%0 Journal Article %T Prostate cancer detection using residual networks %+ Laboratoire Traitement du Signal et de l'Image (LTSI) %+ Memorial Sloane Kettering Cancer Center [New York] %A Xu, Helen %A Baxter, John %A Akin, Oguz %A Cantor-Rivera, Diego %Z P30 CA008748, Memorial Sloan-Kettering Cancer Center %< avec comité de lecture %@ 1861-6410 %J International Journal of Computer Assisted Radiology and Surgery %I Springer Verlag %V 14 %N 10 %P 1647-1650 %8 2019-10 %D 2019 %R 10.1007/s11548-019-01967-5 %M 30972686 %K Deep learning %K Lesion segmentation %K Multi-parametric MRI %K Prostate cancer %Z Life Sciences [q-bio]/BioengineeringJournal articles %X 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. %G English %L hal-02378879 %U https://univ-rennes.hal.science/hal-02378879 %~ UNIV-RENNES1 %~ LTSI %~ STATS-UR1 %~ UR1-HAL %~ UR1-MATH-STIC %~ TEST-UR-CSS %~ UNIV-RENNES %~ UR1-MATH-NUM %~ UR1-BIO-SA