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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

10.04.2019 | Short communication

Prostate cancer detection using residual networks

verfasst von: Helen Xu, John S. H. Baxter, Oguz Akin, Diego Cantor-Rivera

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 10/2019

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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.

http://mathworld.wolfram.com/DiskPointPicking.html.

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Titel: Prostate cancer detection using residual networks
verfasst von: Helen Xu
John S. H. Baxter
Oguz Akin
Diego Cantor-Rivera
Publikationsdatum: 10.04.2019
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 10/2019
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-019-01967-5

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Prostate cancer detection using residual networks

Abstract

Purpose

Methods

Results

Conclusion

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Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

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Abstract

Purpose

Methods

Results

Conclusion

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