M U-Net: Intestine Segmentation Using Multi-dimensional Features for Ileus Diagnosis Assistance

The intestine is an essential digestive organ that can cause serious health problems once diseased. This paper proposes a method for intestine segmentation to intestine obstruction diagnosis assistance called multi-dimensional U-Net (M U-Net). We employ two encoders to extract features from two-dimensional (2D) CT slices and three-dimensional (3D) CT patches. These two encoders collaborate to enhance the segmentation accuracy of the model. Additionally, we incorporate deep supervision with the M U-Net to reduce the limitation of training with sparse label data sets. The experimental results demonstrated that the Dice of the proposed method was 73.22%, the recall was 79.89%, and the precision was 70.61%.