Using planning CTs to enhance CNN-based bladder segmentation on cone beam CT

Eliott Brion; Jean Léger; Umair Javaid; John Lee; Christophe De Vleeschouwer; Benoit Macq

doi:10.1117/12.2512791

8 March 2019 Using planning CTs to enhance CNN-based bladder segmentation on cone beam CT

Eliott Brion, Jean Léger, Umair Javaid, John Lee, Christophe De Vleeschouwer, Benoit Macq

Proceedings Volume 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling; 109511M (2019) https://doi.org/10.1117/12.2512791
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

For prostate cancer patients, large organ deformations occurring between the sessions of a fractionated radiotherapy treatment lead to uncertainties in the doses delivered to the tumour and the surrounding organs at risk. The segmentation of those structures in cone beam CT (CBCT) volumes acquired before every treatment session is desired to reduce those uncertainties. In this work, we perform a fully automatic bladder segmentation of CBCT volumes with u-net, a 3D fully convolutional neural network (FCN). Since annotations are hard to collect for CBCT volumes, we consider augmenting the training dataset with annotated CT volumes and show that it improves 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) of 0:801 ± 0:137 with 32 training CBCT volumes. This result improves to 0:848 ± 0:085 when the training set is augmented with 64 CT volumes. The segmentation accuracy increases both with the number of CBCT and CT volumes in the training set. As a comparison, the state-of-the-art deformable image registration (DIR) contour propagation between planning CT and daily CBCT available in RayStation reaches a DSC of 0:744 ± 0:144 on the same dataset, which is below our FCN result.

Conference Presentation

Citation Download Citation

Eliott Brion, Jean Léger, Umair Javaid, John Lee, Christophe De Vleeschouwer, and Benoit Macq "Using planning CTs to enhance CNN-based bladder segmentation on cone beam CT", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109511M (8 March 2019); https://doi.org/10.1117/12.2512791

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