ILAN DAVIS LAB
OXFORD


CytoCensus is a “point-and- click” supervised machine- learning image analysis software to quantitatively identify defined cell classes and divisions from large multidimensional data sets of complex tissues. In Hailstone et al. we demonstrate its utility in analysing challenging developmental phenotypes in living explanted Drosophila larval brains, mammalian embryos and zebrafish organoids. In comparative tests, a significant improvement in performance over existing easy-to-use image analysis software at cell detection

Documentation & User Manual

What can you do with CytoCensus?

Count cells/objects in 3D – if they are approximately round
Use images of dense/complex tissue with low SNR
Find cells of a particular size/colour/morphology
Identify cell/object centres i.e. XYZ coordinates
Determine counts and coordinates of cells in time-lapse image series
Determine object counts in a user defined regions of interest (ROI) over time
Compare the relative numbers of different object classes (or subclasses)

With a little FIJI/ImageJ knowledge, CytoCensus outputs can be used to:

Determine average intensity across a class of cells
Track cells that standard tracking struggles with (using CytoCensus probability maps and the ImageJ TrackMate plugin)