mlsanityData to 14 Oct 2026

Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks

arXiv 2208.14635 ↗First posted 31 Aug 2022eess.IV cs.CV cs.LGcited by 5

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Abstract (arXiv)

Optical Coherence Tomography(OCT) is a non-invasive technique capturing cross-sectional area of the retina in micro-meter resolutions. It has been widely used as a auxiliary imaging reference to detect eye-related pathology and predict longitudinal progression of the disease characteristics. Retina layer segmentation is one of the crucial feature extraction techniques, where the variations of retinal layer thicknesses and the retinal layer deformation due to the presence of the fluid are highly correlated with multiple epidemic eye diseases like Diabetic Retinopathy(DR) and Age-related Macular Degeneration (AMD). However, these images are acquired from different devices, which have different intensity distribution, or in other words, belong to different imaging domains. This paper proposes a segmentation-guided domain-adaptation method to adapt images from multiple devices into single image domain, where the state-of-art pre-trained segmentation model is available. It avoids the time consumption of manual labelling for the upcoming new dataset and the re-training of the existing network. The semantic consistency and global feature consistency of the network will minimize the hallucination effect that many researchers reported regarding Cycle-Consistent Generative Adversarial Networks(CycleGAN) architecture.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 20 of its 48 references that are in the map. First posted on arXiv 31 Aug 2022.

Builds on

20 of 48 references.

References · 20

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  5. 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,082
  6. 2019Bidirectional Learning for Domain Adaptation of Semantic Segmentationcited by 308
  7. 2017Image to Image Translation for Domain Adaptationcited by 171
  8. 2017Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptationcited by 151
  9. 2019DoveNet: Deep Image Harmonization via Domain Verificationcited by 135
  10. 2020Unsupervised Domain Adaptation in Semantic Segmentation: a Reviewcited by 88
  11. 2018The Unusual Effectiveness of Averaging in GAN Trainingcited by 73
  12. 2019Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanationscited by 64
8 more
  1. 2020Phase Consistent Ecological Domain Adaptationcited by 62
  2. 2018Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data sizecited by 42
  3. 2017Maximum-Likelihood Augmented Discrete Generative Adversarial Networkscited by 39
  4. 2017ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Networkcited by 37
  5. 2018Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaptioncited by 27
  6. 2018Deep Learning based Retinal OCT Segmentationcited by 16
  7. 2020Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networkscited by 10
  8. 2019Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentationcited by 4

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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 3.

5 papers.

  1. 2023Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectivescited by 24
  2. 2023Medical Image Segmentation with Domain Adaptation: A Surveycited by 5
  3. 2025Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Clinical Assessment of Diabetic Retinopathy Severitycited by 0
  4. 2025Bridging Synthetic-to-Real Gaps: Frequency-Aware Perturbation and Selection for Single-shot Multi-Parametric Mapping Reconstructioncited by 0
  5. 2025VessShape: Few-shot 2D blood vessel segmentation by leveraging shape priors from synthetic imagescited by 0