# Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation

arXiv: https://arxiv.org/abs/1801.07198 · HTML: https://mlsanity.com/paper/1801.07198/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2018.

## Built on by (most cited first, 19 of 19)

- [Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation](https://mlsanity.com/paper/2104.06087/) (2021)
- [Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation](https://mlsanity.com/paper/2102.02033/) (2021)
- [AnyStar: Domain randomized universal star-convex 3D instance segmentation](https://mlsanity.com/paper/2307.07044/) (2023)
- [FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy](https://mlsanity.com/paper/2207.01390/) (2022)
- [TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation](https://mlsanity.com/paper/2105.08993/) (2021)
- [Implicit Neural Representations for Generative Modeling of Living Cell Shapes](https://mlsanity.com/paper/2207.06283/) (2022)
- [Forensic Analysis of Synthetically Generated Western Blot Images](https://mlsanity.com/paper/2112.08739/) (2021)
- [When Visible-to-Thermal Facial GAN Beats Conditional Diffusion](https://mlsanity.com/paper/2302.09395/) (2023)
- [Generative modeling of living cells with SO(3)-equivariant implicit neural representations](https://mlsanity.com/paper/2304.08960/) (2023)
- [Improving 3D deep learning segmentation with biophysically motivated cell synthesis](https://mlsanity.com/paper/2408.16471/) (2024)
- [AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned Disentangling Training](https://mlsanity.com/paper/2107.11022/) (2021)
- [Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning](https://mlsanity.com/paper/2403.02566/) (2024)
- [Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to Enhance Cell Segmentation](https://mlsanity.com/paper/2411.11515/) (2024)
- [RCNN-SliceNet: A Slice and Cluster Approach for Nuclei Centroid Detection in Three-Dimensional Fluorescence Microscopy Images](https://mlsanity.com/paper/2106.15753/) (2021)
- [Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey](https://mlsanity.com/paper/2509.15363/) (2025)
- [Bayesian Optimization for Design Parameters of 3D Image Data Analysis](https://mlsanity.com/paper/2602.15660/) (2026)
- [A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification](https://mlsanity.com/paper/2202.06465/) (2022)
- [Microscopy Image Segmentation via Point and Shape Regularized Data Synthesis](https://mlsanity.com/paper/2308.09835/) (2023)
- [CyclePose -- Leveraging Cycle-Consistency for Annotation-Free Nuclei Segmentation in Fluorescence Microscopy](https://mlsanity.com/paper/2503.11266/) (2025)

## 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.