Prostate MR image segmentation using a multi-stage network approach
Lars E. O. Jacobson, Mohamed Bader-El-Den, Lalit Maurya, Adrian A. Hopgood, Vincenzo Tamma, Shamsul K. Masum, David J. Prendergast, Peter Osborn
TL;DR
This paper presents a multi-stage deep learning approach to improve prostate cancer detection using MR images, enhancing diagnostic accuracy.
Contribution
The study introduces a multi-stage segmentation framework using deep learning to improve prostate boundary delineation in MR images.
Findings
The MultiResUNet model in a multi-stage framework significantly improved prostate boundary delineation.
The end-to-end two-stage method outperformed other segmentation strategies in diagnostic accuracy.
The approach was tested on a large dataset of over 61,000 T2-weighted MR images from 1151 patients.
Abstract
Prostate cancer (PCa) remains one of the most prevalent cancers among men, with over 1.4 million new cases and 375,304 deaths reported globally in 2020. Current diagnostic approaches, such as prostate-specific antigen (PSA) testing and trans-rectal ultrasound (TRUS)-guided biopsies, are often Limited by low specificity and accuracy. This study addresses these Limitations by leveraging deep learning-based image segmentation techniques on a dataset comprising 61,119 T2-weighted MR images from 1151 patients to enhance PCa detection and characterisation. A multi-stage segmentation approach, including one-stage, sequential two-stage, and end-to-end two-stage methods, was evaluated using various deep learning architectures. The MultiResUNet model, integrated into a multi-stage segmentation framework, demonstrated significant improvements in delineating prostate boundaries. The study utilised…
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Taxonomy
TopicsProstate Cancer Diagnosis and Treatment · Advanced Neural Network Applications · AI in cancer detection