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Renal parenchyma segmentation from abdominal CT images using multi-atlas method with intensity and shape constraints
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Paper Abstract

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.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109493A (15 March 2019); doi: 10.1117/12.2512768
Show Author Affiliations
Hyeonjin Kim, Seoul Women's Univ. (Korea, Republic of)
Helen Hong, Seoul Women's Univ. (Korea, Republic of)
Kidon Chang, Yonsei Univ. College of Medicine (Korea, Republic of)
Koon Ho Rha, Yonsei Univ. College of Medicine (Korea, Republic of)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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