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

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

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

OriginalspracheEnglisch
TitelBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Redakteure/-innenAlessandro Crimi, Spyridon Bakas
Herausgeber (Verlag)Springer, Berlin [u. a.]
Seiten368-378
Seitenumfang11
ISBN (elektronisch)978-3-030-46640-4
ISBN (Print)978-3-030-46639-8
PublikationsstatusVeröffentlicht - 2020
Peer-Review-StatusJa

Publikationsreihe

ReiheLecture Notes in Computer Science, Volume 11992
ISSN0302-9743

Konferenz

Titel5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019
Dauer17 Oktober 2019
StadtShenzhen
LandChina

Externe IDs

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

Schlagworte

Schlagwörter

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

Bibliotheksschlagworte