An Efficient Hybrid Model for Kidney Tumor Segmentation in CT Images

Kidney tumor segmentation from CT-volumes is essential for lesion diagnosis. Considering excessive GPU memory requirements for 3D medical images, slices and patches are exploited for training and inference in conventional U-Net variant architectures, which inevitably hampers contextual learning. In this paper, we propose a novel effective hybrid model for kidney tumor segmentation in CT images with two parts: 1) Foreground Segmentation Network; 2) Sparse PointCloud Segmentation Network. Specifically, Foreground Segmentation Network firstly segments the foreground, i.e., kidneys with tumors, from background in voxel grid using classical V-Net. Secondly, we represent the obtained foreground regions as point clouds and feed them into the Sparse PointCloud Segmentation Networks to conduct fine-grained segmentation for kidney and tumor. The critical module embedded in the second part is an efficient Submanifold Sparse Convolutional Networks (SSCNs). By exploiting SSCNs, our proposed model can take all foreground as input for better context learning in a memory-efficient manner, and consider the anisotropy of CT images as well. Experiments show that our model can achieve state-of-the-art tumor segmentation while reducing GPU resource demand significantly.