Mr. Kun Cheng Profile

Kun Cheng

at Edinburgh Cancer Research UK Ctr

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Proceedings Article | 12 March 2014 Paper

Active shape models with optimised texture features for radiotherapy

K. Cheng, D. Montgomery, F. Yang, D. McLaren, S. McLaughlin, W. Nailon

Proceedings Volume 9036, 90362G (2014) https://doi.org/10.1117/12.2043584

KEYWORDS: Tumor growth modeling, Prostate, Computed tomography, Radiotherapy, 3D modeling, Mahalanobis distance, Shape analysis, Image segmentation, Prostate cancer, Performance modeling

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There is now considerable interest in radiation oncology on the use of shape models of anatomy to improve target delineation and assess anatomical disparity at time of radiotherapy. In this paper a texture based active shape model (ASM) is presented for automatic delineation of the gross tumor volume (GTV), containing the prostate, on computed tomography (CT) images of prostate cancer patients. The model was trained on two-dimensional (2D) contours identified by a radiation oncologist on sequential CT image slices. A three-dimensional (3D) GTV shape was constructed from these and iteratively aligned using Procrustes analysis. To train the model the shape deformation variance was learnt using the Active Shape Model (ASM) approach. In a novel development to this approach a profile feature was selected from pre-computed texture features by minimizing the Mahalanobis distance to obtain the most distinct feature for each landmark. The interior of the GTV was modelled using quantile histograms to initialize the shape model on new cases. From the archive of 42 cases of contoured CT scans, 32 cases were randomly selected for training the model and 10 cases for evaluating performance. The gold standard was defined by the radiation oncologist. The shape model achieved an overall Dice coefficient of 0.81 for all test cases. Performance was found to increase, mean Dice coefficient of 0.87, when the volume size of the new case was similar to the mean shape of the model. With further work the approach has the potential to be used in real-time delineation of target volumes and improve segmentation accuracy.

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