SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal
Segmentation
- URL: http://arxiv.org/abs/2306.08213v1
- Date: Wed, 14 Jun 2023 02:57:23 GMT
- Title: SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal
Segmentation
- Authors: Zhusi Zhong, Jie Li, Lulu Bi, Li Yang, Ihab Kamel, Rama Chellappa,
Xinbo Gao, Harrison Bai, Zhicheng Jiao
- Abstract summary: We propose a novel Structure-Modal Constrained (SMC) UDA framework based on a discriminative paradigm and introduce edge structure as a bridge between domains.
With the structure-constrained self-learning and progressive ROI, our methods segment the kidney by locating the 3D spatial structure of the edge.
experiments show that our proposed SMC-UDA has a strong generalization and outperforms generative UDA methods.
- Score: 100.86339246424541
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Medical image segmentation based on deep learning often fails when deployed
on images from a different domain. The domain adaptation methods aim to solve
domain-shift challenges, but still face some problems. The transfer learning
methods require annotation on the target domain, and the generative
unsupervised domain adaptation (UDA) models ignore domain-specific
representations, whose generated quality highly restricts segmentation
performance. In this study, we propose a novel Structure-Modal Constrained
(SMC) UDA framework based on a discriminative paradigm and introduce edge
structure as a bridge between domains. The proposed multi-modal learning
backbone distills structure information from image texture to distinguish
domain-invariant edge structure. With the structure-constrained self-learning
and progressive ROI, our methods segment the kidney by locating the 3D spatial
structure of the edge. We evaluated SMC-UDA on public renal segmentation
datasets, adapting from the labeled source domain (CT) to the unlabeled target
domain (CT/MRI). The experiments show that our proposed SMC-UDA has a strong
generalization and outperforms generative UDA methods.
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