Multiple GANs guided by self-attention mechanism for automatic cardiac image segmentation
Chang Yuwen, Lei Jiang, Hengfei Cui · Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021) · 2022
In this paper, we propose a cross-modal unsupervised domain adaptive cardiac substructure automatic segmentation method to address the problem of a sharp decline in performance of a neural network trained in a specific imaging domain to migrate to another target domain that lacks annotations. The proposed multiple generative adversarial networks guided by self-attention framework is called GANSA for short. GANSA eliminates the differences in image appearance and features between different domains (MRI and CT) through GANs, and uses cyclic consistency loss to ensure that the features of the image itself remain unchanged. We use the self-attention mechanism to control GANs, which ensures the details of the generated image, and pays attention to the connection of long-distance information of the image, in order to complete the cross-modal cardiac segmentation task. The proposed framework is trained and tested on the Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge 2017 dataset. Experimental results show that our method outperforms the state-of-the-art method.