TITLE:
Semantic Constraint Based Unsupervised Domain Adaptation for Cardiac Segmentation
AUTHORS:
Xin Wang, Fan Zhu, Yaxin Peng, Chaomin Shen, Zhen Ye, Chaozheng Zhou
KEYWORDS:
Medical Image Segmentation, Domain Adaptation, Category-Wise Alignment, Cardiac Segmentation
JOURNAL NAME:
Advances in Pure Mathematics,
Vol.11 No.6,
June
30,
2021
ABSTRACT: The segmentation of unlabeled medical images is troublesome due to the high cost of annotation, and unsupervised domain adaptation is one solution to this. In this paper, an improved unsupervised domain adaptation method was proposed. The proposed method considered both global alignment and category-wise alignment. First, we aligned the appearance of two domains by image transformation. Second, we aligned the output maps of two domains in a global way. Then, we decomposed the semantic prediction map by category, aligning the prediction maps in a category-wise manner. Finally, we evaluated the proposed method on the 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, and obtained 82.1 on the dice similarity coefficient and 4.6 on the average symmetric surface distance, demonstrating the effectiveness of the combination of global alignment and category-wise alignment.