Helen Xu1, John S H Baxter2, Oguz Akin3, Diego Cantor-Rivera4. 1. Ezra AI Canada, Unit 310, 545 King St. West, Toronto, Canada. 2. Université de Rennes 1, Rennes, France. 3. Memorial Sloan Kettering Cancer Center, New York, NY, USA. 4. Ezra AI Canada, Unit 310, 545 King St. West, Toronto, Canada. diego@ezra.ai.
Abstract
PURPOSE: To automatically identify regions where prostate cancer is suspected on multi-parametric magnetic resonance images (mp-MRI). METHODS: A residual network was implemented based on segmentations from an expert radiologist on T2-weighted, apparent diffusion coefficient map, and high b-value diffusion-weighted images. Mp-MRIs from 346 patients were used in this study. RESULTS: The residual network achieved a hit or miss accuracy of 93% for lesion detection, with an average Jaccard score of 71% that compared the agreement between network and radiologist segmentations. CONCLUSION: This paper demonstrated the ability for residual networks to learn features for prostate lesion segmentation.
PURPOSE: To automatically identify regions where prostate cancer is suspected on multi-parametric magnetic resonance images (mp-MRI). METHODS: A residual network was implemented based on segmentations from an expert radiologist on T2-weighted, apparent diffusion coefficient map, and high b-value diffusion-weighted images. Mp-MRIs from 346 patients were used in this study. RESULTS: The residual network achieved a hit or miss accuracy of 93% for lesion detection, with an average Jaccard score of 71% that compared the agreement between network and radiologist segmentations. CONCLUSION: This paper demonstrated the ability for residual networks to learn features for prostate lesion segmentation.
Entities:
Keywords:
Deep learning; Lesion segmentation; Multi-parametric MRI; Prostate cancer
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