Anchorwise Fuzziness Modeling in Convolution–Transformer Neural Network for Left Atrium Image Segmentation

Segmentation on the left atrium from magnetic resonance imaging scans is a crucial task for the clinical diagnosis of cardiac diseases. Automatic segmentation methods based on deep learning have demonstrated their advantages in recent years, but they often suffer from the difficulty to process high uncertainty of some regions. Focusing on the cognitive uncertainty in deep neural networks, we build an uncertainty-aware model based on anchor fuzzy sets in this article. A fuzzy entropy measure defined on membership functions is employed to quantify the uncertainty induced by an anchor's membership degrees to the individual categories. A key insight in this study is that the anchor regions with higher uncertainty are considered to include hard-generalized cases. Based on this prior of high uncertainty about image segmentation, the uncertainty-aware model exhibits the self-learning attention that sheds light on the regions the deep learning should focus on. In addition, our segmentation model with an uncertainty-aware plug-in unit highlights its nonlocal perception due to Fourier convolution and global self-attention mechanism. Experiments show that our model can achieve competitive results on left atrium segmentation tasks. This article provides some useful guidelines for using uncertainty modeling to improve the performance of deep learning systems for image segmentation tasks.