mlsanityData to 7 Oct 2026

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

arXiv 1809.04282 ↗First posted 12 Sep 2018cs.CVcited by 19

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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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Sep 2018.

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Citing papers per year: 2021: 5; 2022: 8; 2023: 3; 2024: 1; 2025: 2.

19 papers.

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  1. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
  2. 2023A Review of Uncertainty Estimation and its Application in Medical Imagingcited by 59
  3. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  4. 2021Uncertainty guided semi-supervised segmentation of retinal layers in OCT imagescited by 29
  5. 2021Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentationcited by 22
  6. 2021CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysiscited by 8
  7. 2022SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priorscited by 7
  8. 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentationcited by 6
  9. 2021Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentationcited by 5
  10. 2022Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classificationcited by 5
  11. 2022Segmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantificationcited by 5
  12. 2021Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learningcited by 4
7 more
  1. 2022Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detectioncited by 3
  2. 2022Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classificationcited by 2
  3. 2025SD-RetinaNet: Topologically Constrained Semi-Supervised Retinal Lesion and Layer Segmentation in OCTcited by 2
  4. 2022Improved Super Resolution of MR Images Using CNNs and Vision Transformerscited by 1
  5. 2022Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysiscited by 0
  6. 2023Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scorescited by 0
  7. 2025Bayesian Deep Learning Approaches for Uncertainty-Aware Retinal OCT Image Segmentation for Multiple Sclerosiscited by 0