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<p>A major barrier to clinical adoption of artificial intelligence (AI) for brain tumor monitoring is the lack of calibrated uncertainty in automated segmentation, limiting clinician trust. We developed a deep learning framework that generates uncertainty estimates for meningioma segmentation on brain MRI. Evidential deep learning ensembles were trained on 1655 post-contrast T1-weighted MRIs (788 patients) to capture aleatoric-like and epistemic-like uncertainty. Architecturally homogeneous and heterogeneous ensembles were evaluated on an independent test set of 68 MRIs (43 patients) and compared with existing methods. Performance was assessed using Dice similarity coefficient, spatial agreement between uncertainty maps and neuroradiologist-identified ambiguous regions, and calibration of volumetric credible intervals. The model achieved high accuracy (median Dice 0.93), with uncertainty maps aligning with ambiguous regions and well-calibrated volume estimates. External validation in 353 patients confirmed generalizability (median Dice 0.92), supporting safer clinical AI deployment and enabling calibrated uncertainty estimation for lesion segmentation beyond meningiomas.</p>

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Segmenting with confidence through uncertainty quantification for brain tumor imaging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • Yassine Guennoun,
  • Pierre Nedelec,
  • Mark McArthur,
  • Evan Bloch,
  • Jinchi Wei,
  • Leo Sugrue,
  • Evan Calabrese,
  • Andreas M. Rauschecker

摘要

A major barrier to clinical adoption of artificial intelligence (AI) for brain tumor monitoring is the lack of calibrated uncertainty in automated segmentation, limiting clinician trust. We developed a deep learning framework that generates uncertainty estimates for meningioma segmentation on brain MRI. Evidential deep learning ensembles were trained on 1655 post-contrast T1-weighted MRIs (788 patients) to capture aleatoric-like and epistemic-like uncertainty. Architecturally homogeneous and heterogeneous ensembles were evaluated on an independent test set of 68 MRIs (43 patients) and compared with existing methods. Performance was assessed using Dice similarity coefficient, spatial agreement between uncertainty maps and neuroradiologist-identified ambiguous regions, and calibration of volumetric credible intervals. The model achieved high accuracy (median Dice 0.93), with uncertainty maps aligning with ambiguous regions and well-calibrated volume estimates. External validation in 353 patients confirmed generalizability (median Dice 0.92), supporting safer clinical AI deployment and enabling calibrated uncertainty estimation for lesion segmentation beyond meningiomas.