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Unsupervised Domain Adaptation for Neuron Membrane Segmentation based on Structural Features

arXiv 2305.02569 ↗First posted 4 May 2023cs.CVcited by 0

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Abstract (arXiv)

AI-enhanced segmentation of neuronal boundaries in electron microscopy (EM) images is crucial for automatic and accurate neuroinformatics studies. To enhance the limited generalization ability of typical deep learning frameworks for medical image analysis, unsupervised domain adaptation (UDA) methods have been applied. In this work, we propose to improve the performance of UDA methods on cross-domain neuron membrane segmentation in EM images. First, we designed a feature weight module considering the structural features during adaptation. Second, we introduced a structural feature-based super-resolution approach to alleviating the domain gap by adjusting the cross-domain image resolutions. Third, we proposed an orthogonal decomposition module to facilitate the extraction of domain-invariant features. Extensive experiments on two domain adaptive membrane segmentation applications have indicated the effectiveness of our method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 35 references that are in the map. First posted on arXiv 4 May 2023.

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References · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  4. 2017Non-local Neural Networkscited by 2,290
  5. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  6. 2018ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentationcited by 623
  7. 2018Learning to Adapt Structured Output Space for Semantic Segmentationcited by 584
  8. 2020FDA: Fourier Domain Adaptation for Semantic Segmentationcited by 540
  9. 2019ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed datacited by 260
  10. 2017Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Trainingcited by 57
  11. 2020Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weightingcited by 38
  12. 2022Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labellingcited by 16
4 more
  1. 2022Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglementcited by 15
  2. 2022Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learningcited by 10
  3. 2022Unsupervised Domain Adaptive Fundus Image Segmentation with Few Labeled Source Datacited by 2
  4. 2015Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detectioncited by 1

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