{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T20:26:46Z","timestamp":1783024006439,"version":"3.54.6"},"reference-count":69,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110775","type":"journal-article","created":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T16:26:20Z","timestamp":1781454380000},"page":"110775","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Active domain adaptation via structural-prior warm-start and region uncertainty for cross-modality cardiac image segmentation"],"prefix":"10.1016","volume":"125","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7996-3836","authenticated-orcid":false,"given":"Zhen","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4178-9921","authenticated-orcid":false,"given":"Xu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingsen","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0952-9915","authenticated-orcid":false,"given":"Zuoyong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianping","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110775_b1","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1109\/JPROC.2024.3507831","article-title":"Domain generalization for medical image analysis: A review","volume":"112","author":"Yoon","year":"2024","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.bspc.2026.110775_b2","doi-asserted-by":"crossref","first-page":"3543","DOI":"10.1109\/TMI.2021.3090082","article-title":"Multi-centre, multi-vendor and multi-disease cardiac segmentation: The M&Ms challenge","volume":"40","author":"Campello","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110775_b3","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1109\/TMI.2020.3048055","article-title":"Diminishing uncertainty within the training pool: Active learning for medical image segmentation","volume":"40","author":"Nath","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110775_b4","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023","first-page":"25","article-title":"COLoSSAL: A benchmark for cold-start active learning for 3D medical image segmentation","author":"Liu","year":"2023"},{"key":"10.1016\/j.bspc.2026.110775_b5","article-title":"Domain generalization via model-agnostic learning of semantic features","volume":"vol. 32","author":"Dou","year":"2019"},{"key":"10.1016\/j.bspc.2026.110775_b6","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1109\/TMI.2020.2972701","article-title":"Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation","volume":"39","author":"Chen","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110775_b7","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.1109\/TMI.2023.3238067","article-title":"Partial unbalanced feature transport for cross-modality cardiac image segmentation","volume":"42","author":"Dong","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110775_b8","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/JBHI.2021.3126874","article-title":"Constraint-based unsupervised domain adaptation network for multi-modality cardiac image segmentation","volume":"26","author":"Du","year":"2022","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.bspc.2026.110775_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104250","article-title":"A teacher\u2013student framework with Fourier transform augmentation for COVID-19 infection segmentation in CT images","volume":"79","author":"Chen","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110775_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.108729","article-title":"Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning","volume":"121","author":"Hong","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.bspc.2026.110775_b11","doi-asserted-by":"crossref","first-page":"3738","DOI":"10.1109\/TMI.2023.3306105","article-title":"FVP: Fourier visual prompting for source-free unsupervised domain adaptation of medical image segmentation","volume":"42","author":"Wang","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110775_b12","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025","first-page":"574","article-title":"Source-free domain adaptation for cross-modality cardiac image segmentation with contrastive class relationship consistency","author":"Ma","year":"2026"},{"key":"10.1016\/j.bspc.2026.110775_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103179","article-title":"Unsupervised model adaptation for source-free segmentation of medical images","volume":"95","author":"Stan","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110775_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106028","article-title":"Source-free unsupervised adaptive segmentation for knee joint MRI","volume":"92","author":"Li","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110775_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.108429","article-title":"Semi-supervised medical image segmentation via region stitching and teacher-assistant collaboration","volume":"112","author":"Liu","year":"2026","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110775_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102825","article-title":"Unpaired, unsupervised domain adaptation assumes your domains are already similar","volume":"87","author":"van Tulder","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110775_b17","doi-asserted-by":"crossref","unstructured":"B. Xie, L. Yuan, S. Li, C.H. Liu, X. Cheng, Towards fewer annotations: Active learning via region impurity and prediction uncertainty for domain adaptive semantic segmentation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2022, pp. 8058\u20138068.","DOI":"10.1109\/CVPR52688.2022.00790"},{"key":"10.1016\/j.bspc.2026.110775_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105714","article-title":"ECGTransForm: Empowering adaptive ECG arrhythmia classification framework with bidirectional transformer","volume":"89","author":"El-Ghaish","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110775_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107494","article-title":"Cross-view identification based on gait bioinformation using a dynamic densely connected spatial\u2013temporal feature decoupling network","volume":"104","author":"Qiao","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110775_b20","series-title":"Computer Vision \u2013 ECCV 2022","first-page":"449","article-title":"D2ADA: Dynamic density-aware active domain adaptation for semantic segmentation","author":"Wu","year":"2022"},{"key":"10.1016\/j.bspc.2026.110775_b21","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024","first-page":"107","article-title":"An uncertainty-guided tiered self-training framework for active source-free domain adaptation in prostate segmentation","author":"Luo","year":"2024"},{"key":"10.1016\/j.bspc.2026.110775_b22","series-title":"Computer Vision \u2013 ECCV 2024","first-page":"405","article-title":"Efficient active domain adaptation for semantic segmentation by selecting information-rich superpixels","author":"Gao","year":"2025"},{"key":"10.1016\/j.bspc.2026.110775_b23","article-title":"BatchBALD: Efficient and diverse batch acquisition for deep Bayesian active learning","volume":"vol. 32","author":"Kirsch","year":"2019"},{"key":"10.1016\/j.bspc.2026.110775_b24","first-page":"8186","article-title":"Topology-aware uncertainty for image segmentation","volume":"vol. 36","author":"Gupta","year":"2023"},{"key":"10.1016\/j.bspc.2026.110775_b25","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024","first-page":"283","article-title":"SBC-AL: Structure and boundary consistency-based active learning for medical image segmentation","author":"Zhou","year":"2024"},{"key":"10.1016\/j.bspc.2026.110775_b26","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1109\/TMI.2020.2973595","article-title":"Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation","volume":"39","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110775_b27","doi-asserted-by":"crossref","unstructured":"Q. Xu, R. Zhang, Y. Zhang, Y. Wang, Q. Tian, A Fourier-based framework for domain generalization, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2021, pp. 14378\u201314387.","DOI":"10.1109\/CVPR46437.2021.01415"},{"key":"10.1016\/j.bspc.2026.110775_b28","series-title":"Domain generalization with MixStyle","author":"Zhou","year":"2021"},{"key":"10.1016\/j.bspc.2026.110775_b29","doi-asserted-by":"crossref","unstructured":"Q. Liu, C. Chen, J. Qin, Q. Dou, P.-A. Heng, FedDG: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2021, pp. 1013\u20131023.","DOI":"10.1109\/CVPR46437.2021.00107"},{"key":"10.1016\/j.bspc.2026.110775_b30","series-title":"Computer Vision \u2013 ECCV 2018","first-page":"484","article-title":"Two at once: Enhancing learning and generalization capacities via IBN-net","author":"Pan","year":"2018"},{"key":"10.1016\/j.bspc.2026.110775_b31","doi-asserted-by":"crossref","unstructured":"Z. Zhou, L. Qi, X. Yang, D. Ni, Y. Shi, Generalizable cross-modality medical image segmentation via style augmentation and dual normalization, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2022, pp. 20824\u201320833.","DOI":"10.1109\/CVPR52688.2022.02019"},{"key":"10.1016\/j.bspc.2026.110775_b32","doi-asserted-by":"crossref","unstructured":"B. Chen, Y. Zhu, Y. Ao, et al., Generalizable single-source cross-modality medical image segmentation via invariant causal mechanisms, in: 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision, WACV, 2025, pp. 3592\u20133602.","DOI":"10.1109\/WACV61041.2025.00354"},{"key":"10.1016\/j.bspc.2026.110775_b33","first-page":"865","article-title":"Synergistic image and feature adaptation: Towards cross-modality domain adaptation for medical image segmentation","volume":"vol. 33","author":"Chen","year":"2019"},{"key":"10.1016\/j.bspc.2026.110775_b34","doi-asserted-by":"crossref","first-page":"3604","DOI":"10.1109\/TMI.2021.3090432","article-title":"Structure-driven unsupervised domain adaptation for cross-modality cardiac segmentation","volume":"40","author":"Cui","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110775_b35","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1109\/TMI.2023.3238114","article-title":"Unsupervised cross-modality adaptation via dual structural-oriented guidance for 3D medical image segmentation","volume":"42","author":"Xian","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110775_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102078","article-title":"Disentangle domain features for cross-modality cardiac image segmentation","volume":"71","author":"Pei","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110775_b37","doi-asserted-by":"crossref","first-page":"2926","DOI":"10.1109\/TMI.2021.3059265","article-title":"Self-attentive spatial adaptive normalization for cross-modality domain adaptation","volume":"40","author":"Tomar","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110775_b38","doi-asserted-by":"crossref","first-page":"9359","DOI":"10.1109\/TIP.2021.3124674","article-title":"Adversarial domain adaptation with prototype-based normalized output conditioner","volume":"30","author":"Hu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.bspc.2026.110775_b39","unstructured":"Q. En, Y. Guo, Unsupervised domain adaptation for medical image segmentation with dynamic prototype-based contrastive learning, in: Proceedings of the Fifth Conference on Health, Inference, and Learning, 2024, pp. 312\u2013325."},{"key":"10.1016\/j.bspc.2026.110775_b40","doi-asserted-by":"crossref","first-page":"3555","DOI":"10.1109\/TMI.2021.3090412","article-title":"Unsupervised domain adaptation with variational approximation for cardiac segmentation","volume":"40","author":"Wu","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110775_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109155","article-title":"Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation","volume":"250","author":"Hong","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.bspc.2026.110775_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101766","article-title":"Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation","volume":"65","author":"Xia","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110775_b43","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023","first-page":"618","article-title":"Context-aware pseudo-label refinement for source-free domain adaptive fundus image segmentation","author":"Huai","year":"2023"},{"key":"10.1016\/j.bspc.2026.110775_b44","doi-asserted-by":"crossref","first-page":"3932","DOI":"10.1109\/TMI.2023.3318364","article-title":"UPL-SFDA: Uncertainty-aware pseudo label guided source-free domain adaptation for medical image segmentation","volume":"42","author":"Wu","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110775_b45","doi-asserted-by":"crossref","first-page":"3098","DOI":"10.1109\/TMI.2024.3387415","article-title":"FPL+: Filtered pseudo label-based unsupervised cross-modality adaptation for 3D medical image segmentation","volume":"43","author":"Wu","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110775_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102457","article-title":"Source free domain adaptation for medical image segmentation with Fourier style mining","volume":"79","author":"Yang","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110775_b47","series-title":"Unsupervised Domain Adaptation: Recent Advances and Future Perspectives","first-page":"191","article-title":"Continual test-time domain adaptation","author":"Wang","year":"2022"},{"key":"10.1016\/j.bspc.2026.110775_b48","doi-asserted-by":"crossref","unstructured":"Z. Chen, Y. Pan, Y. Ye, M. Lu, Y. Xia, Each test image deserves a specific prompt: Continual test-time adaptation for 2D medical image segmentation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2024, pp. 11184\u201311193.","DOI":"10.1109\/CVPR52733.2024.01063"},{"key":"10.1016\/j.bspc.2026.110775_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.103069","article-title":"TestFit: A plug-and-play one-pass test time method for medical image segmentation","volume":"92","author":"Zhang","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110775_b50","unstructured":"Y. Gal, R. Islam, Z. Ghahramani, Deep Bayesian active learning with image data, in: Proceedings of the 34th International Conference on Machine Learning, 2017, pp. 1183\u20131192."},{"key":"10.1016\/j.bspc.2026.110775_b51","series-title":"Active learning for convolutional neural networks: A core-set approach","author":"Sener","year":"2017"},{"key":"10.1016\/j.bspc.2026.110775_b52","doi-asserted-by":"crossref","unstructured":"L. Cai, X. Xu, J.H. Liew, C.S. Foo, Revisiting superpixels for active learning in semantic segmentation with realistic annotation costs, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2021, pp. 10983\u201310992.","DOI":"10.1109\/CVPR46437.2021.01084"},{"key":"10.1016\/j.bspc.2026.110775_b53","doi-asserted-by":"crossref","unstructured":"Y. Siddiqui, J. Valentin, M. Niessner, ViewAL: Active learning with viewpoint entropy for semantic segmentation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2020, pp. 9430\u20139440.","DOI":"10.1109\/CVPR42600.2020.00945"},{"key":"10.1016\/j.bspc.2026.110775_b54","series-title":"Medical Imaging with Deep Learning","first-page":"496","article-title":"Making your first choice: To address cold start problem in medical active learning","author":"Chen","year":"2024"},{"key":"10.1016\/j.bspc.2026.110775_b55","doi-asserted-by":"crossref","unstructured":"M. Ning, D. Lu, D. Wei, et al., Multi-anchor active domain adaptation for semantic segmentation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2021, pp. 9092\u20139102.","DOI":"10.1109\/ICCV48922.2021.00898"},{"key":"10.1016\/j.bspc.2026.110775_b56","doi-asserted-by":"crossref","unstructured":"V. Prabhu, A. Chandrasekaran, K. Saenko, J. Hoffman, Active domain adaptation via clustering uncertainty-weighted embeddings, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2021, pp. 8485\u20138494.","DOI":"10.1109\/ICCV48922.2021.00839"},{"key":"10.1016\/j.bspc.2026.110775_b57","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111403","article-title":"Active learning for cross-modal cardiac segmentation with sparse annotation","volume":"162","author":"Chen","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.bspc.2026.110775_b58","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1109\/TNNLS.2025.3622936","article-title":"Kernel-based representation alignment for class imbalanced semi-supervised learning","volume":"37","author":"Li","year":"2026","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.bspc.2026.110775_b59","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.bspc.2026.110775_b60","doi-asserted-by":"crossref","unstructured":"H. Huang, L. Lin, R. Tong, et al., UNet 3+: A full-scale connected UNet for medical image segmentation, in: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 2020, pp. 1055\u20131059.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"10.1016\/j.bspc.2026.110775_b61","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.media.2010.12.004","article-title":"A review of segmentation methods in short axis cardiac MR images","volume":"15","author":"Petitjean","year":"2011","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110775_b62","series-title":"2016 Fourth International Conference on 3D Vision","first-page":"565","article-title":"V-Net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"10.1016\/j.bspc.2026.110775_b63","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.1109\/TPAMI.2018.2869576","article-title":"Multivariate mixture model for myocardial segmentation combining multi-source images","volume":"41","author":"Zhuang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.bspc.2026.110775_b64","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101537","article-title":"Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge","volume":"58","author":"Zhuang","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110775_b65","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"vol. 32","author":"Paszke","year":"2019"},{"key":"10.1016\/j.bspc.2026.110775_b66","series-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2017"},{"key":"10.1016\/j.bspc.2026.110775_b67","article-title":"ImageNet classification with deep convolutional neural networks","volume":"vol. 25","author":"Krizhevsky","year":"2012"},{"key":"10.1016\/j.bspc.2026.110775_b68","doi-asserted-by":"crossref","unstructured":"T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Doll\u00e1r, Focal loss for dense object detection, in: Proceedings of the IEEE International Conference on Computer Vision, ICCV, 2017, pp. 2999\u20133007.","DOI":"10.1109\/ICCV.2017.324"},{"key":"10.1016\/j.bspc.2026.110775_b69","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426013297?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426013297?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:59:08Z","timestamp":1783022348000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426013297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":69,"alternative-id":["S1746809426013297"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110775","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Active domain adaptation via structural-prior warm-start and region uncertainty for cross-modality cardiac image segmentation","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110775","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110775"}}