Wenjian Huang1, Hao Li1, Rui Wang2, Xiaodong Zhang2, Xiaoying Wang1,2, Jue Zhang1,3. 1. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China. 2. Department of Radiology, Peking University First Hospital, Beijing, China. 3. College of Engineering, Peking University, Beijing, China.
Abstract
PURPOSE: An automated accurate segmentation for dynamic contrast-enhanced magnetic resonance (DCE-MR) image sequences is essential for quantification of renal function. A self-supervised strategy is proposed for fully automatic segmentation of the renal DCE-MR images without using manually labeled data. METHODS: The proposed strategy employed both temporal and spatial information of the DCE-MR image sequences. First, the kidney area, the seed regions of the cortex, the medulla, and the pelvis were automatically detected in the spatial domain. Subsequently, all the pixels in the kidney were automatically labeled as the cortex, the medulla, or the pelvis based on their time-intensity signal and spatial position using a supervised classifier. The feasibility of the proposed strategy was verified on a dataset of renal DCE-MR images of 14 subjects without history of kidney diseases. Furthermore, the self-supervised strategy and the commonly used traditional unsupervised method were quantitatively compared with a reference manual segmentation by an experienced radiologist, using similarity indexes. RESULTS: The average Dice coefficient (ADC) for the segmentations of the proposed self-supervised method is 0.92 using a ransom walker model as the classifier or 0.86 using a K-nearest neighbor model as the classifier. The ADC of the Kmeans-based unsupervised methods with three and six clusters were 0.65 and 0.79, respectively. The Dice coefficients of the self-supervised method were remarkably higher than that of the unsupervised method (one-tailed paired-sample t-test, P-values <10-3 ). CONCLUSIONS: The results indicate that the proposed self-supervised approach yields a satisfactory similarity with the reference manual segmentation. Compared with the traditional unsupervised clustering method, the new strategy does not require manual intervention during the segmentation process and achieves better results for the segmentation of renal DCE-MR images.
PURPOSE: An automated accurate segmentation for dynamic contrast-enhanced magnetic resonance (DCE-MR) image sequences is essential for quantification of renal function. A self-supervised strategy is proposed for fully automatic segmentation of the renal DCE-MR images without using manually labeled data. METHODS: The proposed strategy employed both temporal and spatial information of the DCE-MR image sequences. First, the kidney area, the seed regions of the cortex, the medulla, and the pelvis were automatically detected in the spatial domain. Subsequently, all the pixels in the kidney were automatically labeled as the cortex, the medulla, or the pelvis based on their time-intensity signal and spatial position using a supervised classifier. The feasibility of the proposed strategy was verified on a dataset of renal DCE-MR images of 14 subjects without history of kidney diseases. Furthermore, the self-supervised strategy and the commonly used traditional unsupervised method were quantitatively compared with a reference manual segmentation by an experienced radiologist, using similarity indexes. RESULTS: The average Dice coefficient (ADC) for the segmentations of the proposed self-supervised method is 0.92 using a ransom walker model as the classifier or 0.86 using a K-nearest neighbor model as the classifier. The ADC of the Kmeans-based unsupervised methods with three and six clusters were 0.65 and 0.79, respectively. The Dice coefficients of the self-supervised method were remarkably higher than that of the unsupervised method (one-tailed paired-sample t-test, P-values <10-3 ). CONCLUSIONS: The results indicate that the proposed self-supervised approach yields a satisfactory similarity with the reference manual segmentation. Compared with the traditional unsupervised clustering method, the new strategy does not require manual intervention during the segmentation process and achieves better results for the segmentation of renal DCE-MR images.
Authors: Panagiotis Korfiatis; Aleksandar Denic; Marie E Edwards; Adriana V Gregory; Darryl E Wright; Aidan Mullan; Joshua Augustine; Andrew D Rule; Timothy L Kline Journal: J Am Soc Nephrol Date: 2021-12-07 Impact factor: 10.121
Authors: Francesca Inglese; Minseon Kim; Gerda M Steup-Beekman; Tom W J Huizinga; Mark A van Buchem; Jeroen de Bresser; Dae-Shik Kim; Itamar Ronen Journal: Front Neurosci Date: 2022-02-16 Impact factor: 4.677