Evaluation of Multi-metric Registration for Online Adaptive Proton Therapy of Prostate Cancer
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 \(92.7\% \pm 1.6\%\) for automatic bladder segmentation. For the automatic contour propagation of the prostate, lymph nodes, and seminal vesicles, the system achieved a DSC of \(0.87\pm 0.03\), \(0.89\pm 0.02\), and \(0.67\pm 0.11\) and Mean Surface Distance of \(1.4\pm 0.30\) mm, \(1.4\pm 0.29\) mm, and \(1.5 \pm 0.37\) mm, respectively. The proposed algorithm is therefore very promising for clinical implementation in the context of online adaptive proton therapy of prostate cancer.
KeywordsDeformable image registration Convolutional neural networks (CNN) Prostate cancer Proton therapy
This study was financially supported by ZonMw, the Netherlands Organization for Health Research and Development, grant number 104003012. The CT-data with contours were collected at Haukeland University Hospital, Bergen, Norway and were provided to us by responsible oncologist Svein Inge Helle and physicist Liv Bolstad Hysing; they are gratefully acknowledged.
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