Increased usage of automated tools like deep learning in medical image segmentation has alleviated the bottleneck of manual contouring. This has shifted manual labour to quality assessment (QA) of automated contours which involves detecting errors and correcting them. A potential solution to semi-automated QA is to use deep Bayesian uncertainty to recommend potentially erroneous regions, thus reducing time spent on error detection. Previous work has investigated the correspondence between uncertainty and error, however, no work has been done on improving the “utility” of Bayesian uncertainty maps such that it is only present in inaccurate regions and not in the accurate ones. Our work trains the FlipOut model with the Accuracy-vs-Uncertainty (AvU) loss which promotes uncertainty to be present only in inaccurate regions. We apply this method on datasets of two radiotherapy body sites, c.f. head-and-neck CT and prostate MR scans. Uncertainty heatmaps (i.e. predictive entropy) are evaluated against voxel inaccuracies using Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves. Numerical results show that when compared to the Bayesian baseline the proposed method successfully suppresses uncertainty for accurate voxels, with similar presence of uncertainty for inaccurate voxels. Code to reproduce experiments is available at https://github.com/prerakmody/bayesuncertainty-error-correspondence
Bayesian Deep Learning · Bayesian Uncertainty · Uncertainty-Error Correspondence · Uncertainty Calibration · Contour Quality Assessment · Model Calibration
@article{melba:2024:018:mody,
title = "Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation",
author = "Mody, Prerak and Chaves-de-Plaza, Nicolas F. and Rao, Chinmay and Astrenidou, Eleftheria and de Ridder, Mischa and Hoekstra, Nienke and Hildebrandt, Klaus and Staring, Marius",
journal = "Machine Learning for Biomedical Imaging",
volume = "2",
issue = "August 2024 issue",
year = "2024",
pages = "1048--1082",
issn = "2766-905X",
doi = "https://doi.org/10.59275/j.melba.2024-5gc8",
url = "https://melba-journal.org/2024:018"
}
TY - JOUR
AU - Mody, Prerak
AU - Chaves-de-Plaza, Nicolas F.
AU - Rao, Chinmay
AU - Astrenidou, Eleftheria
AU - de Ridder, Mischa
AU - Hoekstra, Nienke
AU - Hildebrandt, Klaus
AU - Staring, Marius
PY - 2024
TI - Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation
T2 - Machine Learning for Biomedical Imaging
VL - 2
IS - August 2024 issue
SP - 1048
EP - 1082
SN - 2766-905X
DO - https://doi.org/10.59275/j.melba.2024-5gc8
UR - https://melba-journal.org/2024:018
ER -