# Analyzing Epistemic and Aleatoric Uncertainty for Drusen Segmentation in Optical Coherence Tomography Images

arXiv: https://arxiv.org/abs/2101.08888 · HTML: https://mlsanity.com/paper/2101.08888/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 21 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Efficient Object Localization Using Convolutional Networks](https://mlsanity.com/paper/1411.4280/) (2014)
- [The Importance of Skip Connections in Biomedical Image Segmentation](https://mlsanity.com/paper/1608.04117/) (2016)
- [Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT](https://mlsanity.com/paper/1905.12806/) (2019)
- [DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs](https://mlsanity.com/paper/1811.07492/) (2018)

## Built on by (most cited first, 3 of 3)

- [Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores](https://mlsanity.com/paper/2308.04653/) (2023)
- [Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification](https://mlsanity.com/paper/2411.17571/) (2024)
- [Bayesian Deep Learning Approaches for Uncertainty-Aware Retinal OCT Image Segmentation for Multiple Sclerosis](https://mlsanity.com/paper/2505.12061/) (2025)

## Abstract (arXiv)

Age-related macular degeneration (AMD) is one of the leading causes of permanent vision loss in people aged over 60 years. Accurate segmentation of biomarkers such as drusen that points to the early stages of AMD is crucial in preventing further vision impairment. However, segmenting drusen is extremely challenging due to their varied sizes and appearances, low contrast and noise resemblance. Most existing literature, therefore, have focused on size estimation of drusen using classification, leaving the challenge of accurate segmentation less tackled. Additionally, obtaining the pixel-wise annotations is extremely costly and such labels can often be noisy, suffering from inter-observer and intra-observer variability. Quantification of uncertainty associated with segmentation tasks offers principled measures to inspect the segmentation output. Realizing its utility in identifying erroneous segmentation and the potential applications in clinical decision making, here we develop a U-Net based drusen segmentation model and quantify the segmentation uncertainty. We investigate epistemic and aleatoric uncertainty capturing model confidence and data uncertainty respectively. We present segmentation results and show how uncertainty can help formulate robust evaluation strategies. We visually inspect the pixel-wise uncertainty and segmentation results on test images. We finally analyze the correlation between segmentation uncertainty and accuracy. Our results demonstrate the utility of leveraging uncertainties in developing and explaining segmentation models for medical image analysis.