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Journal of Digital Imaging

, Volume 32, Issue 5, pp 793–807 | Cite as

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

  • Ruba AlkadiEmail author
  • Fatma Taher
  • Ayman El-baz
  • Naoufel Werghi
Article

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.

Keywords

Magnetic resonance imaging Prostate cancer Deep convolutional encoder-decoder 

Notes

Acknowledgements

The authors would also like to thank Dr. Waleed Hassen and Dr. Eric Vens from Cleveland Clinic, Abu Dhabi, and Dr. Salah El-Rai from Sheikh Khalifa General Hospital for their support and collaboration.

Funding Information

This work is support by a research grant from Al-Jalila foundation ref. AJF-201616.

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Society for Imaging Informatics in Medicine 2018

Authors and Affiliations

  1. 1.Khalifa University of Science and TechnologyAbu DhabiUnited Arab Emirates
  2. 2.University of LouisvilleLouisvilleUSA

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