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.
|Title of host publication||Medical Imaging 2020|
|Subtitle of host publication||Computer-Aided Diagnosis|
|Editors||Horst K. Hahn, Maciej A. Mazurowski|
|Publication status||Published - 2020|
|Event||Medical Imaging 2020: Computer-Aided Diagnosis - Houston, United States|
Duration: 2020 Feb 16 → 2020 Feb 19
|Name||Progress in Biomedical Optics and Imaging - Proceedings of SPIE|
|Conference||Medical Imaging 2020: Computer-Aided Diagnosis|
|Period||20/2/16 → 20/2/19|
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT (NRF-2019R1A2C2004746).
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Radiology Nuclear Medicine and imaging