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Segmenting with confidence through uncertainty quantification for brain tumor imaging.

Yassine Guennoun¹, Pierre Nedelec², Mark McArthur²

¹Center for Intelligent Imaging (ci2), Department of Radiology & Biomedical Imaging, University of California San Francisco (UCSF), San Francisco, CA, USA. yassineguennoun02@gmail.com.

NPJ Digital Medicine

|June 19, 2026

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This summary is machine-generated.

This study introduces a deep learning framework for brain tumor segmentation, providing reliable uncertainty estimates to enhance clinician trust in artificial intelligence (AI) tools for meningioma monitoring.

Area of Science:

Neuroimaging
Artificial Intelligence
Medical Diagnostics

Background:

Clinical adoption of AI for brain tumor monitoring is hindered by uncalibrated uncertainty in automated segmentation, impacting clinician trust.
Accurate segmentation and reliable uncertainty quantification are crucial for safe and effective AI deployment in neuro-oncology.

Purpose of the Study:

To develop and evaluate a deep learning framework for generating calibrated uncertainty estimates in meningioma segmentation on brain MRI.
To improve clinician trust and facilitate the clinical adoption of AI in brain tumor monitoring.

Main Methods:

Developed an evidential deep learning ensemble framework trained on 1655 T1-weighted MRIs to capture aleatoric and epistemic uncertainty.
Evaluated homogeneous and heterogeneous ensembles on independent test sets, assessing performance using Dice similarity coefficient and spatial agreement with neuroradiologist annotations.

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Quantified the calibration of volumetric credible intervals to ensure reliable uncertainty estimation.

Main Results:

The framework achieved high segmentation accuracy (median Dice 0.93) with uncertainty maps aligning well with neuroradiologist-identified ambiguous regions.
Volumetric estimates derived from the model were well-calibrated, indicating reliable uncertainty quantification.
External validation in 353 patients confirmed generalizability with a median Dice of 0.92.

Conclusions:

The developed deep learning framework provides calibrated uncertainty estimates for meningioma segmentation, enhancing AI reliability for clinical use.
This approach supports safer clinical AI deployment and has the potential for lesion segmentation beyond meningiomas.
The findings pave the way for broader application of AI in neuroimaging with improved diagnostic confidence.