LSSED: A Robust Segmentation Network for Inflamed Appendix from CT Images

Abstract

Acute appendicitis (AA) is one of the most prevalent surgical acute abdominal condition diseases. The treatment management of A A is highly dependent on the CT image diagnosis. However, the in-flamed appendix exhibits blurred boundaries with nearby tissue, varying shapes, and sizes. These properties require high robustness and generalization capability of inflamed appendix segmentation networks. In this paper, we propose a CNN-Transformer-based encoder-decoder segmentation network (LSSED) equipped with localized stochastic sensitivity (LSS) loss function and residual dilated paths (RD-Paths) to solve above problems. The proposed method effectively learns robust features of the input data by reducing the LSS of unseen samples. In addition, the RD-Paths capture multiscale feature information and reduce the semantic gap between the encoder and decoder, which improves the accuracy of the segmentation. Empirical studies on a real-world AA dataset show that our method yields the best performance in terms of average Dice similarity coefficient (DSC) and Hausdorff Distance of 95% (HD95) compared to several state-of-the-art segmentation networks.