Confidence-Based Annotation Of Brain Tumours In Ultrasound
Journal:
arXiv
Published Date:
Feb 21, 2025
Abstract
Purpose: An investigation of the challenge of annotating discrete
segmentations of brain tumours in ultrasound, with a focus on the issue of
aleatoric uncertainty along the tumour margin, particularly for diffuse
tumours. A segmentation protocol and method is proposed that incorporates this
margin-related uncertainty while minimising the interobserver variance through
reduced subjectivity, thereby diminishing annotator epistemic uncertainty.
Approach: A sparse confidence method for annotation is proposed, based on a
protocol designed using computer vision and radiology theory. Results: Output
annotations using the proposed method are compared with the corresponding
professional discrete annotation variance between the observers. A linear
relationship was measured within the tumour margin region, with a Pearson
correlation of 0.8. The downstream application was explored, comparing training
using confidence annotations as soft labels with using the best discrete
annotations as hard labels. In all evaluation folds, the Brier score was
superior for the soft-label trained network. Conclusion: A formal framework was
constructed to demonstrate the infeasibility of discrete annotation of brain
tumours in B-mode ultrasound. Subsequently, a method for sparse
confidence-based annotation is proposed and evaluated. Keywords: Brain tumours,
ultrasound, confidence, annotation.
