{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T22:52:14Z","timestamp":1776120734757,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: A leading cause of emergency abdominal surgery, appendicitis is a common condition affecting millions of people worldwide. Automatic and accurate segmentation of the appendix from medical imaging is a challenging task, due to its small size, variability in shape, and proximity to other anatomical structures. Methods: In this study, we propose a backbone-enriched Mask R-CNN architecture (MaskAppendix) on the Detectron platform, enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM), for precise appendix segmentation on computed tomography (CT) scans. In the proposed MaskAppendix deep learning model, ResNet101 network is used as the backbone. By integrating Grad-CAM into the MaskAppendix network, our model improves feature localization, allowing it to better capture subtle variations in appendix morphology. Results: We conduct extensive experiments on a dataset of abdominal CT scans, demonstrating that our method achieves state-of-the-art performance in appendix segmentation, outperforming traditional segmentation techniques in terms of both accuracy and robustness. In the automatic segmentation of the appendix region in CT slices, a DSC score of 87.17% was achieved with the proposed approach, and the results obtained have the potential to improve clinical diagnostic accuracy. Conclusions: This framework provides an effective tool for aiding clinicians in the diagnosis of appendicitis and other related conditions, reducing the potential for diagnostic errors and enhancing clinical workflow efficiency.<\/jats:p>","DOI":"10.3390\/diagnostics14212346","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T04:10:14Z","timestamp":1729570214000},"page":"2346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6559-1399","authenticated-orcid":false,"given":"Emre","family":"Dand\u0131l","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Bilecik Seyh Edebali University, 11230 Bilecik, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7793-7887","authenticated-orcid":false,"given":"Bet\u00fcl Tiryaki","family":"Ba\u015ftu\u011f","sequence":"additional","affiliation":[{"name":"Radiology Department, Faculty of Medicine, Bilecik \u015eeyh Edebali University, 11230 Bilecik, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3998-1542","authenticated-orcid":false,"given":"Mehmet S\u00fcleyman","family":"Y\u0131ld\u0131r\u0131m","sequence":"additional","affiliation":[{"name":"Department of S\u00f6\u011f\u00fct Vocational School, Computer Technology, Bilecik \u015eeyh Edebali University, S\u00f6\u011f\u00fct, 11600 Bilecik, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7616-4798","authenticated-orcid":false,"given":"Kadir","family":"\u00c7orbac\u0131","sequence":"additional","affiliation":[{"name":"General Surgery Department, Bilecik Osmaneli Mustafa Selahattin \u00c7etinta\u015f Hospital, 11500 Bilecik, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6749-2159","authenticated-orcid":false,"given":"G\u00fcrkan","family":"G\u00fcneri","sequence":"additional","affiliation":[{"name":"General Surgery Department, Faculty of Medicine, Bilecik \u015eeyh Edebali University, 11230 Bilecik, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"key":"ref_1","first-page":"295","article-title":"Appendix and cecum: Embryology, anatomy, and surgical applications","volume":"80","author":"Schumpelick","year":"2000","journal-title":"Surg. 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