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"Evaluation of multi-metric registration for online adaptive proton therapy of prostate cancer"

Mohamed S. Elmahdy, Thyrza Jagt, Mischa S. Hoogeman, Roel Zinkstok and Marius Staring

Abstract

Delineation of the target volume and Organs-At-Risk (OARs) is a crucial step for proton therapy dose planning of prostate cancer. Adaptive proton therapy mandates automatic delineation, as manual delineation is too time consuming while it should be fast and robust. In this study, we propose an accurate and robust automatic propagation of the delineations from the planning CT to the daily CT by means of Deformable Image Registration (DIR). The proposed algorithm is a multi-metric DIR method that jointly optimizes the registration of the bladder contours and CT images. A 3D Dilated Convolutional Neural Network (DCNN) was trained for automatic bladder segmentation of the daily CT. The network was trained and tested on prostate data of 18 patients, each having 7 to 10 daily CT scans. The network achieved a Dice Similarity Coefficient (DSC) of 92.7% ± 1.6% for automatic bladder segmentation. For the automatic contour propagation of the prostate, lymph nodes, and seminal vesicles, the system achieved a DSC of 0.87 ± 0.03, 0.89 ± 0.02, and 0.67 ± 0.11 and Mean Surface Distance of 1.4 ± 0.30 mm, 1.4 ± 0.29 mm, and 1.5 ± 0.37 mm, respectively. The proposed algorithm is therefore very promising for clinical implementation in the context of online adaptive proton therapy of prostate cancer.

 

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Copyright © 2018 by the authors. Published version © 2018 by Springer Lecture Notes in Computer Science. 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

@inproceedings{Elmahdy:2018,
author = "{Mohamed S. Elmahdy and Thyrza Jagt and Mischa S. Hoogeman and Roel Zinkstok and Marius Staring}",
title = "{Evaluation of multi-metric registration for online adaptive proton therapy of prostate cancer}",
booktitle = "{International Workshop on Biomedical Image Registration (WBIR)}",
editor = "{Stefan Klein and Marius Staring and Stanley Durrleman and Stefan Horst Sommer}",
address = "{Leiden, The Netherlands}",
series = "{Lecture Notes in Computer Science}",
volume = "{10883}",
pages = "{94 - 104}",
month = "{June}",
year = "{2018}",
}

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