Segmentation of neurons in microscopy images

Version 1.0.3 (1,25 Mo) par Omer Yuval
Set up, train and apply a neural network for segmentation of neurons in microscopy images.
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Mise à jour 13 août 2021

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Quick start:
0. Set network and training parameters in Params.m.
1. Prepare input and output images:
Im_In = {Im_In_1,Im_In_2,Im_In_3};
Im_Out = {Im_Out_1,Im_Out_2,Im_Out_3};
2. Generate training set:
Generate_Dataset(Im_In,Im_Out);
3. Train:
net = Train;
4. Apply the trained network to an image:
[Im_Out,Im_Label] = Segment_Neuron(net,Im_In_4);
imshow(Im_Label);
* Example raw and annotated neuron images can be found in this paper [1].
* An example pre-trained network is included.
* Please cite this paper [1].
Advanced options:
- Control sample size (see "Input_Size" in Params.m).
- Control class weights during training (see "Class_Weights" in Params.m).
- Control the minimum number of neuron pixels in training samples (see "Functions" block in Params.m).
- You can generate the training set locally and train on another machine (see "Paths" block in Params.m).

Citation pour cette source

Omer Yuval (2024). Segmentation of neurons in microscopy images (https://www.mathworks.com/matlabcentral/fileexchange/97547-segmentation-of-neurons-in-microscopy-images), MATLAB Central File Exchange. Récupéré le .

Yuval, Omer, et al. “Neuron Tracing and Quantitative Analyses of Dendritic Architecture Reveal Symmetrical Three-Way-Junctions and Phenotypes of Git-1 in C. Elegans.” PLOS Computational Biology, edited by Hugues Berry, vol. 17, no. 7, Public Library of Science (PLoS), July 2021, p. e1009185, doi:10.1371/journal.pcbi.1009185.

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Compatibilité avec les versions de MATLAB
Créé avec R2021a
Compatible avec les versions R2020b et ultérieures
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