Impact of uncertainty quantification through conformal prediction on volume assessment from deep learning-based MRI prostate segmentation

Impact of uncertainty quantification through conformal prediction on volume assessment from deep learning-based MRI prostate segmentation

Abstract Objectives To estimate the uncertainty of a deep learning (DL)-based prostate segmentation algorithm through conformal prediction (CP) and to assess its effect on the calculation of the prostate volume (PV) in patients at risk of prostate cancer (PC). Methods Three-hundred seventy-seven mul...

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Bibliographic Details
Main Authors: Marius Gade, Kevin Mekhaphan Nguyen, Sol Gedde, Alvaro Fernandez-Quilez
Format: Article
Language:English
Published: SpringerOpen 2024-11-01
Series:Insights into Imaging
Subjects:
Magnetic resonance imaging
Deep learning
Prostate
Conformal prediction
Uncertainty
Online Access:https://doi.org/10.1186/s13244-024-01863-w
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https://doi.org/10.1186/s13244-024-01863-w

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