Glioma segmentation and a simple accurate model for overall survival prediction

Evan Gates; J. Gregory Pauloski; Dawid Schellingerhout; David Fuentes

doi:10.1007/978-3-030-11726-9_42

Glioma segmentation and a simple accurate model for overall survival prediction

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Scopus citations

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 language	English (US)
Title of host publication	Brainlesion
Subtitle of host publication	Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers
Editors	Farahani Keyvan, Alessandro Crimi, Spyridon Bakas, Theo van Walsum, Mauricio Reyes, Hugo Kuijf
Publisher	Springer Verlag
Pages	476-484
Number of pages	9
ISBN (Print)	9783030117252
DOIs	https://doi.org/10.1007/978-3-030-11726-9_42
State	Published - Jan 1 2019
Event	4th 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 2018 → Sep 20 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11384 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	9/16/18 → 9/20/18

Keywords

Glioblastoma
Neural network
Quantitative imaging
Segmentation

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-11726-9_42

Cite this

Gates, E., Pauloski, J. G., Schellingerhout, D., & Fuentes, D. (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 et al.
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 proceeding › Conference contribution

Gates, E, Pauloski, JG, Schellingerhout, D & Fuentes, D 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 D. 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)). doi: 10.1007/978-3-030-11726-9_42

Gates, Evan ; Pauloski, J. Gregory ; Schellingerhout, Dawid et al. / 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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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.",

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Glioma segmentation and a simple accurate model for overall survival prediction

Abstract

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Keywords

ASJC Scopus subject areas

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