Kidney and Kidney Tumor Segmentation using Spatial and Channel attention enhanced U-Net

Sajan Rajan Gohil, Abhi Navinkumar Lad

Keywords: Kidney Tumor, segmentation, 3D U-Net, Spatial Attention, Channel Attention

Abstract: Kidney and Kidney tumor segmentation from CT scans has tremendous potential to help doctors in early diagnosis and localization of tumor, its size and type and for making timely treatment plans. However, considering the nature and volume of data, it is difficult and time consuming to train on such CT scans. In this paper, we propose enhancements to the 3D U-Net model to incorporate Spatial and Channel Attention in order to improve the identification and localization of segmentation structures by learning on spatial context. When compared with Residual U-Net model with greater depth and more feature maps, our Spatial and Channel Attention enhanced U-Net with less depth and feature maps performed significantly better on validation and training set when trained under similar conditions.