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.
Keywords: Deep learning; Lesion segmentation; Multi-parametric MRI; Prostate cancer.