A Cascaded Residual UNET for Fully Automated Segmentation of Prostate and Peripheral Zone in T2-weighted 3D Fast Spin Echo Images

• URL: http://arxiv.org/abs/2012.13501v1
• Date: Fri, 25 Dec 2020 03:16:52 GMT
• Title: A Cascaded Residual UNET for Fully Automated Segmentation of Prostate and Peripheral Zone in T2-weighted 3D Fast Spin Echo Images
• Authors: Lavanya Umapathy, Wyatt Unger, Faryal Shareef, Hina Arif, Diego Martin, Maria Altbach, and Ali Bilgin
• Abstract summary: Multi-parametric MR images have been shown to be effective in the non-invasive diagnosis of prostate cancer. We propose a fully automated cascaded deep learning architecture with residual blocks, Cascaded MRes-UNET, for segmentation of the prostate gland and the peripheral zone.
• Score: 1.6710577107094644
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multi-parametric MR images have been shown to be effective in the non-invasive diagnosis of prostate cancer. Automated segmentation of the prostate eliminates the need for manual annotation by a radiologist which is time consuming. This improves efficiency in the extraction of imaging features for the characterization of prostate tissues. In this work, we propose a fully automated cascaded deep learning architecture with residual blocks, Cascaded MRes-UNET, for segmentation of the prostate gland and the peripheral zone in one pass through the network. The network yields high Dice scores ($0.91\pm.02$), precision ($0.91\pm.04$), and recall scores ($0.92\pm.03$) in prostate segmentation compared to manual annotations by an experienced radiologist. The average difference in total prostate volume estimation is less than 5%.

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