Yi Zhu1, Rong Wei1, Ge Gao2, Lian Ding1, Xiaodong Zhang2, Xiaoying Wang1,2, Jue Zhang1,3. 1. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, P.R. China. 2. Department of Radiology, Peking University First Hospital, Beijing, P.R. China. 3. College of Engineering, Peking University, Beijing, P.R. China.
Abstract
BACKGROUND: Computer-aided diagnosis (CAD) can aid radiologists in quantifying prostate cancer, and MRI segmentation plays an essential role in CAD applications. Clinical experience shows that prostate cancer occurs predominantly in the peripheral zone (PZ) and there exist different evaluation criteria for different regions in the Prostate Imaging Reporting and Data System (PI-RADS). PURPOSE: To develop a fully automatic approach to segmenting the prostate outer contour and the PZ contour with high efficacy. POPULATION: In all, 163 subjects, including 61 healthy subjects and 102 prostate cancer patients. For each subject, all slices that contained prostate gland in diffusion-weighted images (DWIs) and T2 -weighted images (T2 WIs) were selected as the datasets. FIELD STRENGTH: T2 -weighted, DWI at 3.0T. ASSESSMENT: The computer-generated segmentation results were compared with the manual outlining results that were depicted by two experts with more than 5 years' experience. Dice similarity coefficient (DSC), false-positive rate (FPR), and false-negative rate (FNR) were used to compared the algorithm and manual segmentation results. STATISTICAL TESTS: A paired t-test was adopted for comparison between our method and classical U-Net segmentation methods. RESULTS: The mean DSC was 92.7 ± 4.2% for the total whole prostate gland and 79.3 ± 10.4% for the total peripheral zone. Compared with classical U-Net segmentation methods, our segmentation precision was significantly higher (P < 0.001). DATA CONCLUSION: By cropping the region of interest and cascading the two networks, our method balances the positive and negative sample gradually, and results in higher segmentation accuracy. This fully automatic strategy could provide satisfactory performance in prostate DWIs and T2 WIs-based image segmentation. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2019;49:1149-1156.
BACKGROUND: Computer-aided diagnosis (CAD) can aid radiologists in quantifying prostate cancer, and MRI segmentation plays an essential role in CAD applications. Clinical experience shows that prostate cancer occurs predominantly in the peripheral zone (PZ) and there exist different evaluation criteria for different regions in the Prostate Imaging Reporting and Data System (PI-RADS). PURPOSE: To develop a fully automatic approach to segmenting the prostate outer contour and the PZ contour with high efficacy. POPULATION: In all, 163 subjects, including 61 healthy subjects and 102 prostate cancerpatients. For each subject, all slices that contained prostate gland in diffusion-weighted images (DWIs) and T2 -weighted images (T2 WIs) were selected as the datasets. FIELD STRENGTH: T2 -weighted, DWI at 3.0T. ASSESSMENT: The computer-generated segmentation results were compared with the manual outlining results that were depicted by two experts with more than 5 years' experience. Dice similarity coefficient (DSC), false-positive rate (FPR), and false-negative rate (FNR) were used to compared the algorithm and manual segmentation results. STATISTICAL TESTS: A paired t-test was adopted for comparison between our method and classical U-Net segmentation methods. RESULTS: The mean DSC was 92.7 ± 4.2% for the total whole prostate gland and 79.3 ± 10.4% for the total peripheral zone. Compared with classical U-Net segmentation methods, our segmentation precision was significantly higher (P < 0.001). DATA CONCLUSION: By cropping the region of interest and cascading the two networks, our method balances the positive and negative sample gradually, and results in higher segmentation accuracy. This fully automatic strategy could provide satisfactory performance in prostate DWIs and T2 WIs-based image segmentation. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2019;49:1149-1156.
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