# Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning

arXiv: https://arxiv.org/abs/1809.04282 · HTML: https://mlsanity.com/paper/1809.04282/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 19 of 19)

- [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://mlsanity.com/paper/2210.03736/) (2022)
- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [Uncertainty guided semi-supervised segmentation of retinal layers in OCT images](https://mlsanity.com/paper/2103.02083/) (2021)
- [Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation](https://mlsanity.com/paper/2104.06087/) (2021)
- [CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis](https://mlsanity.com/paper/2106.10230/) (2021)
- [SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors](https://mlsanity.com/paper/2207.00458/) (2022)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation](https://mlsanity.com/paper/2110.00404/) (2021)
- [Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification](https://mlsanity.com/paper/2206.13123/) (2022)
- [Segmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification](https://mlsanity.com/paper/2210.14799/) (2022)
- [Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning](https://mlsanity.com/paper/2111.07646/) (2021)
- [Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection](https://mlsanity.com/paper/2206.10911/) (2022)
- [Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification](https://mlsanity.com/paper/2204.01728/) (2022)
- [SD-RetinaNet: Topologically Constrained Semi-Supervised Retinal Lesion and Layer Segmentation in OCT](https://mlsanity.com/paper/2509.20864/) (2025)
- [Improved Super Resolution of MR Images Using CNNs and Vision Transformers](https://mlsanity.com/paper/2207.11748/) (2022)
- [Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis](https://mlsanity.com/paper/2211.08424/) (2022)
- [Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores](https://mlsanity.com/paper/2308.04653/) (2023)
- [Bayesian Deep Learning Approaches for Uncertainty-Aware Retinal OCT Image Segmentation for Multiple Sclerosis](https://mlsanity.com/paper/2505.12061/) (2025)

## Abstract (arXiv)

Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our method not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output. The generated uncertainty map can be used to identify erroneously segmented image regions which is useful in downstream analysis. We have validated our method on a dataset of 1487 images obtained from 15 subjects (OCT volumes) and compared it against the state-of-the-art segmentation algorithms that does not take uncertainty into account. The proposed uncertainty based segmentation method results in comparable or improved performance, and most importantly is more robust against noise.