Residual H-Net: A high-level feature extraction approach for brain lesion segmentation

Abstract

In the case of acute stroke patients, mere diagnosis falls short, and segmentation is needed. Recent development in deep learning and image processing has provided us with the potential to automatically perform brain lesion segmentation. However, many of the approaches ended up failing to generalize to new data by overfitting the ATLAS R1.2 dataset and ignoring information extraction of the high-level features. We propose a novel Residual H-Net that addresses these two issues by adding a special residual block in the middle of the U-Net and increasing dilation size to better extract the high-level features. The presented model is less susceptible to overfitting and much easier to train. The Residual H-Net is tested on a subset of ATLAS R2.0 data and shows promising performance against the previous state-of-the-art model.