Current: Research -- Highlighted work -- Bacteria biofilm segmentation

Imaging life at scales below the diffraction limit

Fluorescence microscopy enables spatial and temporal measurements of live cells and cellular communities. However, this potential has not yet been fully realized for investigations of individual cell behaviors and phenotypic changes in dense, three-dimensional (3D) bacterial biofilms. In this project, we collaborate with the Gahlmann Lab in UVA Chemistry on analyzing images of living bacterial biofilms with home-built super-resolution microscopy.

More details are coming up soon!

[1] Jie Wang, Rituparna Sarkar, Arslan Aziz, Andrea Vaccari, A Gahlmann, and Scott T. Acton, "Bact-3d: A level set segmentation approach for dense multi-layered 3d bacterial biofilms," in2017 IEEE International Conferenceon Image Processing (ICIP). IEEE, 2017, pp. 330334.
[2] Jie Wang, Tamal Batabyal, Mingxing Zhang, Ji Zhang, Arslan Aziz, Andreas Gahlmann, and Scott T. Acton, "LCuts: Linear clustering of bacteria using recursive graph cuts," in 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019, pp. 15751579.
[3] Mingxing Zhang, Ji Zhang, Yibo Wang, Jie Wang, Alecia M. Achi-movich, Scott T. Acton, and Andreas Gahlmann, "Non-invasive single-cell morphometry in living bacterial biofilms," accepted in Nature Communications, 2020.
Related paper(s):
Bact-3D: A level set segmentation approach for dense multi-layered 3D bacterial biofilms

This paper presents Bact-3D, a 3D method for segmenting super-resolution images of multi-leveled, living bacteria cultured in vitro. The method incorporates a novel initialization approach that exploits the geometry of the bacterial cells as well an iterative local level set evolution that is tailored to the biological application...

LCuts: Linear Clustering of Bacteria using Recursive Graph Cuts

A graph-based data clustering method, LCuts, is presented with the application on bacterial cell segmentation. The method assists in the assessment of several facets, such as bacterium tracking, cluster growth, and mapping of migration patterns of bacterial biofilms...

Non-invasive single-cell morphometry in living bacterial biofilms

In this work, we present Bacterial Cell Morphometry 3D (BCM3D), an image analysis workflow that combines deep learning with mathematical image analysis to accurately segment and classify single bacterial cells in 3D fluorescence images...Code available!