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Ma Y, Peng Y. Mammogram mass segmentation and classification based on cross-view VAE and spatial hidden factor disentanglement. Phys Eng Sci Med 2024; 47:223-238. [PMID: 38150059 DOI: 10.1007/s13246-023-01359-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 11/19/2023] [Indexed: 12/28/2023]
Abstract
Breast masses are the most important clinical findings of breast carcinomas. The mass segmentation and classification in mammograms remain a crucial yet challenging topic in computer-aided diagnosis systems, as the masses show their irregularities in shape, size and texture. In this paper, we propose a new framework for mammogram mass classification and segmentation. Specifically, to utilize the complementary information within the mammographic cross-views, cranio caudal and mediolateral oblique, a cross-view based variational autoencoder (CV-VAE) combined with a spatial hidden factor disentanglement module is presented, where the two views can be reconstructed from each other through two explicitly disentangled hidden factors: class related (specified) and background common (unspecified). Then, the specified factor is not only divided into two categories: benign and malignant by a new introduced feature pyramid networks based mass classifier, but also used to predict the mass mask label based on a U-Net-like decoder. By integrating the two complementary modules, more discriminative morphological and semantic features can be learned to solve the mass classification and segmentation problems simultaneously. The proposed method is evaluated on two most used public mammography datasets, CBIS-DDSM and INbreast, achieving the Dice similarity coefficient (DSC) of 92.46% and 93.70% for segmentation and the area under receiver operating characteristic curve (AUC) of 93.20% and 95.01% for classification, respectively. Compared with other state-of-the-art approaches, it gives competitive results.
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Affiliation(s)
- Yingran Ma
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, CO, China
| | - Yanjun Peng
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, CO, China.
- Shandong Province Key Laboratory of Wisdom Mining Information Technology, Shandong University of Science and Technology, Qingdao, 266590, CO, China.
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Qureshi I, Yan J, Abbas Q, Shaheed K, Riaz AB, Wahid A, Khan MWJ, Szczuko P. Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends. INFORMATION FUSION 2023. [DOI: 10.1016/j.inffus.2022.09.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
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Mújica-Vargas D, Matuz-Cruz M, García-Aquino C, Ramos-Palencia C. Efficient System for Delimitation of Benign and Malignant Breast Masses. ENTROPY (BASEL, SWITZERLAND) 2022; 24:e24121775. [PMID: 36554180 PMCID: PMC9777637 DOI: 10.3390/e24121775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 11/23/2022] [Accepted: 11/26/2022] [Indexed: 06/01/2023]
Abstract
In this study, a high-performing scheme is introduced to delimit benign and malignant masses in breast ultrasound images. The proposal is built upon by the Nonlocal Means filter for image quality improvement, an Intuitionistic Fuzzy C-Means local clustering algorithm for superpixel generation with high adherence to the edges, and the DBSCAN algorithm for the global clustering of those superpixels in order to delimit masses' regions. The empirical study was performed using two datasets, both with benign and malignant breast tumors. The quantitative results with respect to the BUSI dataset were JSC≥0.907, DM≥0.913, HD≥7.025, and MCR≤6.431 for benign masses and JSC≥0.897, DM≥0.900, HD≥8.666, and MCR≤8.016 for malignant ones, while the MID dataset resulted in JSC≥0.890, DM≥0.905, HD≥8.370, and MCR≤7.241 along with JSC≥0.881, DM≥0.898, HD≥8.865, and MCR≤7.808 for benign and malignant masses, respectively. These numerical results revealed that our proposal outperformed all the evaluated comparative state-of-the-art methods in mass delimitation. This is confirmed by the visual results since the segmented regions had a better edge delimitation.
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Affiliation(s)
- Dante Mújica-Vargas
- Departamento de Ciencias Computacionales, Tecnológico Nacional de México, Centro Nacional de Investigación y Desarrollo Tecnológico, Cuernavaca 62490, Morelos, Mexico
| | - Manuel Matuz-Cruz
- Tecnológico Nacional de México, Instituto Tecnológico de Tapachula, Tapachula 30700, Chiapas, Mexico
| | - Christian García-Aquino
- Departamento de Ciencias Computacionales, Tecnológico Nacional de México, Centro Nacional de Investigación y Desarrollo Tecnológico, Cuernavaca 62490, Morelos, Mexico
| | - Celia Ramos-Palencia
- Departamento de Ciencias Computacionales, Tecnológico Nacional de México, Centro Nacional de Investigación y Desarrollo Tecnológico, Cuernavaca 62490, Morelos, Mexico
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Yu X, Wang SH, Zhang YD. Multiple-level thresholding for breast mass detection. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022; 35:115-130. [DOI: 10.1016/j.jksuci.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Su Y, Liu Q, Xie W, Hu P. YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106903. [PMID: 35636358 DOI: 10.1016/j.cmpb.2022.106903] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 05/15/2022] [Accepted: 05/22/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Both mass detection and segmentation in digital mammograms play a crucial role in early breast cancer detection and treatment. Furthermore, clinical experience has shown that they are the upstream tasks of pathological classification of breast lesions. Recent advancements in deep learning have made the analyses faster and more accurate. This study aims to develop a deep learning model architecture for breast cancer mass detection and segmentation using the mammography. METHODS In this work we proposed a double shot model for mass detection and segmentation simultaneously using a combination of YOLO (You Only Look Once) and LOGO (Local-Global) architectures. Firstly, we adopted YoloV5L6, the state-of-the-art object detection model, to position and crop the breast mass in mammograms with a high resolution; Secondly, to balance training efficiency and segmentation performance, we modified the LOGO training strategy to train the whole images and cropped images on the global and local transformer branches separately. The two branches were then merged to form the final segmentation decision. RESULTS The proposed YOLO-LOGO model was tested on two independent mammography datasets (CBIS-DDSM and INBreast). The proposed model performs significantly better than previous works. It achieves true positive rate 95.7% and mean average precision 65.0% for mass detection on CBIS-DDSM dataset. Its performance for mass segmentation on CBIS-DDSM dataset is F1-score=74.5% and IoU=64.0%. The similar performance trend is observed in another independent dataset INBreast as well. CONCLUSIONS The proposed model has a higher efficiency and better performance, reduces computational requirements, and improves the versatility and accuracy of computer-aided breast cancer diagnosis. Hence it has the potential to enable more assistance for doctors in early breast cancer detection and treatment, thereby reducing mortality.
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Affiliation(s)
- Yongye Su
- Department of Biochemistry and Medical Genetics, University of Manitoba, Room 308-Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg, Manitoba R3E 0J9, Canada
| | - Qian Liu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Room 308-Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg, Manitoba R3E 0J9, Canada; Department of Computer Science, University of Manitoba, Winnipeg, Canada
| | - Wentao Xie
- Department of Biochemistry and Medical Genetics, University of Manitoba, Room 308-Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg, Manitoba R3E 0J9, Canada
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Room 308-Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg, Manitoba R3E 0J9, Canada; Department of Computer Science, University of Manitoba, Winnipeg, Canada; CancerCare Manitoba Research Institute, CancerCare Manitoba, Winnipeg, Canada.
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Yan Y, Conze PH, Lamard M, Quellec G, Cochener B, Coatrieux G. Towards improved breast mass detection using dual-view mammogram matching. Med Image Anal 2021; 71:102083. [PMID: 33979759 DOI: 10.1016/j.media.2021.102083] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 02/18/2021] [Accepted: 04/14/2021] [Indexed: 11/18/2022]
Abstract
Breast cancer screening benefits from the visual analysis of multiple views of routine mammograms. As for clinical practice, computer-aided diagnosis (CAD) systems could be enhanced by integrating multi-view information. In this work, we propose a new multi-tasking framework that combines craniocaudal (CC) and mediolateral-oblique (MLO) mammograms for automatic breast mass detection. Rather than addressing mass recognition only, we exploit multi-tasking properties of deep networks to jointly learn mass matching and classification, towards better detection performance. Specifically, we propose a unified Siamese network that combines patch-level mass/non-mass classification and dual-view mass matching to take full advantage of multi-view information. This model is exploited in a full image detection pipeline based on You-Only-Look-Once (YOLO) region proposals. We carry out exhaustive experiments to highlight the contribution of dual-view matching for both patch-level classification and examination-level detection scenarios. Results demonstrate that mass matching highly improves the full-pipeline detection performance by outperforming conventional single-task schemes with 94.78% as Area Under the Curve (AUC) score and a classification accuracy of 0.8791. Interestingly, mass classification also improves the performance of mass matching, which proves the complementarity of both tasks. Our method further guides clinicians by providing accurate dual-view mass correspondences, which suggests that it could act as a relevant second opinion for mammogram interpretation and breast cancer diagnosis.
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Affiliation(s)
- Yutong Yan
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; Université de Bretagne Occidentale, 3 rue des Archives, Brest 29238, France; IMT Atlantique, Technopôle Brest-Iroise, Brest 29238, France
| | - Pierre-Henri Conze
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; IMT Atlantique, Technopôle Brest-Iroise, Brest 29238, France.
| | - Mathieu Lamard
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; Université de Bretagne Occidentale, 3 rue des Archives, Brest 29238, France
| | - Gwenolé Quellec
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France
| | - Béatrice Cochener
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; Université de Bretagne Occidentale, 3 rue des Archives, Brest 29238, France; CHRU de Brest, 2 avenue Foch, Brest 29200, France
| | - Gouenou Coatrieux
- Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; IMT Atlantique, Technopôle Brest-Iroise, Brest 29238, France
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