Abstract
Risk assessment techniques, in particular Survival Analysis, are crucial to provide personalised treatment to Head and Neck (H&N) cancer patients. These techniques usually rely on accurate segmentation of the Gross Tumour Volume (GTV) region in Computed Tomography (CT) and Positron Emission Tomography (PET) images . This is a challenging task due to the low contrast in CT and lack of anatomical information in PET. Recent approaches based on Convolutional Neural Networks (CNNs) have demonstrated automatic 3D segmentation of the GTV, albeit with high memory footprints (\({\ge }10\) GB/epoch). In this work, we propose an efficient solution (\({\sim }3\) GB/epoch) for the segmentation task in the HECKTOR 2021 challenge. We achieve this by combining the Simple Linear Iterative Clustering (SLIC) algorithm with Graph Convolution Networks to segment the GTV, resulting in a Dice score of 0.63 on the challenge test set. Furthermore, we demonstrate how shape descriptors of the resulting segmentations are relevant covariates in the Weibull Accelerated Failure Time model, which results in a Concordance Index of 0.59 for task 2 in the HECKTOR 2021 challenge.
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Juanco-Müller, Á.V., Mota, J.F.C., Goatman, K., Hoogendoorn, C. (2022). Deep Supervoxel Segmentation for Survival Analysis in Head and Neck Cancer Patients. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2021. Lecture Notes in Computer Science, vol 13209. Springer, Cham. https://doi.org/10.1007/978-3-030-98253-9_24
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DOI: https://doi.org/10.1007/978-3-030-98253-9_24
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