Some New Tricks for Deep Glioma Segmentation

Chase Duncan, Francis Roxas, Neel Jani, Jane Maksimovic, Matthew Bramlet, Brad Sutton, Sanmi Koyejo

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

Abstract

This manuscript outlines the design of methods, and initial progress on automatic detection of glioma from MRI images using deep neural networks, all applied and evaluated for the 2020 Brain Tumor Segmentation (BraTS) Challenge. Our approach builds on existing work using U-net architectures, and evaluates a variety deep learning techniques including model averaging and adaptive learning rates.

Original languageEnglish (US)
Title of host publicationBrainlesion
Subtitle of host publicationGlioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers
EditorsAlessandro Crimi, Spyridon Bakas
PublisherSpringer
Pages320-330
Number of pages11
ISBN (Print)9783030720865
DOIs
StatePublished - 2021
Event6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020 - Virtual, Online
Duration: Oct 4 2020Oct 4 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12659 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020
CityVirtual, Online
Period10/4/2010/4/20

Keywords

  • Deep learning
  • Glioma segmentation
  • Learning rates
  • Uncertainty in glioma segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'Some New Tricks for Deep Glioma Segmentation'. Together they form a unique fingerprint.

Cite this