Tracking-Assisted Segmentation of Biological Cells

TL;DR

This paper enhances biological cell segmentation and tracking by augmenting U-Net with Siamese matching, improving accuracy in complex scenarios like cell collision and mitosis.

Contribution

It introduces a novel tracking-assisted segmentation method that models cell behavior to improve performance over standard U-Net approaches.

Findings

Achieved up to 3.8% improvement in segmentation accuracy

Achieved up to 3.4% improvement in tracking accuracy

Effective in complex biological processes like collision and mitosis

Abstract

U-Net and its variants have been demonstrated to work sufficiently well in biological cell tracking and segmentation. However, these methods still suffer in the presence of complex processes such as collision of cells, mitosis and apoptosis. In this paper, we augment U-Net with Siamese matching-based tracking and propose to track individual nuclei over time. By modelling the behavioural pattern of the cells, we achieve improved segmentation and tracking performances through a re-segmentation procedure. Our preliminary investigations on the Fluo-N2DH-SIM+ and Fluo-N2DH-GOWT1 datasets demonstrate that absolute improvements of up to 3.8 % and 3.4% can be obtained in segmentation and tracking accuracy, respectively.