# Accurate Nuclear Segmentation with Center Vector Encoding

## TL;DR

This paper introduces a novel bottom-up nuclear segmentation method using Center Mask and Center Vector concepts, significantly improving accuracy in crowded and occluded pathology images.

## Contribution

The paper proposes Center Vector Encoding and Center Mask concepts, simplifying instance differentiation and enhancing segmentation performance over existing methods.

## Key findings

- Outperforms state-of-the-art methods in accuracy
- Effectively handles crowded and occluded nuclei
- Simplifies the segmentation process

## Abstract

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.