Domain base dynamic convolution and distance map guidance for anterior mediastinal lesion segmentation

Abstract

The automatic segmentation of anterior mediastinal lesions in the enhanced CT image plays a significant role in clinical diagnosis. Due to the low incidence rate of anterior mediastinal disease, the data for training segmentation network may come from different hospitals, different equipment, and different operators, which leads to lower generalization and robustness of the trained network. This paper proposes an anterior mediastinal lesion segmentation method based on domain-adaptive dynamic convolution and distance map guidance. Considering the diversity of clinical data sources and equipment differences, the domain prior knowledge is added to the global average-pooling layer of the network, and the dynamic convolution forms specific network parameters for specific domains to enhance domain-specific information. Meanwhile, the Singed Distance Map (SDM) head is involved in the network according to the shape, size and position of the anterior mediastinal lesions to constraint the boundary and assist in localization. Ablation experiments demonstrate that the distance map can effectively constrain the segmentation target and reduce false positives. Both qualitative and quantitative experimental results indicate that our method can achieve more accurate anterior mediastinal lesion segmentation with greater generalization ability. Our approach achieves an overall Dice coefficient of 88.33%, which is 2.31% higher than existing state-of-the-art methods, and has achieved good performance in terms of ASSD evaluation for segmentation edges.