# Accurate 3D Cell Segmentation using Deep Feature and CRF Refinement

arXiv: https://arxiv.org/abs/1902.04729 · HTML: https://mlsanity.com/paper/1902.04729/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 2 of 2)

- [Parallel Watershed Partitioning: GPU-Based Hierarchical Image Segmentation](https://mlsanity.com/paper/2410.08946/) (2024)
- [Deep Learning Enabled Time-Lapse 3D Cell Analysis](https://mlsanity.com/paper/2208.07997/) (2022)

## Abstract (arXiv)

We consider the problem of accurately identifying cell boundaries and labeling individual cells in confocal microscopy images, specifically, 3D image stacks of cells with tagged cell membranes. Precise identification of cell boundaries, their shapes, and quantifying inter-cellular space leads to a better understanding of cell morphogenesis. Towards this, we outline a cell segmentation method that uses a deep neural network architecture to extract a confidence map of cell boundaries, followed by a 3D watershed algorithm and a final refinement using a conditional random field. In addition to improving the accuracy of segmentation compared to other state-of-the-art methods, the proposed approach also generalizes well to different datasets without the need to retrain the network for each dataset. Detailed experimental results are provided, and the source code is available on GitHub.