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Prostate cancer detection using residual networks

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.
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Contributor : Laurent Jonchère <>
Submitted on : Monday, November 25, 2019 - 2:05:51 PM
Last modification on : Thursday, January 14, 2021 - 11:25:12 AM

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Helen Xu, John Baxter, Oguz Akin, Diego Cantor-Rivera. Prostate cancer detection using residual networks. International Journal of Computer Assisted Radiology and Surgery, Springer Verlag, 2019, 14 (10), pp.1647-1650. ⟨10.1007/s11548-019-01967-5⟩. ⟨hal-02378879⟩



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