# Robust 3D Cell Segmentation: Extending the View of Cellpose

arXiv: https://arxiv.org/abs/2105.00794 · HTML: https://mlsanity.com/paper/2105.00794/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Spherical Harmonics for Shape-Constrained 3D Cell Segmentation](https://mlsanity.com/paper/2010.12369/) (2020)
- [SEGMENT3D: A Web-based Application for Collaborative Segmentation of 3D images used in the Shoot Apical Meristem](https://mlsanity.com/paper/1710.09933/) (2017)

## Built on by (most cited first, 5 of 5)

- [Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets](https://mlsanity.com/paper/2301.10227/) (2023)
- [3D fluorescence microscopy data synthesis for segmentation and benchmarking](https://mlsanity.com/paper/2107.10180/) (2021)
- [Learning Deformable 3D Graph Similarity to Track Plant Cells in Unregistered Time Lapse Images](https://mlsanity.com/paper/2309.11157/) (2023)
- [3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence](https://mlsanity.com/paper/2502.01890/) (2025)
- [trAIce3D: A Prompt-Driven Transformer Based U-Net for Semantic Segmentation of Microglial Cells from Large-Scale 3D Microscopy Images](https://mlsanity.com/paper/2507.22635/) (2025)

## Abstract (arXiv)

Increasing data set sizes of 3D microscopy imaging experiments demand for an automation of segmentation processes to be able to extract meaningful biomedical information. Due to the shortage of annotated 3D image data that can be used for machine learning-based approaches, 3D segmentation approaches are required to be robust and to generalize well to unseen data. The Cellpose approach proposed by Stringer et al. proved to be such a generalist approach for cell instance segmentation tasks. In this paper, we extend the Cellpose approach to improve segmentation accuracy on 3D image data and we further show how the formulation of the gradient maps can be simplified while still being robust and reaching similar segmentation accuracy. The code is publicly available and was integrated into two established open-source applications that allow using the 3D extension of Cellpose without any programming knowledge.