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Improved 3D U-Net robustness against JPEG 2000 compression for male pelvic organ segmentation in radiotherapy

Improved 3D U-Net robustness against JPEG 2000 compression for male pelvic organ segmentation in... Abstract.Purpose: Automation of organ segmentation, via convolutional neural networks (CNNs), is key to facilitate the work of medical practitioners by ensuring that the adequate radiation dose is delivered to the target area while avoiding harmful exposure of healthy organs. The issue with CNNs is that they require large amounts of data transfer and storage which makes the use of image compression a necessity. Compression will affect image quality which in turn affects the segmentation process. We address the dilemma involved with handling large amounts of data while preserving segmentation accuracy.Approach: We analyze and improve 2D and 3D U-Net robustness against JPEG 2000 compression for male pelvic organ segmentation. We conduct three experiments on 56 cone beam computed tomography (CT) and 74 CT scans targeting bladder and rectum segmentation. The two objectives of the experiments are to compare the compression robustness of 2D versus 3D U-Net and to improve the 3D U-Net compression tolerance via fine-tuning.Results: We show that a 3D U-Net is 50% more robust to compression than a 2D U-Net. Moreover, by fine-tuning the 3D U-Net, we can double its compression tolerance compared to a 2D U-Net. Furthermore, we determine that fine-tuning the network to a compression ratio of 64:1 will ensure its flexibility to be used at compression ratios equal or lower.Conclusions: We reduce the potential risk involved with using image compression on automated organ segmentation. We demonstrate that a 3D U-Net can be fine-tuned to handle high compression ratios while preserving segmentation accuracy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Imaging SPIE

Improved 3D U-Net robustness against JPEG 2000 compression for male pelvic organ segmentation in radiotherapy

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Publisher
SPIE
Copyright
© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
ISSN
2329-4302
eISSN
2329-4310
DOI
10.1117/1.jmi.8.4.041207
Publisher site
See Article on Publisher Site

Abstract

Abstract.Purpose: Automation of organ segmentation, via convolutional neural networks (CNNs), is key to facilitate the work of medical practitioners by ensuring that the adequate radiation dose is delivered to the target area while avoiding harmful exposure of healthy organs. The issue with CNNs is that they require large amounts of data transfer and storage which makes the use of image compression a necessity. Compression will affect image quality which in turn affects the segmentation process. We address the dilemma involved with handling large amounts of data while preserving segmentation accuracy.Approach: We analyze and improve 2D and 3D U-Net robustness against JPEG 2000 compression for male pelvic organ segmentation. We conduct three experiments on 56 cone beam computed tomography (CT) and 74 CT scans targeting bladder and rectum segmentation. The two objectives of the experiments are to compare the compression robustness of 2D versus 3D U-Net and to improve the 3D U-Net compression tolerance via fine-tuning.Results: We show that a 3D U-Net is 50% more robust to compression than a 2D U-Net. Moreover, by fine-tuning the 3D U-Net, we can double its compression tolerance compared to a 2D U-Net. Furthermore, we determine that fine-tuning the network to a compression ratio of 64:1 will ensure its flexibility to be used at compression ratios equal or lower.Conclusions: We reduce the potential risk involved with using image compression on automated organ segmentation. We demonstrate that a 3D U-Net can be fine-tuned to handle high compression ratios while preserving segmentation accuracy.

Journal

Journal of Medical ImagingSPIE

Published: Jul 1, 2021

Keywords: U-Net segmentation; JPEG2000; convolutional neural networks; image compression; 3D medical imaging

References