{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T10:48:48Z","timestamp":1768214928036,"version":"3.49.0"},"reference-count":32,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801288"],"award-info":[{"award-number":["61801288"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cardiac magnetic resonance imaging (CMRI) is a non\u2010invasive medical examination method that provides a comprehensive evaluation of the anatomy, function, blood flow, and histology for cardiovascular diseases. Accurately segmentation of the left and right ventricles and myocardium from CMR images can significantly aid doctors in diagnosing cardiovascular diseases. However, due to the variable shapes of the right ventricle, narrow and tubular myocardial structures, and unclear boundaries caused by small grayscale differences between cardiac substructures, CMR image segmentation remains a challenging task.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To address these challenges, a dual\u2010branch network that combines a region\u2010constrained Siamese encoder for ventricle segmentation and dynamic snake convolutions to enhance the detection of tubular myocardium structures. The fused outputs enable precise segmentation of cardiac substructures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A multi\u2010target segmentation network named RCSiamTANet which integrates a region constraint guided Siamese network and a tubular structure ware is proposed. The network consists of two sub\u2010networks, i.e., the left\u2010right ventricle segmentation sub\u2010network and the myocardium segmentation sub\u2010network. The encoder of the left right ventricle segmentation sub\u2010network contains two parallel branches, in which the original image slice encoder uses CNN to extract the original image features, while the region constraint slice encoder leverages the Siamese network to focus on the target region, avoiding redundant feature extraction. The information of the adjacent slice region constraint provided by the Siamese network and the features of the original slices are extracted at the same time to capture more local details for the joint segmentation of the left and right ventricles. The myocardium segmentation sub\u2010network employs dynamic snake convolutions to capture the topological information of the myocardium's tubular structure, improving the perception of the slender tubular structure. Finally, the outputs of the two sub\u2010networks are fused to achieve accurate segmentation of the cardiac substructures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared to state\u2010of\u2010art models such as FCN, UNet, TransUNet, SwinUNet, and EMCAD, RCSiamTANet achieves significance improvements in mean Dice scores on the ACDC dataset, with increases of 4.47%, 3.81%, 2.56%, 4.56%, and 0.84%, respectively. On the M&amp;Ms dataset, RCSiamTANet improves mean Dice scores by 3.44%, 3.49%, 2.76%, 3.37%, and 1.21%, respectively. On the M&amp;Ms\u20102 dataset, improvements in mean Dice scores are 2.33%, 0.40%, 1.10%, 0.33%, and 0.33%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed RCSiamTANet significantly enhances the segmentation performance of complex cardiac structural regions and tubular structures, effectively improving both accuracy and generalization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70172","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T13:35:18Z","timestamp":1764250518000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A segmentation method for cardiac MRI that incorporates region constraint guidance and tubular structure awareness within a Siamese network architecture"],"prefix":"10.1002","volume":"52","author":[{"given":"Qiaohong","family":"Liu","sequence":"first","affiliation":[{"name":"School of Medical Instruments Shanghai University of Medicine and Health Sciences  Shanghai China"},{"name":"Innovation Center for Intelligent Ophthalmic Technologies and Equipment Shanghai University of Medicine and Health Sciences  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keyan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Health Science and Engineering University of Shanghai for Science and Technology  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Health Science and Engineering University of Shanghai for Science and Technology  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junshi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Health Science and Engineering University of Shanghai for Science and Technology  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanqing","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Medical Instruments Shanghai University of Medicine and Health Sciences  Shanghai China"},{"name":"Innovation Center for Intelligent Ophthalmic Technologies and Equipment Shanghai University of Medicine and Health Sciences  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Medical Instruments Shanghai University of Medicine and Health Sciences  Shanghai China"},{"name":"Innovation Center for Intelligent Ophthalmic Technologies and Equipment Shanghai University of Medicine and Health Sciences  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","unstructured":"RadjenovicA RoditiG Clinical cardiovascular imaging Textbook of Vascular Medicine(2019)67\u201079.","DOI":"10.1007\/978-3-030-16481-2_7"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11101034"},{"key":"e_1_2_9_4_1","unstructured":"TranPV A fully convolutional neural network for cardiac segmentation in short\u2010axis mri arXiv preprint arXiv:1604.00494 (2016)."},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.10.004"},{"key":"e_1_2_9_6_1","doi-asserted-by":"crossref","unstructured":"BaumgartnerCF KochLM PollefeysM et\u00a0al.An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation.Statistical Atlases and Computational Models of the Heart. 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