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.
@article{arxiv.1910.08735,
title = {Tracking-Assisted Segmentation of Biological Cells},
author = {Deepak K. Gupta and Nathan de Bruijn and Andreas Panteli and Efstratios Gavves},
journal= {arXiv preprint arXiv:1910.08735},
year = {2019}
}
Comments
Accepted in NeurIPS2019, Medical Imaging meets NeurIPS workshop