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International Journal of Computer Assisted Radiology and Surgery

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01-10-2019 | Prostate Cancer | Short communication

Prostate cancer detection using residual networks

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

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 10/2019

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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Title: Prostate cancer detection using residual networks
Authors: Helen Xu
John S. H. Baxter
Oguz Akin
Diego Cantor-Rivera
Publication date: 01-10-2019
Publisher: Springer International Publishing
Keywords: Prostate Cancer
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Prostate Cancer
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 10/2019
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-019-01967-5

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Prostate cancer detection using residual networks

Abstract

Purpose

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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