Using planning CTs to enhance CNN-based bladder segmentation on Cone Beam CT

Brion, Eliott [UCL] Léger, Jean [UCL] Javaid, Umair [UCL] Lee, John Aldo [UCL] De Vleeschouwer, Christophe [UCL] Macq, Benoît [UCL]

For prostate cancer patients, large organ deformations occurring between the sessions of a fractionated radiotherapytreatment lead to uncertainties in the doses delivered to the tumour and the surrounding organs at risk. Thesegmentation of those structures in cone beam CT (CBCT) volumes acquired before every treatment sessionis desired to reduce those uncertainties. In this work, we perform a fully automatic bladder segmentation ofCBCT volumes with u-net, a 3D fully convolutional neural network (FCN). Since annotations are hard to collectfor CBCT volumes, we consider augmenting the training dataset with annotated CT volumes and show that itimproves the segmentation performance.Our network is trained and tested on 48 annotated CBCT volumes using a 6-fold cross-validation scheme.The network reaches a mean Dice similarity coefficient (DSC) of0.801±0.137 with 32 training CBCT volumes.This result improves to0.848±0.085 when the training set is augmented with 64 CT volumes. The segmentationaccuracy increases both with the number of CBCT and CT volumes in the training set. As a comparison, thestate-of-the-art deformable image registration (DIR) contour propagation between planning CT and daily CBCTavailable in RayStation reaches a DSC of0.744±0.144 on the same dataset, which is below our FCN result.