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"Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer"

Mohamed S. Elmahdy, Thyrza Jagt, Roel Th. Zinkstok, Yuchuan Qiao, Rahil Shazad, Hessam Sokooti, Sahar Yousefi, Luca Incrocci, Corrie A.M. Marijnen, Mischa Hoogeman and Marius Staring

Abstract

Purpose: To develop and validate a robust and accurate registration pipeline for automatic contour propagation for online adaptive Intensity-Modulated Proton Therapy (IMPT) of prostate cancer using elastix software and deep learning.

Methods: A 3D Convolutional Neural Network was trained for automatic bladder segmentation of the CT scans. The automatic bladder segmentation alongside the CT scan are jointly optimized to add explicit knowledge about the underlying anatomy to the registration algorithm. We included three datasets from different institutes and CT manufacturers. The first was used for training and testing the ConvNet, where the second and the third were used for evaluation of the proposed pipeline. The system performance was quantified geometrically using the Dice Similarity Coefficient (DSC), the Mean Surface Distance (MSD), and the 95% Hausdorff Distance (HD). The propagated contours were validated clinically through generating the associated IMPT plans and compare it with the IMPT plans based on the manual delineations. Propagated contours were considered clinically acceptable if their treatment plans met the dosimetric coverage constraints on the manual contours.

Results: The bladder segmentation network achieved a DSC of 88% and 82% on the test datasets. The proposed registration pipeline achieved a MSD of 1.29 ± 0.39, 1.48 ± 1.16, and 1.49 ± 0.44 mm for the prostate, seminal vesicles, and lymph nodes, respectively on the second dataset and a MSD of 2.31 ± 1.92 and 1.76 ± 1.39 mm for the prostate and seminal vesicles on the third dataset. The automatically propagated contours met the dose coverage constraints in 86%, 91%, and 99% of the cases for the prostate, seminal vesicles, and lymph nodes, respectively. A Conservative Success Rate (CSR) of 80% was obtained, compared to 65% when only using intensity-based registration.

Conclusions: The proposed registration pipeline obtained highly promising results for generating treatment plans adapted to the daily anatomy. With 80% of the automatically generated treatment plans directly usable without manual correction, a substantial improvement in system robustness was reached compared to a previous approach. The proposed method therefore facilitates more precise proton therapy of prostate cancer, potentially leading to fewer treatment related adverse side effects.

 

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Copyright © 2019 by the authors. Published version © 2019 by American Association of Physicists in Medicine (AAPM). Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

 

BibTeX entry

@article{Elmahdy:2019,
author = "{Mohamed S. Elmahdy and Thyrza Jagt and Roel Th. Zinkstok and Yuchuan Qiao and Rahil Shazad and Hessam Sokooti and Sahar Yousefi and Luca Incrocci and Corrie A.M. Marijnen and Mischa Hoogeman and Marius Staring}",
title = "{Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer}",
journal = "{Medical Physics}",
volume = "{46}",
number = "{8}",
pages = "{3329 - 3343}",
month = "{August}",
year = "{2019}",
}

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