gutanalysistoolbox

Analysis and characterisation of cells within the gut wall using deep learning models. The current focus is on studying enteric neurons and enteric glia.

https://github.com/pr4deepr/gutanalysistoolbox

Science Score: 49.0%

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    Found 10 DOI reference(s) in README
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    Links to: zenodo.org
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Keywords

calcium-imaging cell-counting colon deep-learning enteric enteric-nervous-system glia microscopy neuron neurons
Last synced: 6 months ago · JSON representation

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Analysis and characterisation of cells within the gut wall using deep learning models. The current focus is on studying enteric neurons and enteric glia.

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calcium-imaging cell-counting colon deep-learning enteric enteric-nervous-system glia microscopy neuron neurons
Created over 4 years ago · Last pushed 7 months ago
Metadata Files
Readme License Citation

README.md

Gut Analysis Toolbox

DOI:10.1101/2024.01.17.576140

To get started with using GAT, please go to the Documentation.


GAT_overview

Gut Analysis Toolbox or GAT allows the semi-automated analysis of the cells within the enteric nervous system of the gastrointestinal tract in 2D. GAT enables quantification of enteric neurons and their subtypes in gut wholemounts. It can run in FIJI or QuPath, popular image analysis softwares in microscopy and uses deep learning models to segment cells of interest.

The workflows are available as video tutorials on Youtube in two separate playlists for Fiji and QuPath.

What you can do with GAT: * Semi-automated analysis of number of enteric neurons: Uses pan-neuronal marker Hu or anything with similar labelling * Normalise counts to the number of ganglia. * Count number of neuronal subtypes, such as ChAT, nNOS etc.. * Spatial analysis using number of neighboring cells. * Calcium imaging analysis: Alignment of images and extraction of normalised traces (Fiji)

Reporting problems

If you have any difficulties, suggestions or find any bugs, you can either:

  • Use this link to access a Google form for submitting issues or questions.

OR

OR

Installing and configuring GAT in Fiji

Click on this video to watch how to install and configure FIJI and GAT

Youtube

GAT requires the following update sites: * BIG-EPFL * CSBDeep * clij * clij2 * DeepImageJ * IJBP-Plugins (MorphoLibJ) * StarDist * PT-BIOP * 3D ImageJ Suite

GAT update site: https://sites.imagej.net/GutAnalysisToolbox/


Model files for use in FIJI

The GAT models are located in Fiji.app/models folder and contains 3 separate model files:

  • Enteric neuron model: 2Dentericneuronv41.zip

StarDist model to segment enteric neurons labelled with Hu, a pan-neuronal marker - Enteric neuron subtype model: 2Dentericneuronsubtypev4_1.zip

StarDist model to segment enteric neuronal subtypes. It has been trained on images with labelling for: * neuronal nitric oxide synthase (nNOS) * Calbindin * Calretinin * Mu-opioid receptor (MOR) reporter (mCherry) * Delta-opioid receptor (DOR) reporter (GFP) * Choline acetyltransferase (ChAT) * Neurofilament (NFM) - Ganglia model folder: 2DGangliaRGB_v3

DeepImageJ-based UNet model to segment ganglia. Needs both Hu and a neuronal/glial marker labelling the ganglia

Model files for use in QuPath

Click to Download QuPath model and scripts

Check here for more detail on using QuPath models


Accessing training data

To download the training data, notebooks and associated models please go to the following Zenodo link:

DOI


Citing

Sorensen L, Humenick A, Poon SSB, Han MN, Mahdavian NS, Rowe MC, Hamnett R, Gmez-de-Mariscal E, Neckel PH, Saito A, Mutunduwe K, Glennan C, Haase R, McQuade RM, Foong JPP, Brookes SJH, Kaltschmidt JA, Muoz-Barrutia A, King SK, Veldhuis NA, Carbone SE, Poole DP, Rajasekhar P. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci. 2024 Sep 2:jcs.261950. doi: 10.1242/jcs.261950.

Owner

  • Name: Pradeep Rajasekhar
  • Login: pr4deepr
  • Kind: user
  • Location: Melbourne, Australia
  • Company: Walter and Eliza Hall Institute of Medical Research

Bioimage Analyst with a background in neuroscience and engineering. Particularly interested in Python, machine learning, data visualisation and analysis.

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