# Improving 3D deep learning segmentation with biophysically motivated cell synthesis

arXiv: https://arxiv.org/abs/2408.16471 · HTML: https://mlsanity.com/paper/2408.16471/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 52 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy](https://mlsanity.com/paper/1908.03636/) (2019)
- [Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation](https://mlsanity.com/paper/1801.07198/) (2018)
- [Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets](https://mlsanity.com/paper/2301.10227/) (2023)
- [Comparison and Analysis of Image-to-Image Generative Adversarial Networks: A Survey](https://mlsanity.com/paper/2112.12625/) (2021)
- [3D fluorescence microscopy data synthesis for segmentation and benchmarking](https://mlsanity.com/paper/2107.10180/) (2021)
- [AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned Disentangling Training](https://mlsanity.com/paper/2107.11022/) (2021)

## Built on by (most cited first, 4 of 4)

- [Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges](https://mlsanity.com/paper/2505.10993/) (2025)
- [Bayesian Optimization for Design Parameters of 3D Image Data Analysis](https://mlsanity.com/paper/2602.15660/) (2026)
- [Boosting Overlapping Organoid Instance Segmentation Using Pseudo-Label Unmixing and Synthesis-Assisted Learning](https://mlsanity.com/paper/2601.06642/) (2026)
- [Data Synthesis Improves 3D Myotube Instance Segmentation](https://mlsanity.com/paper/2604.14720/) (2026)

## Abstract (arXiv)

Biomedical research increasingly relies on 3D cell culture models and AI-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level. However, this requires for a precise segmentation of 3D cell datasets, which in turn demands high-quality ground truth for training. Manual annotation, the gold standard for ground truth data, is too time-consuming and thus not feasible for the generation of large 3D training datasets. To address this, we present a novel framework for generating 3D training data, which integrates biophysical modeling for realistic cell shape and alignment. Our approach allows the in silico generation of coherent membrane and nuclei signals, that enable the training of segmentation models utilizing both channels for improved performance. Furthermore, we present a new GAN training scheme that generates not only image data but also matching labels. Quantitative evaluation shows superior performance of biophysical motivated synthetic training data, even outperforming manual annotation and pretrained models. This underscores the potential of incorporating biophysical modeling for enhancing synthetic training data quality.