期刊
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
卷 14, 期 10, 页码 1647-1650出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s11548-019-01967-5
关键词
Prostate cancer; Multi-parametric MRI; Lesion segmentation; Deep learning
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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