About

Introduction

cellfinder takes a stitched, but otherwise raw whole-brain dataset with at least two channels:

  • Background channel (i.e. autofluorescence)
  • Signal channel, the one with the cells to be detected:

raw Raw coronal serial two-photon mouse brain image showing labelled cells

Cell candidate detection

Classical image analysis (e.g. filters, thresholding) is used to find cell-like objects (with false positives):

raw Candidate cells (including many artefacts)

Cell candidate classification

A deep-learning network (ResNet) is used to classify cell candidates as true cells or artefacts:

raw Cassified cell candidates. Yellow - cells, Blue - artefacts

Registration and segmentation (amap)

Using amap, cellfinder aligns a template brain and atlas annotations (e.g. the Allen Reference Atlas, ARA) to the sample allowing detected cells to be assigned a brain region.

This transformation can be inverted, allowing detected cells to be transformed to a standard anatomical space.

raw ARA overlaid on sample image

Analysis of cell positions in a common anatomical space

Registration to a template allows for powerful group-level analysis of cellular disributions. (Example to come)