CONSeg: Voxelwise Uncertainty Quantification for Glioma Segmentation Using Conformal Prediction.

Journal: AJNR. American journal of neuroradiology
Published Date: Jul 3, 2025

Abstract

BACKGROUND AND PURPOSE: Accurate glioma segmentation has the potential to enhance clinical decision-making and treatment planning. Uncertainty quantification methods, including conformal prediction (CP), can enhance segmentation models reliability. CP quantifies uncertainty with statistical confidence guarantees. This study aims to use CP in glioma segmentation.

Authors

  • Danial Elyassirad
    Student Research Committee, Mashhad University of Medical Sciences, Iran.
  • Benyamin Gheiji
    Student Research Committee, Mashhad University of Medical Sciences, Iran.
  • Mahsa Vatanparast
    Student Research Committee, Mashhad University of Medical Sciences, Iran.
  • Amir Mahmoud Ahmadzadeh
    Department of Radiology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Shahriar Faghani
    Mayo Clinic Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, 200 1st Street, S.W., Rochester, MN, 55905, USA.

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