Deep learning in magnetic resonance prostate segmentation: A review and a new perspective

• URL: http://arxiv.org/abs/2011.07795v1
• Date: Mon, 16 Nov 2020 08:58:38 GMT
• Title: Deep learning in magnetic resonance prostate segmentation: A review and a new perspective
• Authors: David Gillespie, Connah Kendrick, Ian Boon, Cheng Boon, Tim Rattay, Moi Hoon Yap
• Abstract summary: We review the state-of-the-art deep learning algorithms in MR prostate segmentation. We provide insights to the field by discussing their limitations and strengths. We propose an optimised 2D U-Net for MR prostate segmentation.
• Score: 4.453410156617238
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prostate radiotherapy is a well established curative oncology modality, which in future will use Magnetic Resonance Imaging (MRI)-based radiotherapy for daily adaptive radiotherapy target definition. However the time needed to delineate the prostate from MRI data accurately is a time consuming process. Deep learning has been identified as a potential new technology for the delivery of precision radiotherapy in prostate cancer, where accurate prostate segmentation helps in cancer detection and therapy. However, the trained models can be limited in their application to clinical setting due to different acquisition protocols, limited publicly available datasets, where the size of the datasets are relatively small. Therefore, to explore the field of prostate segmentation and to discover a generalisable solution, we review the state-of-the-art deep learning algorithms in MR prostate segmentation; provide insights to the field by discussing their limitations and strengths; and propose an optimised 2D U-Net for MR prostate segmentation. We evaluate the performance on four publicly available datasets using Dice Similarity Coefficient (DSC) as performance metric. Our experiments include within dataset evaluation and cross-dataset evaluation. The best result is achieved by composite evaluation (DSC of 0.9427 on Decathlon test set) and the poorest result is achieved by cross-dataset evaluation (DSC of 0.5892, Prostate X training set, Promise 12 testing set). We outline the challenges and provide recommendations for future work. Our research provides a new perspective to MR prostate segmentation and more importantly, we provide standardised experiment settings for researchers to evaluate their algorithms. Our code is available at https://github.com/AIEMMU/MRI\_Prostate.

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