Multi-view semi-supervised attention network for 3D cardiac image segmentation

In recent years, semi-supervised methods have been rapidly developed for three-dimensional (3D) medical image analysis. However, previous semi-supervised methods for three-dimensional medical images usually focused on single-view information and required a large number of annotated datasets. In this paper, we innovatively propose a multi-view (coronal and transverse) attention network for semi-supervised 3D cardiac image segmentation. In this way, the proposed method obtained more complementary segmentation information, which improved the segmentation performance. Simultaneously, we integrated the CBAM module and adaptive channel attention block into the 3D VNet (CBAP - VNet) to enhance the focus on the segmentation regions and edge portions. We first introduced the CutMix data augmentation mechanism to enhance 3D cardiac medical image segmentation. In this way, the proposed method made full use of the mixed regions in the images and expanded the training dataset. Our method was tested on two publicly available cardiac datasets and achieved good segmentation results. Our code and models are available at https://github.com/HuaidongLi-NEFU/TPSSAN.