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Automatic zonal segmentation of the prostate from 2D and 3D T2-weighted MRI and evaluation for clinical use

Automatic zonal segmentation of the prostate from 2D and 3D T2-weighted MRI and evaluation for... Abstract.Purpose: An accurate zonal segmentation of the prostate is required for prostate cancer (PCa) management with MRI.Approach: The aim of this work is to present UFNet, a deep learning-based method for automatic zonal segmentation of the prostate from T2-weighted (T2w) MRI. It takes into account the image anisotropy, includes both spatial and channelwise attention mechanisms and uses loss functions to enforce prostate partition. The method was applied on a private multicentric three-dimensional T2w MRI dataset and on the public two-dimensional T2w MRI dataset ProstateX. To assess the model performance, the structures segmented by the algorithm on the private dataset were compared with those obtained by seven radiologists of various experience levels.Results: On the private dataset, we obtained a Dice score (DSC) of 93.90  ±  2.85 for the whole gland (WG), 91.00  ±  4.34 for the transition zone (TZ), and 79.08  ±  7.08 for the peripheral zone (PZ). Results were significantly better than other compared networks’ (p-value  <  0.05). On ProstateX, we obtained a DSC of 90.90  ±  2.94 for WG, 86.84  ±  4.33 for TZ, and 78.40  ±  7.31 for PZ. These results are similar to state-of-the art results and, on the private dataset, are coherent with those obtained by radiologists. Zonal locations and sectorial positions of lesions annotated by radiologists were also preserved.Conclusions: Deep learning-based methods can provide an accurate zonal segmentation of the prostate leading to a consistent zonal location and sectorial position of lesions, and therefore can be used as a helping tool for PCa diagnosis. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Imaging SPIE

Automatic zonal segmentation of the prostate from 2D and 3D T2-weighted MRI and evaluation for clinical use

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Publisher
SPIE
Copyright
© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)
ISSN
2329-4302
eISSN
2329-4310
DOI
10.1117/1.jmi.9.2.024001
Publisher site
See Article on Publisher Site

Abstract

Abstract.Purpose: An accurate zonal segmentation of the prostate is required for prostate cancer (PCa) management with MRI.Approach: The aim of this work is to present UFNet, a deep learning-based method for automatic zonal segmentation of the prostate from T2-weighted (T2w) MRI. It takes into account the image anisotropy, includes both spatial and channelwise attention mechanisms and uses loss functions to enforce prostate partition. The method was applied on a private multicentric three-dimensional T2w MRI dataset and on the public two-dimensional T2w MRI dataset ProstateX. To assess the model performance, the structures segmented by the algorithm on the private dataset were compared with those obtained by seven radiologists of various experience levels.Results: On the private dataset, we obtained a Dice score (DSC) of 93.90  ±  2.85 for the whole gland (WG), 91.00  ±  4.34 for the transition zone (TZ), and 79.08  ±  7.08 for the peripheral zone (PZ). Results were significantly better than other compared networks’ (p-value  <  0.05). On ProstateX, we obtained a DSC of 90.90  ±  2.94 for WG, 86.84  ±  4.33 for TZ, and 78.40  ±  7.31 for PZ. These results are similar to state-of-the art results and, on the private dataset, are coherent with those obtained by radiologists. Zonal locations and sectorial positions of lesions annotated by radiologists were also preserved.Conclusions: Deep learning-based methods can provide an accurate zonal segmentation of the prostate leading to a consistent zonal location and sectorial position of lesions, and therefore can be used as a helping tool for PCa diagnosis.

Journal

Journal of Medical ImagingSPIE

Published: Mar 1, 2022

Keywords: prostate; segmentation; deep learning; lesion; magnetic resonance imaging; inter-rater variability

References