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Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation | IEEE Journals & Magazine | IEEE Xplore

Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation


Abstract:

Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) and computed tomography angiography (CTA) is essential in providing supportive i...Show More

Abstract:

Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) and computed tomography angiography (CTA) is essential in providing supportive information for diagnosing and treatment planning of multiple intracranial vascular diseases. Different imaging modalities utilize distinct principles to visualize the cerebral vasculature, which leads to the limitations of expensive annotations and performance degradation while training and deploying deep learning models. In this paper, we propose an unsupervised domain adaptation framework CereTS to perform translation and segmentation of cross-modality unpaired cerebral angiography. Considering the commonality of vascular structures and stylistic textures as domain-invariant and domain-specific features, CereTS adopts a multi-level domain alignment pattern that includes an image-level cyclic geometric consistency constraint, a patch-level masked contrastive constraint and a feature-level semantic perception constraint to shrink domain discrepancy while preserving consistency of vascular structures. Conducted on a publicly available TOF-MRA dataset and a private CTA dataset, our experiment shows that CereTS outperforms current state-of-the-art methods by a large margin. Code is available at https://github.com/mileswyn/CereTS.
Page(s): 1 - 14
Date of Publication: 26 December 2024

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Funding Agency:

Image Processing Center, Beihang University, Beijing, China
Image Processing Center, Beihang University, Beijing, China
Department of Neurology, Tongji Hospital, Wuhan, China
Zhongxiang Hospital of Traditional Chinese Medicine, Zhongxiang, China
Zhongxiang Hospital of Traditional Chinese Medicine, Zhongxiang, China
Image Processing Center, Beihang University, Beijing, China
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China

Image Processing Center, Beihang University, Beijing, China
Image Processing Center, Beihang University, Beijing, China
Department of Neurology, Tongji Hospital, Wuhan, China
Zhongxiang Hospital of Traditional Chinese Medicine, Zhongxiang, China
Zhongxiang Hospital of Traditional Chinese Medicine, Zhongxiang, China
Image Processing Center, Beihang University, Beijing, China
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
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