Segmentation of the renal parenchyma consisting of the cortex and the medulla responsible for the renal function is necessary to assess contralateral renal hypertrophy and to predict renal function after renal partial nephrectomy (RPN). In this paper, we propose an automatic renal parenchyma segmentation from abdominal CT images using multi-atlas methods with intensity and shape constraints. First, atlas selection is performed to select the training images in a training set which is similar in appearance to the target image using volume-based registration and intensity similarity. Second, renal parenchyma is segmented using volume- and model-based registration and intensity-constrained locally-weighted voting to segment the cortex and medulla with different intensities. Finally, the cortex and medulla are refined with the threshold value selected by applying a Gaussian mixture model and the cortex slab accumulation map to reduce leakage to the adjacent organs with similar intensity to the medulla and under-segmented area due to lower intensity than the training set. The average dice similarity coefficient of renal parenchyma was 92.68%, showed better results of 15.84% and 2.47% compared to the segmentation method using majority voting and intensity-constrained locally-weighted voting, 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 renal partial nephrectomy.
|Title of host publication||Medical Imaging 2019|
|Subtitle of host publication||Image Processing|
|Editors||Elsa D. Angelini, Elsa D. Angelini, Elsa D. Angelini, Bennett A. Landman|
|Publication status||Published - 2019|
|Event||Medical Imaging 2019: Image Processing - San Diego, United States|
Duration: 2019 Feb 19 → 2019 Feb 21
|Name||Progress in Biomedical Optics and Imaging - Proceedings of SPIE|
|Conference||Medical Imaging 2019: Image Processing|
|Period||19/2/19 → 19/2/21|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea grant funded by the Korean Government (MEST) (NRF-2015R1A2A2A04003460), and the MIST(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW supervised by the IITP(Institute for Information & communications Technology Promotion)(2016-0-00022).
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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