Segmentation of renal parenchyma responsible for renal function is necessary to evaluate contralateral renal hypertrophy and to predict renal function after renal partial nephrectomy (RPN). Although most studies have segmented the kidney on CT images to analyze renal function, renal function analysis is required without radiation exposure by segmenting the renal parenchyma on MR images. However, renal parenchyma segmentation is difficult due to small area in the abdomen, blurred boundary, large variations in the shape of kidney among patients, similar intensities with nearby organs such as the liver, spleen and vessels. Furthermore, signal intensity is different for each data due to a lot of movement when taking abdominal MR even when photographed with the same device. Therefore, we propose cascaded deep convolutional neural network for renal parenchyma segmentation with signal intensity correction in abdominal MR images. First, intensity normalization is performed in the whole MR image. Second, kidney is localized using 2D segmentation networks based on attention UNet on the axial, coronal, sagittal planes and combining through a majority voting. Third, signal intensity correction between each data is performed in the localized kidney area. Finally, renal parenchyma is segmented using 3D segmentation network based on UNet++. The average DSC of renal parenchyma was 91.57%. Our method can be used to assess contralateral renal hypertrophy and to predict renal function by measuring volume change of the renal parenchyma on MR images without radiation exposure instead of CT images, and can establish basis for treatment after RPN.
|Title of host publication||Medical Imaging 2021|
|Subtitle of host publication||Computer-Aided Diagnosis|
|Editors||Maciej A. Mazurowski, Karen Drukker|
|Publication status||Published - 2021|
|Event||Medical Imaging 2021: Computer-Aided Diagnosis - Virtual, Online, United States|
Duration: 2021 Feb 15 → 2021 Feb 19
|Name||Progress in Biomedical Optics and Imaging - Proceedings of SPIE|
|Conference||Medical Imaging 2021: Computer-Aided Diagnosis|
|Period||21/2/15 → 21/2/19|
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) , and by the MISP(Ministry of Science, ICT), Korea, under the National Program for Excellence in SW(2016-0-00022) supervised by the IITP(Institute of Information & Communications Technology Planning & Evaluation).
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All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Radiology Nuclear Medicine and imaging