{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T21:06:12Z","timestamp":1791666372009,"version":"4.3.4"},"reference-count":75,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T00:00:00Z","timestamp":1775952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Universidad San Francisco de Quito USFQ for financial support through the Poli-Grants Program","award":["17981"],"award-info":[{"award-number":["17981"]}]},{"name":"the Universidad San Francisco de Quito USFQ for financial support through the Poli-Grants Program","award":["19031"],"award-info":[{"award-number":["19031"]}]},{"name":"the Universidad San Francisco de Quito USFQ for financial support through the Poli-Grants Program","award":["24276"],"award-info":[{"award-number":["24276"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>This work introduces the MassSeg-Framework, a fully automatic two-stage pipeline for breast mass analysis in mammography that integrates YOLOv11-based detection with Chan\u2013Vese ACM refinement to achieve accurate mass localization and segmentation with a lightweight computational footprint. The framework was trained and evaluated on two publicly available datasets using consistent experimental protocols. In the detection stage, YOLOv11-nano was the most effective architecture, with a confidence threshold of 0.4, achieving statistically significant mAP50 values of 0.862 and 0.709 on the dINbreast and dCBIS datasets, respectively. These results confirm that a moderate threshold preserves clinically relevant true-positive candidates, which is particularly important for screening-oriented settings where missed lesions are costly. In the segmentation stage, the proposed framework achieved mean DICE scores of 0.721 and 0.700 on the test sets of the same datasets, demonstrating consistent overlap with expert annotations. Compared with state-of-the-art approaches that commonly assume lesion-centered ROIs or rely on heavier backbones, the proposed pipeline addresses a more realistic scenario by performing automatic detection followed by segmentation while maintaining substantially lower computational requirements. This balance between performance and efficiency makes the MassSeg-Framework a promising tool for scalable mammography analysis, particularly in resource-constrained environments or high-throughput screening workflows that require rapid processing.<\/jats:p>","DOI":"10.3390\/life16040653","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T11:38:26Z","timestamp":1776080306000},"page":"653","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MassSeg-Framework: A Breast Mass Detection and Segmentation Framework Based on Deep Learning and an Active Contour Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3536-1418","authenticated-orcid":false,"given":"Camila","family":"Zambrano","sequence":"first","affiliation":[{"name":"Colegio de Ciencias e Ingenier\u00edas \u201cEl Polit\u00e9cnico\u201d, Universidad San Francisco de Quito USFQ, Quito 170157, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3166-745X","authenticated-orcid":false,"given":"Noel","family":"P\u00e9rez-P\u00e9rez","sequence":"additional","affiliation":[{"name":"Colegio de Ciencias e Ingenier\u00edas \u201cEl Polit\u00e9cnico\u201d, Universidad San Francisco de Quito USFQ, Quito 170157, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7501-6523","authenticated-orcid":false,"given":"Miguel","family":"Coimbra","sequence":"additional","affiliation":[{"name":"INESC TEC, Faculdade de Ci\u00eancias da Universidade do Porto, 4169-007 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9379-8151","authenticated-orcid":false,"given":"Maria","family":"Baldeon-Calisto","sequence":"additional","affiliation":[{"name":"Colegio de Ciencias e Ingenier\u00edas \u201cEl Polit\u00e9cnico\u201d, Universidad San Francisco de Quito USFQ, Quito 170157, Ecuador"},{"name":"Porter B. Byrum School of Business, Wingate University, Wingate, NC 28174, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7785-7761","authenticated-orcid":false,"given":"Ricardo","family":"Flores-Moyano","sequence":"additional","affiliation":[{"name":"Colegio de Ciencias e Ingenier\u00edas \u201cEl Polit\u00e9cnico\u201d, Universidad San Francisco de Quito USFQ, Quito 170157, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6128-9504","authenticated-orcid":false,"given":"Jos\u00e9 Ram\u00f3n","family":"Mora","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Qu\u00edmica, Instituto de Simulaci\u00f3n Computacional (ISC-USFQ), Universidad San Francisco de Quito USFQ, Quito 170157, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8827-5938","authenticated-orcid":false,"given":"Oscar","family":"Camacho","sequence":"additional","affiliation":[{"name":"Colegio de Ciencias e Ingenier\u00edas \u201cEl Polit\u00e9cnico\u201d, Universidad San Francisco de Quito USFQ, Quito 170157, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6219-067X","authenticated-orcid":false,"given":"Diego","family":"Ben\u00edtez","sequence":"additional","affiliation":[{"name":"Colegio de Ciencias e Ingenier\u00edas \u201cEl Polit\u00e9cnico\u201d, Universidad San Francisco de Quito USFQ, Quito 170157, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,12]]},"reference":[{"key":"ref_1","unstructured":"National Institutes of Health (US) (2007). 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