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Journal of Imaging Informatics in Medicine

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01-10-2019

A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images

Authors: Ruba Alkadi, Fatma Taher, Ayman El-baz, Naoufel Werghi

Published in: Journal of Imaging Informatics in Medicine | Issue 5/2019

Abstract

We address the problem of prostate lesion detection, localization, and segmentation in T2W magnetic resonance (MR) images. We train a deep convolutional encoder-decoder architecture to simultaneously segment the prostate, its anatomical structure, and the malignant lesions. To incorporate the 3D contextual spatial information provided by the MRI series, we propose a novel 3D sliding window approach, which preserves the 2D domain complexity while exploiting 3D information. Experiments on data from 19 patients provided for the public by the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) show that our approach outperforms traditional pattern recognition and machine learning approaches by a significant margin. Particularly, for the task of cancer detection and localization, the system achieves an average AUC of 0.995, an accuracy of 0.894, and a recall of 0.928. The proposed mono-modal deep learning-based system performs comparably to other multi-modal MR-based systems. It could improve the performance of a radiologist in prostate cancer diagnosis and treatment planning.

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Title: A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images
Authors: Ruba Alkadi
Fatma Taher
Ayman El-baz
Naoufel Werghi
Publication date: 01-10-2019
Publisher: Springer International Publishing
Published in: Journal of Imaging Informatics in Medicine / Issue 5/2019
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-018-0160-1

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A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images

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