Towards Automated Semantic Segmentation in Mammography Images for Enhanced Clinical Applications.
Mammography images are widely used to detect non-palpable breast lesions or nodules, aiding in cancer prevention and enabling timely intervention when necessary. To support medical analysis, computer-aided detection systems can automate the segmentation of landmark structures, which is helpful in lo...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2260 - 2281 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278988&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278988 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278988 187278988 187278988 10.1007/s10278-024-01364-8 187278988 ppf: 2260 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Towards Automated Semantic Segmentation in Mammography Images for Enhanced Clinical Applications. aug: au: Sierra-Franco, Cesar A. Hurtado, Jan de A. Thomaz, Victor da Cruz, Leonardo C. Silva, Santiago V. Silva-Calpa, Greis Francy M. Raposo, Alberto affil: https://ror.org/01dg47b60 Tecgraf Institute and Department of Informatics, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil sug: subj: Mammography Utilization Breast Neoplasms Diagnosis Image Processing, Computer Assisted Utilization Diagnostic Imaging Methods Deep Learning Methods Nipples Radiography Pectoralis Muscles Radiography Adipose Tissue Radiography Breast Tissue Density Radiography Human Female Diagnosis, Computer Assisted Retrospective Design Hospitals Data Curation Female ab: Mammography images are widely used to detect non-palpable breast lesions or nodules, aiding in cancer prevention and enabling timely intervention when necessary. To support medical analysis, computer-aided detection systems can automate the segmentation of landmark structures, which is helpful in locating abnormalities and evaluating image acquisition adequacy. This paper presents a deep learning-based framework for segmenting the nipple, the pectoral muscle, the fibroglandular tissue, and the fatty tissue in standard-view mammography images. To the best of our knowledge, we introduce the largest dataset dedicated to mammography segmentation of key anatomical structures, specifically designed to train deep learning models for this task. Through comprehensive experiments, we evaluated various deep learning model architectures and training configurations, demonstrating robust segmentation performance across diverse and challenging cases. These results underscore the framework's potential for clinical integration. In our experiments, four semantic segmentation architectures were compared, all showing suitability for the target problem, thereby offering flexibility in model selection. Beyond segmentation, we introduce a suite of applications derived from this framework to assist in clinical assessments. These include automating tasks such as multi-view lesion registration and anatomical position estimation, evaluating image acquisition quality, measuring breast density, and enhancing visualization of breast tissues, thus addressing critical needs in breast cancer screening and diagnosis. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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