Look in Different Views: Multi-Scheme Regression Guided Cell Instance Segmentation

TL;DR

This paper introduces a multi-scheme regression guided network for cell instance segmentation, effectively addressing issues with densely packed and elongated cells, achieving state-of-the-art results on multiple datasets.

Contribution

The paper proposes a novel multi-scheme regression guidance approach with gaussian attention and point regression modules for improved cell segmentation.

Findings

Achieves state-of-the-art performance on DSB2018, CA2.5, and SCIS datasets.

Surpasses previous methods by 1.2% AP50 on DSB2018 and CA2.5.

Improves AP50 by 3.0% on SCIS dataset.

Abstract

Cell instance segmentation is a new and challenging task aiming at joint detection and segmentation of every cell in an image. Recently, many instance segmentation methods have applied in this task. Despite their great success, there still exists two main weaknesses caused by uncertainty of localizing cell center points. First, densely packed cells can easily be recognized into one cell. Second, elongated cell can easily be recognized into two cells. To overcome these two weaknesses, we propose a novel cell instance segmentation network based on multi-scheme regression guidance. With multi-scheme regression guidance, the network has the ability to look each cell in different views. Specifically, we first propose a gaussian guidance attention mechanism to use gaussian labels for guiding the network's attention. We then propose a point-regression module for assisting the regression of cell center. Finally, we utilize the output of the above two modules to further guide the instance segmentation. With multi-scheme regression guidance, we can take full advantage of the characteristics of different regions, especially the central region of the cell. We conduct extensive experiments on benchmark datasets, DSB2018, CA2.5 and SCIS. The encouraging results show that our network achieves SOTA (state-of-the-art) performance. On the DSB2018 and CA2.5, our network surpasses previous methods by 1.2% (AP50). Particularly on SCIS dataset, our network performs stronger by large margin (3.0% higher AP50). Visualization and analysis further prove that our proposed method is interpretable.