Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation

Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation

PurposeThis work investigates the use of a spherical projection-based U-Net (SPU-Net) segmentation model to improve meningioma segmentation performance and allow for uncertainty quantification.MethodsA total of 76 supratentorial meningioma patients treated with radiotherapy were studied. Gross tumor...

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Bibliographic Details
Main Authors: Lana Wang, Zhenyu Yang, Dominic LaBella, Zachary Reitman, John Ginn, Jingtong Zhao, Justus Adamson, Kyle Lafata, Evan Calabrese, John Kirkpatrick, Chunhao Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Oncology
Subjects:
meningioma
uncertainty quantification
radiation therapy
deep learning
auto-segmentation
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2025.1474590/full
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https://www.frontiersin.org/articles/10.3389/fonc.2025.1474590/full

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