Epistemic and Heteroscedastic Uncertainty Estimation in Retinal Blood Vessel Segmentation

U.Porto Journal of Engineering

Aurélio Campilho¹  Asim Smailagic²  Jaime Cardoso³  Pedro Costa⁴ 

[1] University of Porto;Carnegie Mellon University;INESC TEC-Institute for Systems and Computer Engineering, Technology and Science;University of Porto;

关键词: diabetic retinopathy;    blood vessel segmentation;    uncertainty estimation;    deep learning;    convolutional neural networks;   

DOI  :  10.24840/2183-6493_007.003_0008

来源: DOAJ

【 摘 要 】

Current state-of-the-art medical image segmentation methods require high quality datasets to obtain good performance. However, medical specialists often disagree on diagnosis, hence, datasets contain contradictory annotations. This, in turn, leads to difficulties in the optimization process of Deep Learning models and hinder performance. We propose a method to estimate uncertainty in Convolutional Neural Network (CNN) segmentation models, that makes the training of CNNs more robust to contradictory annotations. In this work, we model two types of uncertainty, heteroscedastic and epistemic, without adding any additional supervisory signal other than the ground-truth segmentation mask. As expected, the uncertainty is higher closer to vessel boundaries, and on top of thinner and less visible vessels where it is more likely for medical specialists to disagree. Therefore, our method is more suitable to learn from datasets created with heterogeneous annotators. We show that there is a correlation between the uncertainty estimated by our method and the disagreement in the segmentation provided by two different medical specialists. Furthermore, by explicitly modeling the uncertainty, the Intersection over Union of the segmentation network improves 5.7 percentage points.

【 授权许可】

Unknown