Tumor Segmentation Based on Deeply Supervised Multi-Scale U-Net

Lei Wang, Bo Wang, Zhenghua Xu

Abstract: Although deep learning has achieved great success in the field of medical image processing, the existing deep learning based medical image segmentation solutions still cannot obtain satisfactory performances for abdominal small organs and lesions due to their small object size and shape-variability. In this work, a Deeply Supervised Multi-Scale U-Net (DSMS U-Net) is proposed for more accurate segmentation performances on abdominal small organs images. DSMS U-Net integrate the existing U-Net model with a restoration decoder module and some multi-scale convolution modules. Our experiment results demonstrate that the proposed DSMS U-Net approach has much better segmentation performances than the state-of-the-art baselines.