Uncertainty-Aware Multi-Source Retinal Fluid Segmentation in OCT
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Abstract (arXiv)
Measuring retinal fluid from optical coherence tomography (OCT) drives treatment decisions in macular disease, but manual annotation is slow and segmentation models trained on one scanner degrade on another. We present an attention-guided TransUNet that segments three fluid types across four independent OCT sources, combining a domain-adaptive normalisation scheme with an uncertainty estimate that flags unreliable pixels. The model reaches a mean fluid Dice of 0.78, and -- most usefully for clinicians -- its uncertainty is 1.34x higher exactly where expert graders disagree (p<10^-4), turning a raw segmentation map into an actionable clinical triage signal.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 14 references that are in the map. First posted on arXiv 9 Jul 2026.
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References · 10
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
- 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
- 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
- 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
- 2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 272
- 2020DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasetscited by 70
- 2018Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Samplingcited by 20
- 2022RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentationcited by 6
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