{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T00:30:12Z","timestamp":1792801812086,"version":"4.5.0"},"reference-count":52,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T00:00:00Z","timestamp":1744588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MICIU\/AEI\/10.13039\/50100011033","award":["CPP2021-008491"],"award-info":[{"award-number":["CPP2021-008491"]}]},{"name":"European Union NextGeneration EU\/PRTR","award":["CPP2021-008491"],"award-info":[{"award-number":["CPP2021-008491"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Semantic segmentation of medical images holds significant potential for enhancing diagnostic and surgical procedures. Radiology specialists can benefit from automated segmentation tools that facilitate identifying and isolating regions of interest in medical scans. Nevertheless, to obtain precise results, sophisticated deep learning models tailored to this specific task must be developed and trained, a capability not universally accessible. Segment Anything Model (SAM) 2 is a foundational model designed for image and video segmentation tasks, built on its predecessor, SAM. This paper introduces a novel approach leveraging SAM 2\u2019s video segmentation capabilities to reduce the prompts required to segment an entire volume of medical images. The study first compares SAM and SAM 2\u2019s performance in medical image segmentation. Evaluation metrics such as the Jaccard index and Dice score are used to measure precision and segmentation quality. Then, our novel approach is introduced. Statistical tests include comparing precision gains and computational efficiency, focusing on the trade-off between resource use and segmentation time. The results show that SAM 2 achieves an average improvement of 1.76% in the Jaccard index and 1.49% in the Dice score compared to SAM, albeit with a ten-fold increase in segmentation time. Our novel approach to segmentation reduces the number of prompts needed to segment a volume of medical images by 99.95%. We demonstrate that it is possible to segment all the slices of a volume and, even more, of a whole dataset, with a single prompt, achieving results comparable to those obtained by state-of-the-art models explicitly trained for this task. Our approach simplifies the segmentation process, allowing specialists to devote more time to other tasks. The hardware and personnel requirements to obtain these results are much lower than those needed to train a deep learning model from scratch or to modify the behavior of an existing one using model modification techniques.<\/jats:p>","DOI":"10.3390\/a18040227","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T09:06:51Z","timestamp":1744621611000},"page":"227","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Prompt Once, Segment Everything: Leveraging SAM 2 Potential for Infinite Medical Image Segmentation with a Single Prompt"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1024-6202","authenticated-orcid":false,"given":"Juan D.","family":"Guti\u00e9rrez","sequence":"first","affiliation":[{"name":"Department of Electronics and Computer Science, Universidad de Santiago de Compostela, R\u00faa Benigno Ledo, 27002 Lugo, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9144-0698","authenticated-orcid":false,"given":"Emilio","family":"Delgado","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n en Tecnolog\u00edas Inform\u00e1ticas Aplicadas (INTIA), Universidad de Extremadura, Av. Universidad s\/n, 10003 C\u00e1ceres, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4507-9907","authenticated-orcid":false,"given":"Carlos","family":"Breuer","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n en Tecnolog\u00edas Inform\u00e1ticas Aplicadas (INTIA), Universidad de Extremadura, Av. Universidad s\/n, 10003 C\u00e1ceres, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2640-679X","authenticated-orcid":false,"given":"Jos\u00e9 M.","family":"Conejero","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n en Tecnolog\u00edas Inform\u00e1ticas Aplicadas (INTIA), Universidad de Extremadura, Av. Universidad s\/n, 10003 C\u00e1ceres, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6545-0913","authenticated-orcid":false,"given":"Roberto","family":"Rodriguez-Echeverria","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n en Tecnolog\u00edas Inform\u00e1ticas Aplicadas (INTIA), Universidad de Extremadura, Av. Universidad s\/n, 10003 C\u00e1ceres, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Szeliski, R. (2022). Computer Vision: Algorithms and Applications, Springer Nature.","DOI":"10.1007\/978-3-030-34372-9"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Nieto-del-Amor, F., Ye-Lin, Y., Monfort-Ortiz, R., Diago-Almela, V.J., Modrego-Pardo, F., Martinez-de-Juan, J.L., Hao, D., and Prats-Boluda, G. (2024). 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