A self‐supervised strategy for fully automatic segmentation of renal dynamic contrast‐enhanced magnetic resonance 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 da...
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| Published in: | Medical physics (Lancaster) Vol. 46; no. 10; pp. 4417 - 4430 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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United States
01.10.2019
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| Subjects: | |
| ISSN: | 0094-2405, 2473-4209, 2473-4209 |
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| 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. |
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| AbstractList | 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. 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.PURPOSEAn 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.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.METHODSThe 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.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 ).RESULTSThe 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 ).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.CONCLUSIONSThe 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. 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. 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. 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 ). 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. |
| Author | Huang, Wenjian Zhang, Xiaodong Wang, Xiaoying Li, Hao Zhang, Jue Wang, Rui |
| Author_xml | – sequence: 1 givenname: Wenjian surname: Huang fullname: Huang, Wenjian organization: Peking University – sequence: 2 givenname: Hao surname: Li fullname: Li, Hao organization: Peking University – sequence: 3 givenname: Rui surname: Wang fullname: Wang, Rui organization: Peking University First Hospital – sequence: 4 givenname: Xiaodong surname: Zhang fullname: Zhang, Xiaodong organization: Peking University First Hospital – sequence: 5 givenname: Xiaoying surname: Wang fullname: Wang, Xiaoying email: cjr.wangxiaoying@vip.163.com organization: Peking University First Hospital – sequence: 6 givenname: Jue surname: Zhang fullname: Zhang, Jue email: zhangjue@pku.edu.cn organization: Peking University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31306492$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3389_fnins_2022_695888 crossref_primary_10_1109_ACCESS_2021_3078430 crossref_primary_10_1371_journal_pone_0298227 crossref_primary_10_1016_j_ejrad_2021_109535 crossref_primary_10_1016_j_procs_2024_04_158 crossref_primary_10_1681_ASN_2021030404 crossref_primary_10_1016_j_cmpb_2022_107001 crossref_primary_10_1016_j_mric_2023_09_004 crossref_primary_10_3390_s21237942 |
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| Keywords | random walker DCE-MRI self-supervised algorithm unsupervised algorithm automatic kidney segmentation |
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An automated accurate segmentation for dynamic contrast‐enhanced magnetic resonance (DCE‐MR) image sequences is essential for quantification of renal... An automated accurate segmentation for dynamic contrast-enhanced magnetic resonance (DCE-MR) image sequences is essential for quantification of renal function.... |
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| SubjectTerms | automatic kidney segmentation Automation Contrast Media DCE‐MRI Female Humans Image Processing, Computer-Assisted - methods Kidney - diagnostic imaging Magnetic Resonance Imaging Male Middle Aged random walker self‐supervised algorithm Supervised Machine Learning unsupervised algorithm |
| Title | A self‐supervised strategy for fully automatic segmentation of renal dynamic contrast‐enhanced magnetic resonance images |
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