Tracking bacteria at high density with FAST, the Feature-Assisted Segmenter/Tracker.

ABSTRACT: Most bacteria live attached to surfaces in densely-packed communities. While new experimental and imaging techniques are beginning to provide a window on the complex processes that play out in these communities, resolving the behaviour of individual cells through time and space remains a major challenge. Although a number of different software solutions have been developed to track microorganisms, these typically require users either to tune a large number of parameters or to groundtruth a large volume of imaging data to train a deep learning model-both manual processes which can be very time consuming for novel experiments. To overcome these limitations, we have developed FAST, the Feature-Assisted Segmenter/Tracker, which uses unsupervised machine learning to optimise tracking while main

SUBMITTER: Meacock OJ 

PROVIDER: S-EPMC10586697 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature