mlsanityData to 7 Oct 2026

Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation

arXiv 1801.07198 ↗First posted 22 Jan 2018cs.CV cs.LG eess.IVcited by 19

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Abstract (arXiv)

Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D deep learning nuclei segmentation method using synthetic 3D volumes for training. A set of synthetic volumes and the corresponding groundtruth are generated using spatially constrained cycle-consistent adversarial networks. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully for various data sets.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jan 2018.

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Citing papers per year: 2021: 6; 2022: 3; 2023: 4; 2024: 3; 2025: 2; 2026: 1.

19 papers.

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  1. 2021Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentationcited by 22
  2. 2021Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentationcited by 17
  3. 2023AnyStar: Domain randomized universal star-convex 3D instance segmentationcited by 14
  4. 2022FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomycited by 13
  5. 2021TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translationcited by 12
  6. 2022Implicit Neural Representations for Generative Modeling of Living Cell Shapescited by 12
  7. 2021Forensic Analysis of Synthetically Generated Western Blot Imagescited by 11
  8. 2023When Visible-to-Thermal Facial GAN Beats Conditional Diffusioncited by 4
  9. 2023Generative modeling of living cells with SO(3)-equivariant implicit neural representationscited by 4
  10. 2024Improving 3D deep learning segmentation with biophysically motivated cell synthesiscited by 4
  11. 2021AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned Disentangling Trainingcited by 2
  12. 2024Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learningcited by 2
7 more
  1. 2024Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to Enhance Cell Segmentationcited by 2
  2. 2021RCNN-SliceNet: A Slice and Cluster Approach for Nuclei Centroid Detection in Three-Dimensional Fluorescence Microscopy Imagescited by 1
  3. 2025Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Surveycited by 1
  4. 2026Bayesian Optimization for Design Parameters of 3D Image Data Analysiscited by 1
  5. 2022A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortificationcited by 0
  6. 2023Microscopy Image Segmentation via Point and Shape Regularized Data Synthesiscited by 0
  7. 2025CyclePose -- Leveraging Cycle-Consistency for Annotation-Free Nuclei Segmentation in Fluorescence Microscopycited by 0