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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Acknowledgements
We would like to thank Dr. Fuad Nurili and Dr. Ismail Caymaz for their work in prostate segmentation.
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Dr. Oguz Akin receives support from Memorial Sloan Kettering Cancer Center Support Grant/Core Grant (P30 CA008748). Dr. Oguz Akin, Dr. Helen Xu and Dr. Diego Cantor-Rivera hold stock options and serve as scientific advisors for Ezra AI Inc., which is developing artificial intelligence algorithms related to the research being reported in this paper.
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Xu, H., Baxter, J.S.H., Akin, O. et al. Prostate cancer detection using residual networks. Int J CARS 14, 1647–1650 (2019). https://doi.org/10.1007/s11548-019-01967-5
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DOI: https://doi.org/10.1007/s11548-019-01967-5