An integrative analysis of image segmentation and survival of brain tumour patients

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

Abstract

Our contribution to the BraTS 2019 challenge consisted of a deep learning based approach for segmentation of brain tumours from MR images using cross validation ensembles of 2D-UNet models. Furthermore, different approaches for the prediction of patient survival time using clinical as well as imaging features were investigated. A simple linear regression model using patient age and tumour volumes outperformed more elaborate approaches like convolutional neural networks or radiomics-based analysis with an accuracy of 0.55 on the validation cohort and 0.51 on the test cohort.

Details

Original languageEnglish
Title of host publicationBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
EditorsAlessandro Crimi, Spyridon Bakas
PublisherSpringer, Berlin [u. a.]
Pages368-378
Number of pages11
ISBN (electronic)978-3-030-46640-4
ISBN (print)978-3-030-46639-8
Publication statusPublished - 2020
Peer-reviewedYes

Publication series

SeriesLecture Notes in Computer Science, Volume 11992
ISSN0302-9743

Conference

Title5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019
Duration17 October 2019
CityShenzhen
CountryChina

External IDs

ORCID /0000-0002-7017-3738/work/146646030
ORCID /0000-0002-4590-1908/work/163293974

Keywords

Keywords

  • Deep-learning, Ensemble, Linear regression, Radiomics, Segmentation, Survival analysis, UNet

Library keywords