{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T11:23:42Z","timestamp":1768217022645,"version":"3.49.0"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Rectal cancer is a globally prevalent cancer, and accurate segmentation of rectal lesions in abdominal CT images is critical for clinical diagnosis and treatment planning. Existing methods struggle with imprecise boundary delineation due to low tissue contrast, image noise, and varied lesion sizes, prompting the development of a specialized segmentation framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed the Hierarchical Hypercolumn-guided Fusion Segment Anything Model (HHF-SAM) with three core components: 1) A Med-Adapter SAM Encoder integrating LoRA and Adapter modules to adapt SAM's natural image understanding capability to medical-specific features; 2) A Multi-scale Hypercolumn Processing Module to capture comprehensive features for lesions of varying sizes and shapes; 3) A Progressive Hierarchical Fusion Decoder with Hierarchical Fusion Module to aggregate multi-scale features and resolve boundary blurring. The model was evaluated on two public abdominal CT datasets (CARE and WORD) using mean Dice coefficient (mDice) and mean Intersection over Union (mIoU) as metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On the CARE dataset, HHF-SAM achieved a mean mDice of 74.05% and mean mIoU of 58.96%, outperforming state-of-the-art methods (U-SAM: 69.28% mDice, 53.11% mIoU; SAM: 65.98% mDice, 49.44% mIoU). For tumor segmentation specifically, it reached 76.42% mDice and 62.03% mIoU. On the WORD dataset, it achieved an average mDice of 85.84% across all organs, with 83.24% mDice for rectal segmentation (surpassing U-SAM's 80.66% and SAM's 72.77%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This study presents an SAM-based framework optimized for the unique characteristics of abdominal CT images, effectively overcoming the limitations of general segmentation models in medical image processing. The proposed HHF-SAM provides a reliable tool for clinical auxiliary diagnosis, reducing inter-reader variability and improving efficiency in lesion delineation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2025.1696984","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:20:24Z","timestamp":1768206024000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Rectal cancer segmentation via HHF-SAM: a hierarchical hypercolumn-guided fusion segment anything model"],"prefix":"10.3389","volume":"8","author":[{"given":"Ye","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhoushan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Congcong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"014006","DOI":"10.1117\/1.JMI.6.1.014006","article-title":"Recurrent residual u-net for medical image segmentation","volume":"6","author":"Alom","year":"2019","journal-title":"J. Med. Imaging"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1528","DOI":"10.6004\/jnccn.2012.0158","article-title":"Rectal cancer","volume":"10","author":"Benson","year":"2012","journal-title":"J. Natl. Compr. Canc. Netw"},{"key":"B3","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1038\/s41698-024-00516-x","article-title":"An automated deep learning pipeline for EMVI classification and response prediction of rectal cancer using baseline MRI: a multi-centre study","volume":"8","author":"Cai","year":"2024","journal-title":"npj Precis. Oncol"},{"key":"B4","first-page":"205","article-title":"\u201cSwin-unet: unet-like pure transformer for medical image segmentation,\u201d","volume-title":"European Conference on Computer Vision","author":"Cao","year":"2022"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2102.04306","article-title":"Transunet: transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021","journal-title":"arXiv [preprint]"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv:2304.09148","article-title":"Sam fails to segment anything?-sam-adapter: adapting sam in underperformed scenes: camouflage, shadow, medical image segmentation, and more","author":"Chen","year":"2023","journal-title":"arXiv [preprint]"},{"key":"B7","first-page":"3511","article-title":"\u201cUnleashing the potential of sam for medical adaptation via hierarchical decoding,\u201d","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Cheng","year":"2024"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1676","DOI":"10.3109\/0284186X.2012.754989","article-title":"Clinical validation of atlas-based auto-segmentation of pelvic volumes and normal tissue in rectal tumors using auto-segmentation computed system","volume":"52","author":"Gambacorta","year":"2013","journal-title":"Acta Oncol"},{"key":"B9","doi-asserted-by":"publisher","first-page":"2763","DOI":"10.1109\/TMI.2023.3264513","article-title":"H2former: an efficient hierarchical hybrid transformer for medical image segmentation","volume":"42","author":"He","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B10","first-page":"2790","article-title":"\u201cParameter-efficient transfer learning for NLP,\u201d","volume-title":"International Conference on Machine Learning","author":"Houlsby","year":"2019"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv:2106.09685","article-title":"Lora: low-rank adaptation of large language models","author":"Hu","year":"2021","journal-title":"arXiv [preprint]"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1109\/TMI.2022.3230943","article-title":"Missformer: an effective transformer for 2D medical image segmentation","volume":"42","author":"Huang","year":"2022","journal-title":"IEEE Trans Med. Imaging"},{"key":"B13","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"Multiresunet: rethinking the u-net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw"},{"key":"B14","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"NNU-NET: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"B15","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1109\/ISM46123.2019.00049","article-title":"\u201cResunet++: an advanced architecture for medical image segmentation,\u201d","volume-title":"2019 IEEE International Symposium on Multimedia (ISM)","author":"Jha","year":"2019"},{"key":"B16","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1148\/radiol.09090587","article-title":"Rectal cancer: comparison of accuracy of local-regional staging with two-and three-dimensional preoperative 3-t mr imaging","volume":"254","author":"Kim","year":"2021","journal-title":"Radiology"},{"key":"B17","first-page":"4015","article-title":"\u201cSegment anything,\u201d","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Kirillov","year":"2023"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv:2401.00722","article-title":"Brau-net++: U-shaped hybrid CNN-transformer network for medical image segmentation","author":"Lan","year":"2024","journal-title":"arXiv [preprint]"},{"key":"B19","doi-asserted-by":"publisher","first-page":"9121","DOI":"10.3390\/app13169121","article-title":"A fuzzy transformer fusion network (fuzzytransnet) for medical image segmentation: the case of rectal polyps and skin lesions","volume":"13","author":"Liu","year":"2023","journal-title":"Appl. Sci"},{"key":"B20","doi-asserted-by":"publisher","first-page":"102642","DOI":"10.1016\/j.media.2022.102642","article-title":"Word: a large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image","volume":"82","author":"Luo","year":"2021","journal-title":"Med. Image Anal"},{"key":"B21","first-page":"108","article-title":"\u201cZero-shot performance of the segment anything model (SAM) in 2D medical imaging: a comprehensive evaluation and practical guidelines,\u201d","volume-title":"2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering","author":"Mattjie","year":"2023"},{"key":"B22","doi-asserted-by":"publisher","first-page":"6571","DOI":"10.1007\/s11042-024-19229-1","article-title":"MSBC-net: automatic rectal cancer segmentation from MR scans","volume":"84","author":"Meng","year":"2025","journal-title":"Multimed. Tools Appl"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv:2212.09263","article-title":"Focal-unet: Unet-like focal modulation for medical image segmentation","author":"Naderi","year":"2022","journal-title":"arXiv [preprint]"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.4550\/arXiv:1804.03999","article-title":"Attention U-net: learning where to look for the pancreas","author":"Oktay","year":"2018","journal-title":"arXiv [preprint]"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv:2408.06447","article-title":"S-SAM: SVD-based fine-tuning of segment anything model for medical image segmentation","author":"Paranjape","year":"2024","journal-title":"arXiv [preprint]"},{"key":"B26","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/978-3-030-87589-3_28","article-title":"\u201cU-net transformer: self and cross attention for medical image segmentation,\u201d","author":"Petit","year":"2021","journal-title":"\u201cMachine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 12"},{"key":"B27","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1155\/2015\/514740","article-title":"Mri for assessing response to neoadjuvant therapy in locally advanced rectal cancer using dce-mr and dw-mr data sets: a preliminary report","volume":"2015","author":"Petrillo","year":"2015","journal-title":"BioMed Res. Int"},{"key":"B28","first-page":"234","article-title":"\u201cU-net: convolutional networks for biomedical image segmentation,\u201d","volume-title":"Medical Image Computing and comPuter-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Vol. 18","author":"Ronneberger","year":"2015"},{"key":"B29","doi-asserted-by":"publisher","first-page":"4812","DOI":"10.1109\/ACCESS.2024.3349409","article-title":"FCTFORMER: fusing convolutional operations and transformer for 3D rectal tumor segmentation in MR images","volume":"12","author":"Sang","year":"2024","journal-title":"IEEE Access"},{"key":"B30","doi-asserted-by":"publisher","first-page":"113","DOI":"10.3389\/fonc.2023.1172424","article-title":"Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the flex U-net network","volume":"13","author":"Sha","year":"2023","journal-title":"Front. Oncol"},{"key":"B31","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.3390\/diagnostics13111947","article-title":"Generalist vision foundation models for medical imaging: a case study of segment anything model on zero-shot medical segmentation","volume":"13","author":"Shi","year":"2023","journal-title":"Diagnostics"},{"key":"B32","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1016\/j.amjsurg.2014.09.024","article-title":"Simultaneous resection for rectal cancer with synchronous liver metastasis is a safe procedure","volume":"209","author":"Silberhumer","year":"2015","journal-title":"Am. J. Surg"},{"key":"B33","doi-asserted-by":"publisher","first-page":"1398237","DOI":"10.3389\/fbioe.2024.1398237","article-title":"DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentation","volume":"12","author":"Sun","year":"2024","journal-title":"Front. Bioeng. Biotechnol"},{"key":"B34","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1007\/s11517-023-02799-x","article-title":"Colorectal cancer lymph node metastasis prediction with weakly supervised transformer-based multi-instance learning","volume":"61","author":"Tan","year":"2023","journal-title":"Med. Biol. Eng. Comput"},{"key":"B35","article-title":"\u201cSAM.MD: zero-shot medical image segmentation capabilities of the segment anything mode,\u201d","volume-title":"Imaging with Deep Learning","author":"Wald","year":"2023"},{"key":"B36","article-title":"Tuning vision foundation models for rectal cancer segmentation from CT scans: development and validation of U-sam","author":"Wan","year":"2024","journal-title":"arXiv [Preprint]"},{"key":"B37","doi-asserted-by":"publisher","first-page":"2441","DOI":"10.1609\/aaai.v36i3.20144","article-title":"Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer","volume":"36","author":"Wang","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2304.12620","article-title":"Medical sam adapter: adapting segment anything model for medical image segmentation","author":"Wu","year":"2023","journal-title":"arXiv [preprint]"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00322","article-title":"SAM fewshot finetuning for anatomical segmentation in medical images","author":"Xie","year":"2024","journal-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision"},{"key":"B40","first-page":"7975","article-title":"\u201cAfter-SAM: adapting sam with axial fusion transformer for medical imaging segmentation,\u201d","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","author":"Yan","year":"2024"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2304.13785","article-title":"\u201cCustomized segment anything model for medical image segmentation","author":"Zhang","year":"2023","journal-title":"arXiv [preprint]"},{"key":"B42","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1186\/s12880-024-01269-6","article-title":"Imaging segmentation mechanism for rectal tumors using improved u-net","volume":"24","author":"Zhang","year":"2024","journal-title":"BMC Med. Imaging"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1696984\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:20:32Z","timestamp":1768206032000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2025.1696984\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,12]]},"references-count":42,"alternative-id":["10.3389\/frai.2025.1696984"],"URL":"https:\/\/doi.org\/10.3389\/frai.2025.1696984","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,12]]},"article-number":"1696984"}}