# Uncertainty guided semi-supervised segmentation of retinal layers in OCT images

arXiv: https://arxiv.org/abs/2103.02083 · HTML: https://mlsanity.com/paper/2103.02083/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 2 Mar 2021.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Efficient Object Localization Using Convolutional Networks](https://mlsanity.com/paper/1411.4280/) (2014)
- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://mlsanity.com/paper/1611.09326/) (2016)
- [ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network](https://mlsanity.com/paper/1704.02161/) (2017)
- [Deep Retinal Image Understanding](https://mlsanity.com/paper/1609.01103/) (2016)
- [Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning](https://mlsanity.com/paper/1809.04282/) (2018)
- [Semi-Supervised Deep Learning for Fully Convolutional Networks](https://mlsanity.com/paper/1703.06000/) (2017)

## Built on by (most cited first, 29 of 29)

- [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 quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2405.00378/) (2024)
- [NP-Match: When Neural Processes meet Semi-Supervised Learning](https://mlsanity.com/paper/2207.01066/) (2022)
- [Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2206.09293/) (2022)
- [Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2305.16214/) (2023)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Anatomically-aware Uncertainty for Semi-supervised Image Segmentation](https://mlsanity.com/paper/2310.16099/) (2023)
- [Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2111.10989/) (2021)
- [Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation](https://mlsanity.com/paper/2304.07519/) (2023)
- [SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors](https://mlsanity.com/paper/2207.00458/) (2022)
- [Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations](https://mlsanity.com/paper/2312.01726/) (2023)
- [Segmentation with mixed supervision: Confidence maximization helps knowledge distillation](https://mlsanity.com/paper/2109.10902/) (2021)
- [Uncertainty-Aware Semi-Supervised Few Shot Segmentation](https://mlsanity.com/paper/2110.08954/) (2021)
- [Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation](https://mlsanity.com/paper/2203.05682/) (2022)
- [NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2308.02866/) (2023)
- [Uncertainty-Aware Vision-Language Segmentation for Medical Imaging](https://mlsanity.com/paper/2602.14498/) (2026)
- [Dual Cross-image Semantic Consistency with Self-aware Pseudo Labeling for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2507.21440/) (2025)
- [POPCORN: Progressive Pseudo-labeling with Consistency Regularization and Neighboring](https://mlsanity.com/paper/2109.06361/) (2021)
- [Curvilinear object segmentation in medical images based on ODoS filter and deep learning network](https://mlsanity.com/paper/2301.07475/) (2023)
- [Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning](https://mlsanity.com/paper/2107.14476/) (2021)
- [Probabilistic Domain Adaptation for Biomedical Image Segmentation](https://mlsanity.com/paper/2303.11790/) (2023)
- [Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2411.04493/) (2024)
- [SD-RetinaNet: Topologically Constrained Semi-Supervised Retinal Lesion and Layer Segmentation in OCT](https://mlsanity.com/paper/2509.20864/) (2025)
- [NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning](https://mlsanity.com/paper/2301.13569/) (2023)
- [CORA: Consistency-Guided Semi-Supervised Framework for Reasoning Segmentation](https://mlsanity.com/paper/2511.17755/) (2025)
- [Clinically Acceptable Segmentation of Organs at Risk in Cervical Cancer Radiation Treatment from Clinically Available Annotations](https://mlsanity.com/paper/2302.10661/) (2023)
- [Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey](https://mlsanity.com/paper/2504.11588/) (2025)
- [Are We Overconfident in Models and Results for Semi-Supervised 3D Medical Image Segmentation?](https://mlsanity.com/paper/2605.25561/) (2026)

## Abstract (arXiv)

Deep convolutional neural networks have shown outstanding performance in medical image segmentation tasks. The usual problem when training supervised deep learning methods is the lack of labeled data which is time-consuming and costly to obtain. In this paper, we propose a novel uncertainty-guided semi-supervised learning based on a student-teacher approach for training the segmentation network using limited labeled samples and a large number of unlabeled images. First, a teacher segmentation model is trained from the labeled samples using Bayesian deep learning. The trained model is used to generate soft segmentation labels and uncertainty maps for the unlabeled set. The student model is then updated using the softly segmented samples and the corresponding pixel-wise confidence of the segmentation quality estimated from the uncertainty of the teacher model using a newly designed loss function. Experimental results on a retinal layer segmentation task show that the proposed method improves the segmentation performance in comparison to the fully supervised approach and is on par with the expert annotator. The proposed semi-supervised segmentation framework is a key contribution and applicable for biomedical image segmentation across various imaging modalities where access to annotated medical images is challenging