{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T14:05:54Z","timestamp":1789049154799,"version":"build-2803163510"},"reference-count":24,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Education of Guangdong Province Young Innovative Talents Program Project","award":["2025KQNCX237"],"award-info":[{"award-number":["2025KQNCX237"]}]},{"award":["2025KQNCX237"],"award-info":[{"award-number":["2025KQNCX237"]}],"id":[{"id":"https:\/\/ror.org\/01s5gyw91","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Three-dimensional medical image segmentation is critical for clinical diagnosis and treatment planning. Medical images typically exhibit heterogeneous characteristics of low inter-slice resolution and high in-plane resolution, leading 3D CNNs to lose fine edge details via cross-slice feature averaging and traditional 2D slice-based methods to produce discontinuous results due to neglected inter-slice spatial relationships. Multi-view learning shows potential in addressing these issues but lacks sample-adaptive dynamic consistency awareness, as conventional fixed-weight fusion cannot adaptively calibrate view discrepancies for heterogeneous medical data. To tackle this, we propose a dynamic consistency-aware multi-view framework enhanced with SAM3 for 3D medical image segmentation. The framework embeds pairwise cross-view similarity-driven dynamic gating into the multi-view pipeline: decomposing 3D images into three orthogonal views, extracting fine-grained features with a frozen SAM3 encoder, and using a dynamic consistency-aware fusion module that computes pairwise view similarity to dynamically calibrate resolution-induced discrepancies, capture inter-slice and cross-view spatial dependencies, and reconstruct unified 3D features, which are then mapped to segmentation masks via a decoder. Experiments on Automated Cardiac Diagnosis Challenge (ACDC) and infrapatellar fat pad (IPFP) datasets demonstrate that our method achieves superior segmentation accuracy over state-of-the-art approaches, with promising technical performance that supports further multicenter prospective clinical validation.<\/jats:p>","DOI":"10.3390\/s26185753","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T13:04:57Z","timestamp":1789045497000},"page":"5753","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Consistency-Aware Multi-View Learning with SAM3 for 3D Medical Image Segmentation"],"prefix":"10.3390","volume":"26","author":[{"given":"Xuan","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Sciences, Universiti Sains Malaysia, George Town 11600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaolong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106711","DOI":"10.1016\/j.bspc.2024.106711","article-title":"Unified semantic model for medical image segmentation","volume":"98","author":"Yuan","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"114277","DOI":"10.1016\/j.knosys.2025.114277","article-title":"CAFNet: Circular attention fusion for medical image segmentation","volume":"329","author":"Wu","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"122509","DOI":"10.1016\/j.eswa.2023.122509","article-title":"An evolutionary chameleon swarm algorithm based network for 3D medical image segmentation","volume":"239","author":"Rajesh","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"132165","DOI":"10.1016\/j.neucom.2025.132165","article-title":"TransCapsUNet: A transformer-capsule integrated U-Net for 3D volumetric medical image segmentation","volume":"666","author":"Vatani","year":"2026","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103293","DOI":"10.1016\/j.inffus.2025.103293","article-title":"Multi-scale dual-attention frequency fusion for joint segmentation and deformable medical image registration","volume":"123","author":"Zhou","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107412","DOI":"10.1016\/j.bspc.2024.107412","article-title":"A model use context complementarity feature fusion learning for semi-supervised 3D medical image segmentation","volume":"102","author":"Chen","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108086","DOI":"10.1016\/j.bspc.2025.108086","article-title":"SIT-SAM: A semantic-integration transformer that adapts the segment anything model to zero-shot medical image semantic segmentation","volume":"110","author":"Shi","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"114127","DOI":"10.1016\/j.knosys.2025.114127","article-title":"HA-U3Net: A modality-agnostic framework for 3D medical image segmentation using nested V-Net structure and hybrid attention","volume":"327","author":"Allaoui","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016). V-net: Fully convolutional neural networks for volumetric medical image segmentation. 2016 Fourth International Conference on 3D Vision (3DV), IEEE.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016). 3D U-Net: Learning dense volumetric segmentation from sparse annotation. arXiv.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_11","unstructured":"Chen, J.N., Mei, J.R., Li, X.H., Lu, Y., Yu, Q., Wei, Q., Luo, X., Xie, Y., Adeli, E., and Wang, Y. (2023). 3D TransUNet: Advancing medical image segmentation through vision transformers. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108988","DOI":"10.1016\/j.bspc.2025.108988","article-title":"WKPNet: A novel wavelet-KAN-POLA network for medical image segmentation","volume":"113","author":"Liang","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103300","DOI":"10.1016\/j.inffus.2025.103300","article-title":"CertainTTA: Estimating uncertainty for test-time adaptation on medical image segmentation","volume":"123","author":"Dong","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_14","first-page":"17386","article-title":"Multi-view evidential learning-based medical image segmentation","volume":"39","author":"Huang","year":"2025","journal-title":"AAAI Conf. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1109\/TMI.2025.3530097","article-title":"Learnable prompting SAM-induced knowledge distillation for semi-supervised medical image segmentation","volume":"44","author":"Huang","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s13748-025-00406-8","article-title":"MM-Net: Accurate tumor segmentation from medical images with lightweight hybrid transformers","volume":"15","author":"Wang","year":"2026","journal-title":"Prog. Artif. Intell."},{"key":"ref_17","unstructured":"Carion, N., Gustafson, L., Hu, H.T., Debnath, S., Hu, R., Suris, D., Ryali, C., Alwala, K.V., Khedr, H., and Huang, A. (2025). SAM 3: Segment anything with concepts. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.-Y. (2022). Segment anything. arXiv.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_19","unstructured":"Ravi, N., Gabeur, V., Hu, Y.T., Hu, R., Ryali, C., Ma, T., Khedr, H., R\u00e4dle, R., Rolland, C., and Gustafson, L. (2024). SAM 2: Segment anything in images and videos. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"29795","DOI":"10.1038\/s41598-025-15617-1","article-title":"Revisiting model scaling with a U-net benchmark for 3D medical image segmentation","volume":"15","author":"Huang","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nasir, R., Khurshid, K., and Hayat, M. (2026). Few-shot hyperspectral representation learning for multi-task forensic document analysis using episodic prototypical networks. Appl. Sci., 16.","DOI":"10.3390\/app16146910"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","article-title":"Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved ?","volume":"37","author":"Bernard","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103547","DOI":"10.1016\/j.media.2025.103547","article-title":"Medical SAM adapter: Adapting segment anything model for medical image segmentation","volume":"102","author":"Wu","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"109526","DOI":"10.1016\/j.compbiomed.2024.109526","article-title":"3D MFA: An automated 3D multi-feature attention based approach for spine segmentation using a multi-stage network pruning","volume":"185","author":"Saeed","year":"2025","journal-title":"Comput. Biol. Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5753\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T13:06:04Z","timestamp":1789045564000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5753"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,10]]},"references-count":24,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185753"],"URL":"https:\/\/doi.org\/10.3390\/s26185753","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,10]]}}}