Neuron traces are used to determine the pathway of axons/dendrites in 3D images of the brain. One common file format for traces is SWC, which stores the traces as a structually connected components (e.g. cylinders)
We would like to have a easy way to load data from a SWC into napari.
It is of interest to neuroscience researchers to develop and evaluate automatic neuron tracing algorithms. Visualizing SWC neuron traces as a layer on top of the brain image can help researchers test these tracing algorithms.
I am on a project working with the neuron traces from the MouseLight dataset, and we are approaching the automatic neuron tracing problem as a semantic segmentation problem. As a first step we are converting the ground truth SWCs into binary image labels, so that we can use supervised learning methods to generate neuron segmentations/labels. Conversions are done semi-automatically. Labels generated from methods such as Ostu thresholding will need to be manually corrected.
Because napari is built with python, it is easy to visualize the effects of image processing algorithms or segmentation algorithms from common python packages. Furthermore, napari has great tools for manually making and editing labels.
Pitch
A simple way to implement this feature is to read a SWC and render it as a path shape layer.
vtwang
commented
4 years ago
🚀 Feature
Neuron traces are used to determine the pathway of axons/dendrites in 3D images of the brain. One common file format for traces is SWC, which stores the traces as a structually connected components (e.g. cylinders)
We would like to have a easy way to load data from a SWC into napari.
example of neuron traces
Motivation
It is of interest to neuroscience researchers to develop and evaluate automatic neuron tracing algorithms. Visualizing SWC neuron traces as a layer on top of the brain image can help researchers test these tracing algorithms.
I am on a project working with the neuron traces from the MouseLight dataset, and we are approaching the automatic neuron tracing problem as a semantic segmentation problem. As a first step we are converting the ground truth SWCs into binary image labels, so that we can use supervised learning methods to generate neuron segmentations/labels. Conversions are done semi-automatically. Labels generated from methods such as Ostu thresholding will need to be manually corrected.
Because napari is built with python, it is easy to visualize the effects of image processing algorithms or segmentation algorithms from common python packages. Furthermore, napari has great tools for manually making and editing labels.
Pitch
A simple way to implement this feature is to read a SWC and render it as a
pathshape layer.