Fully Automated Breast Density Segmentation and Classification Using Deep Learning

Breast density estimation with visual evaluation is still challenging due to low contrast and significant fluctuations in the mammograms’ fatty tissue background. The primary key to breast density classification is to detect the dense tissues in the mammographic images correctly. Many methods have been proposed for breast density estimation; nevertheless, most of them are not fully automated. Besides, they have been badly affected by low signal-to-noise ratio and variability of density in appearance and texture. This study intends to develop a fully automated and digitalized breast tissue segmentation and classification using advanced deep learning techniques. The conditional Generative Adversarial Networks (cGAN) network is applied to segment the dense tissues in mammograms. To have a complete system for breast density classification, we propose a Convolutional Neural Network (CNN) to classify mammograms based on the standardization of Breast Imaging-Reporting and Data System (BI-RADS). The classification network is fed by the segmented masks of dense tissues generated by the cGAN network. For screening mammography, 410 images of 115 patients from the INbreast dataset were used. The proposed framework can segment the dense regions with an accuracy, Dice coefficient, Jaccard index of 98%, 88%, and 78%, respectively. Furthermore, we obtained precision, sensitivity, and specificity of 97.85%, 97.85%, and 99.28%, respectively, for breast density classification. This study’s findings are promising and show that the proposed deep learning-based techniques can produce a clinically useful computer-aided tool for breast density analysis by digital mammography.

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Quantitative Measurements of Breast Density Using Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis

Journal of Clinical Medicine ◽

10.3390/jcm8050745 ◽

2019 ◽

Vol 8 (5) ◽

pp. 745 ◽

Cited By ~ 1

Author(s):

Rooa Sindi ◽

Cláudia Sá Dos Reis ◽

Colleen Bennett ◽

Gil Stevenson ◽

Zhonghua Sun

Keyword(s):

Systematic Review ◽

Cluster Analysis ◽

Breast Density ◽

Breast Imaging ◽

Quantitative Assessment ◽

Nearest Neighbor ◽

Meta Analysis ◽

Fatty Tissue ◽

Single Linkage ◽

Meta Analyses

Breast density, a measure of dense fibroglandular tissue relative to non-dense fatty tissue, is confirmed as an independent risk factor of breast cancer. Although there has been an increasing interest in the quantitative assessment of breast density, no research has investigated the optimal technical approach of breast MRI in this aspect. Therefore, we performed a systematic review and meta-analysis to analyze the current studies on quantitative assessment of breast density using MRI and to determine the most appropriate technical/operational protocol. Databases (PubMed, EMBASE, ScienceDirect, and Web of Science) were searched systematically for eligible studies. Single arm meta-analysis was conducted to determine quantitative values of MRI in breast density assessments. Combined means with their 95% confidence interval (CI) were calculated using a fixed-effect model. In addition, subgroup meta-analyses were performed with stratification by breast density segmentation/measurement method. Furthermore, alternative groupings based on statistical similarities were identified via a cluster analysis employing study means and standard deviations in a Nearest Neighbor/Single Linkage. A total of 38 studies matched the inclusion criteria for this systematic review. Twenty-one of these studies were judged to be eligible for meta-analysis. The results indicated, generally, high levels of heterogeneity between study means within groups and high levels of heterogeneity between study variances within groups. The studies in two main clusters identified by the cluster analysis were also subjected to meta-analyses. The review confirmed high levels of heterogeneity within the breast density studies, considered to be due mainly to the applications of MR breast-imaging protocols and the use of breast density segmentation/measurement methods. Further research should be performed to determine the most appropriate protocol and method for quantifying breast density using MRI.

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A deep learning method for volumetric breast density estimation from processed full field digital mammograms

Medical Imaging 2019: Computer-Aided Diagnosis ◽

10.1117/12.2512818 ◽

2019 ◽

Author(s):

Michiel Kallenberg ◽

Doiriel Vanegas Camargo ◽

Mahlet Birhanu ◽

Albert Gubern-Mérida ◽

Nico Karssemeijer

Keyword(s):

Deep Learning ◽

Density Estimation ◽

Breast Density ◽

Learning Method ◽

Full Field

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Sensitivity of screening mammography by density and texture: a cohort study from a population-based screening program in Denmark

Breast Cancer Research ◽

10.1186/s13058-019-1203-3 ◽

2019 ◽

Vol 21 (1) ◽

Cited By ~ 6

Author(s):

My von Euler-Chelpin ◽

Martin Lillholm ◽

Ilse Vejborg ◽

Mads Nielsen ◽

Elsebeth Lynge

Keyword(s):

Breast Density ◽

Breast Imaging ◽

Breast Tissue ◽

Screening Program ◽

Population Based ◽

Screening Mammography ◽

Dense Breast Tissue ◽

Density Score ◽

Screening Sensitivity ◽

Low Sensitivity

Abstract Background Screening mammography works better in fatty than in dense breast tissue. Computerized assessment of parenchymal texture is a non-subjective method to obtain a refined description of breast tissue, potentially valuable in addition to breast density scoring for the identification of women in need of supplementary imaging. We studied the sensitivity of screening mammography by a combination of radiologist-assessed Breast Imaging Reporting and Data System (BI-RADS) density score and computer-assessed parenchymal texture marker, mammography texture resemblance (MTR), in a population-based screening program. Methods Breast density was coded according to the fourth edition of the BI-RADS density code, and MTR marker was divided into quartiles from 1 to 4. Screening data were followed up for the identification of screen-detected and interval cancers. We calculated sensitivity and specificity with 95% confidence intervals (CI) by BI-RADS density score, MTR marker, and combination hereof. Results Density and texture were strongly correlated, but the combination led to the identification of subgroups with different sensitivity. Sensitivity was high, about 80%, in women with BI-RADS density score 1 and MTR markers 1 or 2. Sensitivity was low, 67%, in women with BI-RADS density score 2 and MTR marker 4. For women with BI-RADS density scores 3 and 4, the already low sensitivity was further decreased for women with MTR marker 4. Specificity was 97–99% in all subgroups. Conclusion Our study showed that women with low density constituted a heterogenous group. Classifying women for extra imaging based on density only might be a too crude approach. Screening sensitivity was systematically high in women with fatty and homogenous breast tissue.

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Connected-UNets: a deep learning architecture for breast mass segmentation

npj Breast Cancer ◽

10.1038/s41523-021-00358-x ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Asma Baccouche ◽

Begonya Garcia-Zapirain ◽

Cristian Castillo Olea ◽

Adel S. Elmaghraby

Keyword(s):

Deep Learning ◽

Network Architecture ◽

Breast Imaging ◽

Rapid Development ◽

Synthetic Data ◽

Screening Mammography ◽

Medical Image Segmentation ◽

Breast Mass ◽

Generative Adversarial Network ◽

Mass Segmentation

AbstractBreast cancer analysis implies that radiologists inspect mammograms to detect suspicious breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic systems for breast mass segmentation to assist radiologists in their diagnosis. With the rapid development of deep learning and its application to medical imaging challenges, UNet and its variations is one of the state-of-the-art models for medical image segmentation that showed promising performance on mammography. In this paper, we propose an architecture, called Connected-UNets, which connects two UNets using additional modified skip connections. We integrate Atrous Spatial Pyramid Pooling (ASPP) in the two standard UNets to emphasize the contextual information within the encoder–decoder network architecture. We also apply the proposed architecture on the Attention UNet (AUNet) and the Residual UNet (ResUNet). We evaluated the proposed architectures on two publically available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Experiments were also conducted using additional synthetic data using the cycle-consistent Generative Adversarial Network (CycleGAN) model between two unpaired datasets to augment and enhance the images. Qualitative and quantitative results show that the proposed architecture can achieve better automatic mass segmentation with a high Dice score of 89.52%, 95.28%, and 95.88% and Intersection over Union (IoU) score of 80.02%, 91.03%, and 92.27%, respectively, on CBIS-DDSM, INbreast, and the private dataset.

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Volumetric breast density estimation on MRI using explainable deep learning regression

Scientific Reports ◽

10.1038/s41598-020-75167-6 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Bas H. M. van der Velden ◽

Markus H. A. Janse ◽

Max A. A. Ragusi ◽

Claudette E. Loo ◽

Kenneth G. A. Gilhuijs

Keyword(s):

Neural Network ◽

Density Estimation ◽

Breast Density ◽

Ground Truth ◽

Pectoral Muscle ◽

Fatty Tissue ◽

Limits Of Agreement ◽

Test Set ◽

3 Dimensional ◽

Spearman’S Correlation

Abstract To purpose of this paper was to assess the feasibility of volumetric breast density estimations on MRI without segmentations accompanied with an explainability step. A total of 615 patients with breast cancer were included for volumetric breast density estimation. A 3-dimensional regression convolutional neural network (CNN) was used to estimate the volumetric breast density. Patients were split in training (N = 400), validation (N = 50), and hold-out test set (N = 165). Hyperparameters were optimized using Neural Network Intelligence and augmentations consisted of translations and rotations. The estimated densities were evaluated to the ground truth using Spearman’s correlation and Bland–Altman plots. The output of the CNN was visually analyzed using SHapley Additive exPlanations (SHAP). Spearman’s correlation between estimated and ground truth density was ρ = 0.81 (N = 165, P < 0.001) in the hold-out test set. The estimated density had a median bias of 0.70% (95% limits of agreement = − 6.8% to 5.0%) to the ground truth. SHAP showed that in correct density estimations, the algorithm based its decision on fibroglandular and fatty tissue. In incorrect estimations, other structures such as the pectoral muscle or the heart were included. To conclude, it is feasible to automatically estimate volumetric breast density on MRI without segmentations, and to provide accompanying explanations.

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A weakly labeled approach for breast tissue segmentation and breast density estimation in digital mammography

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) ◽

10.1109/isbi.2016.7493368 ◽

2016 ◽

Cited By ~ 2

Author(s):

Rami Ben-Ari ◽

Aviad Zlotnick ◽

Sharbell Hashoul

Keyword(s):

Density Estimation ◽

Breast Density ◽

Digital Mammography ◽

Breast Tissue ◽

Tissue Segmentation

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Relationship between Mammographic Density and Age in the United Arab Emirates Population

Journal of Oncology ◽

10.1155/2019/7351350 ◽

2019 ◽

Vol 2019 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Young-Joon Kang ◽

Soo Kyung Ahn ◽

Seung Ja Kim ◽

Hyeyoung Oh ◽

Jaihong Han ◽

...

Keyword(s):

United Arab Emirates ◽

Breast Density ◽

Breast Imaging ◽

Screening Program ◽

Age Groups ◽

Significant Negative Correlation ◽

Geographic Area ◽

Screening Mammography ◽

Mammographic Breast Density ◽

The Relationship

Objective. Higher breast density is a strong, independent risk factor for breast cancer. Breast density varies by age, ethnicity, and geographic area although dense breast tissue has been associated with younger age and premenopausal status. The relationship between breast density and age in women in the United Arab Emirates (UAE) has not been determined. This study evaluated breast density in the UAE population and its relationship with age. Methods. Women participating in the national cancer screening program from August 2015 to May 2018 who underwent screening mammography were included. Breast parenchymal density was classified according to the American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) from category a (almost entirely fatty) through d (extremely dense). Subjects were divided into six age groups, and the association between age and breast density was evaluated. Results. Of the 4911 women included, 1604 (32.7%), 2149 (43.8%), 1055 (21.5%), and 103 (2.1%) were classified as having categories a–d breast density, respectively. A significant negative correlation was observed between age and breast density category (p<0.001). Women of mean age 44 ± 7 years had the highest breast density, whereas those of mean age 56 ± 14 years had the lowest breast density. Comparisons of Emirati women with Lebanese and Western women showed that breast density was lower in Emirati women than in the other populations. Conclusions. To our knowledge, this is the first study to evaluate the relationship between mammographic breast density and age in UAE women. As in other populations, age was inversely related to breast density, but the proportion of Emirati women with dense breasts was lower than in other populations. Because this study lacked demographic, clinical, and histopathological data, further evaluation is required.

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