Deep Supervoxel Segmentation for Survival Analysis in Head and Neck Cancer Patients

Angel Victor Juanco Muller, João F. C. Mota, Keith Goatman, Corné Hoogendoorn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (SciVal)

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 (≥10 GB/epoch). In this work, we propose an efficient solution (∼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.
Original languageEnglish
Title of host publicationHead and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2021
EditorsVincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge
PublisherSpringer
Pages257–265
Number of pages9
ISBN (Electronic)9783030982539
ISBN (Print)9783030982522
DOIs
Publication statusPublished - 13 Mar 2022

Publication series

NameLecture Notes in Computer Science
Volume13209
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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