Renal parenchyma segmentation in abdominal CT images based on deep convolutional neural networks with similar atlas selection and transformation

Abstract

Segmentation of the renal parenchyma which consists of renal cortex and medulla responsible for the renal function is necessary to evaluate contralateral renal hypertrophy and to predict renal function after renal partial nephrectomy (RPN). However, segmentation of the renal parenchyma is difficult due to the large variations in the shape of the kidney among patients and similar intensities with nearby organs such as the liver, spleen, vessels and the collecting system. Therefore, we propose an automatic renal parenchyma segmentation based on 2D and 3D deep convolutional neural networks with similar atlas selection and transformation in abdominal CT images. First, kidney is localized using 2D segmentation networks based on U-net on the axial, coronal, and sagittal planes and combining through a majority voting. Second, similar atlases to test volume in the training set are selected by calculating mutual information between the kidney test volume and the training volume, and then transformed to the test volume using volume-based affine registration. Finally, renal parenchyma is segmented using 3D segmentation network based on U-net. The average dice similarity coefficient of renal parenchyma was 94.59%, showed better results of 10.41% and 0.80% compared to the segmentation method using fusion of three 2D segmentation networks results and combined 2D and 3D segmentation networks, respectively. Our method can be used to assess the contralateral renal hypertrophy and to predict the renal function by measuring the volume change of the renal parenchyma, and can establish the basis for treatment after RPN.