Multiple token rearrangement Transformer network with explicit superpixel constraint for segmentation of echocardiography

Abstract

Diagnostic cardiologists have considerable clinical demand for precise segmentation of echocardiography to diagnose cardiovascular disease. The paradox is that manual segmentation of echocardiography is a time-consuming and operator-dependent task. Computer-aided segmentation can reduce the workflow greatly. However, it is challenging to segment multi-type echocardiography, which is reflected in differential anatomic structures, artifacts, and blurred borderline. This study proposes the multiple token rearrangement Transformer network (MTRT-Net) embedded in three novel modules to address the corresponding three challenges. First, the depthwise deformable attention module can extract flexible features to adapt to anatomic structures of echocardiography with different ages and diseases. Second, the superpixel supervised module can cluster similar features and keep discriminative features away to make the segmentation regions tend to be an entire body. The artifacts have the influence in separating the complete internal region. Third, the atrous affinity aggregation module can integrate affinity features near the borderline to judge the blurred regions. Overall, the three modules rearrange the relationships of tokens and broaden the diversity of features. Besides, the explicit constraint brought by the superpixel supervised module enhances the performance of fitting ability. This study has 13747 echocardiography to train and test the MTRT-Net. Abundant experiments also validate the performance of MTRT-Net. Therefore, MTRT-Net can assist the diagnostician in segmenting the echocardiography precisely.