ProGNet: prostate gland segmentation on MRI with deep learning

Simon John Christoph Soerensen; Richard Fan; Arun Seetharaman; Leo Chen; Wei Shao; Indrani Bhattacharya; Michael Borre; Benjamin Chung; Katherine To’o; Geoffrey Sonn; Mirabela Rusu

doi:10.1117/12.2580448

15 February 2021 ProGNet: prostate gland segmentation on MRI with deep learning

Simon John Christoph Soerensen, Richard Fan, Arun Seetharaman, Leo Chen, Wei Shao, Indrani Bhattacharya, Michael Borre, Benjamin Chung, Katherine To’o, Geoffrey Sonn, Mirabela Rusu

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115962R (2021) https://doi.org/10.1117/12.2580448
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

The use of magnetic resonance-ultrasound fusion targeted biopsy improves diagnosis of aggressive prostate cancer. Fusion of ultrasound & magnetic resonance images (MRI) requires accurate prostate segmentations. In this paper, we developed a 2.5 dimensional deep learning model, ProGNet, to segment the prostate on T2-weighted magnetic resonance imaging (MRI). ProGNet is an optimized U-Net model that weighs three adjacent slices in each MRI sequence to segment the prostate in a 2.5D context. We trained ProGNet on 529 cases where experts annotated the whole gland (WG) on axial T2-weighted MRI prior to targeted prostate biopsy. In 132 cases, experts also annotated the central gland (CG) on MRI. After five-fold cross-validation, we found that for WG segmentation, ProGNet had a mean Dice similarity coefficient (DSC) of 0.91±0.02, sensitivity of 0.89±0.03, specificity of 0.97±0.00, and an accuracy of 0.95±0.01. For CG segmentation, ProGNet achieved a mean DSC 0.86±0.01, sensitivity of 0.84±0.03, specificity of 0.99±0.01, and an accuracy of 0.96±0.01. We then tested the generalizability of the model on the 60-case NCI-ISBI 2013 challenge dataset and on a local, independent 61-case test set. We achieved DSCs of 0.81±0.02 and 0.72±0.02 for WG and CG segmentation on the NCI-ISBI 2013 challenge dataset, and 0.83±0.01 and 0.75±0.01 for WG and CG segmentation on the local dataset. Model performance was excellent and outperformed state-of-art U-Net and holistically-nested edge detector (HED) networks in all three datasets.

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Simon John Christoph Soerensen, Richard Fan, Arun Seetharaman, Leo Chen, Wei Shao, Indrani Bhattacharya, Michael Borre, Benjamin Chung, Katherine To’o, Geoffrey Sonn, and Mirabela Rusu "ProGNet: prostate gland segmentation on MRI with deep learning", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962R (15 February 2021); https://doi.org/10.1117/12.2580448

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