annotation process, the four structures of interest were
annotated over all the 5213 MLO images. The nipple, the fibroglandular tissue,
and the fatty tissue were annotated over all the 5137 CC view images. Because
the pectoral muscle is not always present in this view, this structure was
annotated on 2952 (57%) of these images.

###  III-E Data stratification

Following the standard data splitting process for machine learning methods and
considering independent sets for each view, we randomly divide the annotated
images into three disjoint subsets: train, validation, and test. This
splitting process avoids the inclusion of samples of the same examination in
different subsets and tries to balance the samples regarding the
fibroglandular tissue density. The density-based balancing is possible because
some of the examinations were previously categorized by clinical experts into
the four standard density classes: (A) almost entirely fatty, (B) scattered
fibroglandular tissue, (C) heterogeneously dense, and (D) extremely dense.
However, the majority of examinations do not present this density annotation.

The distribution of the subsets for each view is presented in Table I,
considering the density annotations. Notice how the test subset presents a
balanced behavior regarding the density classes, making it a good benchmark
for evaluation.

##  IV Mammography deep learning semantic segmentation

###  IV-A Overview

Figure 1 shows the mammography image segmentation pipeline proposed in this
paper. This pipeline focuses on the segmentation of the structures of interest
and the image background. The proposed solution employs a deep learning-based
approach using semantic segmentation neural networks. The semantic
segmentation process involves predicting labels for each pixel in the image,
assigning them to specific classes. This process establishes meaningful
regions within the image, associating each pixel with semantically relevant
information. In our solution, we consider five classes of region: background,
nipple, pectoral muscle, fibroglandular tissue, and fatty tissue. The deep
learning model consumes a pre-processed mammography image and generates a set
of probability maps that define the degree of correspondence of each pixel to
a given class.

Figure 1: Mammography deep learning segmentation

###  IV-B Evaluation metric

To assess the segmentation accuracy of the target structures in the resulting
trained models, we use the Intersection over Union (IoU) score, a widely-used
metric in segmentation tasks. The IoU measures the overlap between the model-
predicted structures and the ground-truth annotations. This score is
calculated by dividing the intersection area between the predicted and ground-
truth regions by the area of their union. This ratio yields a value between 0
and 1, where a score of 1 indicates a perfect overlap between the predicted
and ground-truth structures, while a score of 0 suggests no overlap at all.
Higher IoU scores indicate that the model’s predictions align more closely
with the desired target structures.

###  IV-C Pre-processing

To create ground-truth probability maps for each structure, we first rasterize
the annotated polygons into a single multi-class label map considering the
following operations: (1) fill the full label map with the background class
label. (2) rasterize the fatty tissue polygon on the previous label map, i.e.,
the breast contour polygon. (3) rasterize the fibroglandular tissue polygon on
the previous label map. (4) rasterize the pectoral muscle polygon on the
previous label map. (5) rasterize the nipple polygon on the previous label
map. Then, this label map is converted to the probability maps by using one-
hot encoding.

The mammography image is represented in the DICOM file as an integer grayscale
image with values in the range $[0,4095]$. To prepare this image for the deep-
learning model consumption, we apply the following operations: (1) image
normalization using the percentiles 2 and 98 as minimum and maximum values.
(2) adaptive histogram equalization using the algorithm Contrast Limited
Adaptive Histogram Equalization (CLAHE) [43] with kernel size $1/8$ times the
height and width of the image. (3) image intensity rescaling to the range
$[0,255]$.

To reduce the domain space, we flip all the left breast images and their
corresponding probability maps horizontally. Thus, all the images are treated
as right breast images.

###  IV-D Neural Network architectures results

Due to the distinct characteristics of MLO and CC views, we trained separate
models for each one. In our training experiments, we test several
architectures for semantic segmentation, including Unet, FPN, Linknet, and
PSPNet. While all these architectures serve the same purpose they employ
different strategies.

Based on extensive hyper-parameter optimization experiments, we selected the
following training configuration. The model input is a single-channel
$384\times 384$ image with intensity values in the range $[0,1]$. Thus, the
pre-processed image should be rescaled and resized to the corresponding range
and size. The model output is a tensor that represents the probability maps of
the structures of interest, including the background. We use softmax as the
activation function in the final layer to help the model to predict a single
class for each pixel, i.e., a multi-class problem. Further, because the
segmentation models allow the integration of a custom convolutional backbone,
we consider the EfficientnetB3 as the feature extractor.

During training, we use the Jaccard loss function with learning rate $10^{-3}$
and batch size $4$. We consider an early stopping scheme with 65 as the
maximum number of epochs and 20 as patience, selecting the best weights on the
validation dataset regarding the loss function.

Tables II and III present the mean IoU results per structure for the CC and
MLO view, respectively. From the tables, it is possible to observe good IoU
scores above 0.7 for all the segmented structures obtained from the test set.
In this mammography segmentation context, the PSPNet network presented the
worst metrics, while the FPN showed the best results, improving pectoral
muscle detection.

TABLE II: CC Mean IoU results on test set per segmented structure Architecture | Nipple |  | Pectoral
---
muscle
| Fibro
---
tissue
| Fatty
---
tissue
| Mean
---
Unet | 0,77 | 0.78 | 0.88 | 0.76 | 0.84
PSPNet | 0.75 | 0.73 | 0.88 | 0.76 | 0.83
Linknet | 0.76 | 0.75 | 0.90 | 0.81 | 0.84
FPN | 0.77 | 0.79 | 0.90 | 0.81 | 0.85
TABLE III: MLO Mean IoU results on test set per segmented structure Architecture | Nipple |  | Pectoral
---
muscle
| Fibro
---
tissue
| Fatty
---
tissue
| Mean
---
Unet | 0.70 | 0.95 | 0.90 | 0.80 | 0.87
PSPNet | 0.71 | 0.96 | 0.89 | 0.80 | 0.87
Linknet | 0.72 | 0.95 | 0.89 | 0.79 | 0.87
FPN | 0.73 | 0.96 | 0.91 | 0.82 | 0.88

###  IV-E Visual results

Figures 2 and 3 illustrate some visual results for the models’ segmentation
predictions. In both Figures, column a) refers to the preprocessed image,
column b) represents the ground-truth, and column c) the model prediction.
From the visual results, it’s possible to observe the effectiveness of the
model’s segmentation predictions. Comparing the ground truth with the model’s
predictions in column c), we can assess the model’s performance in accurately
delineating the structures of interest. These visual results offer insights
into the models’ ability to capture important features and accurately segment
the desired mammogram structures.

Despite the positive results, upon visual inspection, we observe that there is
still room for improvement. For instance, in the second row of Figure 2, there
is a false positive segmentation of the pectoral muscle on the CC view. From
the visual point of view, the pectoral structure is not always present in this
view, and when it is present, it could not be easy to spot even for human
specialists. This structure presents a high variability of cases that impact
this structure’s visualization. Sometimes it overlaps with the fibroglandular
tissue and does not always present a clear pattern like in the MLO view.
Another imprecision could be observed on some mammograms with flat or not-in-
profile nipples, such as the sample illustrated on the second row in Figure 3.

These observations highlight a potential area for refinement in the model’s
performance. While the overall results may be promising, identifying and
addressing such instances of inaccuracies can contribute to enhancing the
model’s segmentation accuracy and overall effectiveness.

Figure 2: CC model visual results: a) preprocessed image, b) ground-truth and
c) model prediction  Figure 3: MLO model visual results: a) preprocessed
image, b) ground-truth and c) model prediction

##  V Conclusion and future work

We presented a deep learning-based solution for the segmentation of structures
of interest in mammography images. This solution includes a fully-annotated
dataset useful for training the models in a supervised manner. The
segmentation results show that this method is at least promising for its
integration into clinical practice.

Although the results are encouraging, there is still a gap for improvement in
the estimation of more accurate segments, especially for the challenging
structures. Further, this idea can be extended for detecting and segmenting
other structures, such as skin folds, vessels, abnormalities, dense
fibroglandular tissue regions, and image artifacts, among others.

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