# Accurate Nuclear Segmentation with Center Vector Encoding

arXiv: https://arxiv.org/abs/1907.03951 · HTML: https://mlsanity.com/paper/1907.03951/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

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

- [Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network](https://mlsanity.com/paper/2106.10641/) (2021)
- [Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images](https://mlsanity.com/paper/2106.07159/) (2021)

## Abstract (arXiv)

Nuclear segmentation is important and frequently demanded for pathology image analysis, yet is also challenging due to nuclear crowdedness and possible occlusion. In this paper, we present a novel bottom-up method for nuclear segmentation. The concepts of Center Mask and Center Vector are introduced to better depict the relationship between pixels and nuclear instances. The instance differentiation process are thus largely simplified and easier to understand. Experiments demonstrate the effectiveness of Center Vector Encoding, where our method outperforms state-of-the-arts by a clear margin.