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.
Highlights
Medical image segmentation is a basic task of intelligent medical diagnosis, which aims at extracting target regions like organs, tissues or lesions from medical images
We use the strategy proposed by Synergistic Image and Feature Adaptation (SIFA) [20]; for category-wise alignment, we introduce a new module to the semantic prediction space
Four important cardiac substructures not covering each other in 2D coronal view are selected for segmentation, respectively, the ascending aorta (AA), the left atrium blood cavity (LAC), the left ventricle blood cavity (LVC) and the myocardium of the left ventricle (MYO)
Summary
Medical image segmentation is a basic task of intelligent medical diagnosis, which aims at extracting target regions like organs, tissues or lesions from medical images. Aligning the distributions of source and target domain data is a common strategy for unsupervised domain adaptation. Some unsupervised domain adaptation works implement global alignment in the input image space [5] [6] [7] [8] [9], regarding each input image as a whole sample. Some other works implement global alignment in the feature space [10] [11] [12] [13], taking each feature map as a sample. The alignment of output maps provides a low computation way for feature alignment, which has been widely used in unsupervised domain adaptation segmentation [14] [15] [16]. Category-wise alignment in unsupervised domain adaptation segmentation is mainly implemented at the feature
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