TY - GEN
T1 - CAUDA-MI
T2 - 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
AU - Du, Dianrong
AU - Cui, Hengfei
AU - Li, Jiatong
AU - Zheng, Fan
AU - Xia, Yong
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Late Gadolinium Enhancement (LGE) imaging has emerged as the gold standard for cardiovascular disease diagnosis due to its ability to clearly delineate myocardial pathology. Professional interpretation of LGE images is usually difficult since their annotations are scarce, often necessitating the reliance on domain adaptation methods. Nevertheless, significant distribution discrepancy between datasets of different modalities usually results in poor transfer learning performances. To address this issue, we propose a general framework for cardiac MRI segmentation, called Cross Attention-Guided Unsupervised Domain Adaptation with Mutual Information (CAUDA-MI). This model leverages attention mechanisms on two data streams from the source and target domains, cleverly fusing the Query from the source domain with the Key and Value from the target domain, thereby aligning the implicit features of the target domain towards the source domain in the latent space. Additionally, we introduce single-domain mutual information as a supplementary means to further enhance the accuracy of myocardial segmentation. The proposed CAUDA-MI is tested on the MS-CMRSeg 2019 and MyoPS 2020 datasets, which achieves an average Dice score of 0.847 and 0.797 respectively. Comprehensive experimental results demonstrate that our proposed method surpasses previous state-of-the-art algorithms.
AB - Late Gadolinium Enhancement (LGE) imaging has emerged as the gold standard for cardiovascular disease diagnosis due to its ability to clearly delineate myocardial pathology. Professional interpretation of LGE images is usually difficult since their annotations are scarce, often necessitating the reliance on domain adaptation methods. Nevertheless, significant distribution discrepancy between datasets of different modalities usually results in poor transfer learning performances. To address this issue, we propose a general framework for cardiac MRI segmentation, called Cross Attention-Guided Unsupervised Domain Adaptation with Mutual Information (CAUDA-MI). This model leverages attention mechanisms on two data streams from the source and target domains, cleverly fusing the Query from the source domain with the Key and Value from the target domain, thereby aligning the implicit features of the target domain towards the source domain in the latent space. Additionally, we introduce single-domain mutual information as a supplementary means to further enhance the accuracy of myocardial segmentation. The proposed CAUDA-MI is tested on the MS-CMRSeg 2019 and MyoPS 2020 datasets, which achieves an average Dice score of 0.847 and 0.797 respectively. Comprehensive experimental results demonstrate that our proposed method surpasses previous state-of-the-art algorithms.
KW - Cardiac segmentation
KW - Domain adaptation
UR - https://www.scopus.com/pages/publications/105017862879
U2 - 10.1007/978-3-032-04978-0_5
DO - 10.1007/978-3-032-04978-0_5
M3 - 会议稿件
AN - SCOPUS:105017862879
SN - 9783032049773
T3 - Lecture Notes in Computer Science
SP - 46
EP - 56
BT - Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, Proceedings
A2 - Gee, James C.
A2 - Hong, Jaesung
A2 - Sudre, Carole H.
A2 - Golland, Polina
A2 - Alexander, Daniel C.
A2 - Iglesias, Juan Eugenio
A2 - Venkataraman, Archana
A2 - Kim, Jong Hyo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 September 2025 through 27 September 2025
ER -
