Automatic Head and Neck Tumor Segmentation and Progression Free Survival Analysis on PET/CT Images

Yading Yuan, Saba Adabi, Xuefeng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Automatic segmentation is an essential but challenging step for extracting quantitative imaging bio-markers for characterizing head and neck tumor in tumor detection, diagnosis, prognosis, treatment planning and assessment. The HEad and neCK TumOR Segmentation Challenge 2021 (HECKTOR 2021) provides a common platform for the following three tasks: 1) the automatic segmentation of the primary gross target volume (GTV) in the oropharynx region on FDG-PET and CT images; 2) the prediction of patient outcomes, namely Progression Free Survival (PFS) from the FDG-PET/CT images with automatic segmentation results and the available clinical data; and 3) the prediction of PFS with ground truth annotations. We participated in the first two tasks by further evaluating a fully automatic segmentation network based on encoder-decoder architecture. In order to better integrate information across different scales, we proposed a dynamic scale attention mechanism that incorporates low-level details with high-level semantics from feature maps at different scales. Radiomic features were extracted from the segmented tumors and used for PFS prediction. Our segmentation framework was trained using the 224 challenge training cases provided by HECKTOR 2021, and achieved an average Dice Similarity Coefficient (DSC) of 0.7693 with cross validation. By testing on the 101 testing cases, our model achieved an average DSC of 0.7608 and 95 % Hausdorff distance of 3.27 mm. The overall PFS prediction yielded a concordance index (c-index) of 0.53 on the testing dataset (id: deepX).

Original languageEnglish
Title of host publicationHead and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings
EditorsVincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge
PublisherSpringer Science and Business Media Deutschland GmbH
Pages179-188
Number of pages10
ISBN (Print)9783030982522
DOIs
StatePublished - 2022
Event2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 27 Sep 202127 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13209 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period27/09/2127/09/21

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