Glioma segmentation and a simple accurate model for overall survival prediction

Evan Gates, J. Gregory Pauloski, Dawid Schellingerhout, David Thomas Alfonso Fuentes

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

Abstract

Brain tumor segmentation is a challenging task necessary for quantitative tumor analysis and diagnosis. We apply a multi-scale convolutional neural network based on the DeepMedic to segment glioma subvolumes provided in the 2018 MICCAI Brain Tumor Segmentation Challenge. We go on to extract intensity and shape features from the images and cross-validate machine learning models to predict overall survival. Using only the mean FLAIR intensity, nonenhancing tumor volume, and patient age we are able to predict patient overall survival with reasonable accuracy.

Original languageEnglish (US)
Title of host publicationBrainlesion
Subtitle of host publicationGlioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers
EditorsFarahani Keyvan, Alessandro Crimi, Spyridon Bakas, Theo van Walsum, Mauricio Reyes, Hugo Kuijf
PublisherSpringer Verlag
Pages476-484
Number of pages9
ISBN (Print)9783030117252
DOIs
StatePublished - Jan 1 2019
Event4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018 - Granada, Spain
Duration: Sep 16 2018Sep 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11384 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018
CountrySpain
CityGranada
Period9/16/189/20/18

Fingerprint

Brain Tumor
Tumors
Tumor
Segmentation
Predict
Shape Feature
Prediction
Brain
Machine Learning
Neural Networks
Necessary
Model
Learning systems
Neural networks

Keywords

  • Glioblastoma
  • Neural network
  • Quantitative imaging
  • Segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Gates, E., Pauloski, J. G., Schellingerhout, D., & Fuentes, D. T. A. (2019). Glioma segmentation and a simple accurate model for overall survival prediction. In F. Keyvan, A. Crimi, S. Bakas, T. van Walsum, M. Reyes, & H. Kuijf (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers (pp. 476-484). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11384 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-11726-9_42

Glioma segmentation and a simple accurate model for overall survival prediction. / Gates, Evan; Pauloski, J. Gregory; Schellingerhout, Dawid; Fuentes, David Thomas Alfonso.

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers. ed. / Farahani Keyvan; Alessandro Crimi; Spyridon Bakas; Theo van Walsum; Mauricio Reyes; Hugo Kuijf. Springer Verlag, 2019. p. 476-484 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11384 LNCS).

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

Gates, E, Pauloski, JG, Schellingerhout, D & Fuentes, DTA 2019, Glioma segmentation and a simple accurate model for overall survival prediction. in F Keyvan, A Crimi, S Bakas, T van Walsum, M Reyes & H Kuijf (eds), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11384 LNCS, Springer Verlag, pp. 476-484, 4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018, Granada, Spain, 9/16/18. https://doi.org/10.1007/978-3-030-11726-9_42
Gates E, Pauloski JG, Schellingerhout D, Fuentes DTA. Glioma segmentation and a simple accurate model for overall survival prediction. In Keyvan F, Crimi A, Bakas S, van Walsum T, Reyes M, Kuijf H, editors, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers. Springer Verlag. 2019. p. 476-484. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-11726-9_42
Gates, Evan ; Pauloski, J. Gregory ; Schellingerhout, Dawid ; Fuentes, David Thomas Alfonso. / Glioma segmentation and a simple accurate model for overall survival prediction. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers. editor / Farahani Keyvan ; Alessandro Crimi ; Spyridon Bakas ; Theo van Walsum ; Mauricio Reyes ; Hugo Kuijf. Springer Verlag, 2019. pp. 476-484 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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