Measuring Efficiency of Semi-automated Brain Tumor Segmentation by Simulating User Interaction

Measuring Efficiency of Semi-automated Brain Tumor Segmentation by Simulating User Interaction

Traditionally, radiologists have crudely quantified tumor extent by measuring the longest and shortest dimension by dragging a cursor between opposite boundary points across a single image rather than full segmentation of the volumetric extent. For algorithmic-based volumetric segmentation, the degr...

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Bibliographic Details
Main Authors: David Gering, Aikaterini Kotrotsou, Brett Young-Moxon, Neal Miller, Aaron Avery, Lisa Kohli, Haley Knapp, Jeffrey Hoffman, Roger Chylla, Linda Peitzman, Thomas R. Mackie
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-04-01
Series:Frontiers in Computational Neuroscience
Subjects:
brain MRI
tumor
segmentation
glioma
deep learning
efficiency
Online Access:https://www.frontiersin.org/article/10.3389/fncom.2020.00032/full

Internet

https://www.frontiersin.org/article/10.3389/fncom.2020.00032/full

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