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Reference-free Bayesian model for pointing errors of typein neurosurgical planning.

John S H Baxter1, Stéphane Croci2, Antoine Delmas2

  • 1Laboratoire Traitement du Signal et de l'Image (LTSI - INSERM UMR 1099), Université de Rennes 1, Rennes, France. jbaxter@univ-rennes1.fr.

International Journal of Computer Assisted Radiology and Surgery
|May 30, 2023
PubMed
Summary

This study introduces a novel Bayesian model to quantify annotator disagreement in neurosurgical image analysis. The reference-free method accurately measures errors, aiding research where ground truth is unknown.

Keywords:
Bayesian statisticsError modellingLocalisationPointingSurgical planningTranscranial magnetic stimulation

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Area of Science:

  • Neurosurgical imaging analysis
  • Medical image segmentation
  • Computational anatomy

Background:

  • Identifying critical points in volumetric neurosurgical images is challenging due to inter-annotator variability.
  • Inferred points, not directly visible, lead to significant disagreement among experts, complicating research.
  • Errors of type, where experts select fundamentally different points, are a key challenge.

Purpose of the Study:

  • To develop a regularized Bayesian model for measuring "errors of type" in image-based pointing tasks.
  • To create a reference-free model that does not require ground truth data.
  • To assess annotator consensus for improved reliability in neurosurgical planning.

Main Methods:

  • A regularized Bayesian framework was developed to quantify disagreement in pointing tasks.
  • The model operates without prior knowledge of the ground truth point.
  • It leverages the consensus level among multiple annotators to estimate accuracy.

Main Results:

  • The model achieved an estimated probability of selecting the correct point between 82.6% and 88.6%.
  • Uncertainty in these estimates ranged from 2.8% to 4.0%, providing dataset strength insights.
  • Results align with literature values where ground truth was known.

Conclusions:

  • The reference-free Bayesian model effectively quantifies "errors of type" in pointing tasks.
  • Enables clinical studies with fewer annotators and unknown ground truth.
  • Facilitates better understanding of human error in neurosurgical planning.