Nov 13, 2024
Version 3
TruAI Neuromelanin Quantification V.3
- Anastasia Filimontseva1,2,
- Zac Chatterton1,2,
- Glenda Halliday1,2,
- YuHong Fu1,2
- 1Brain and Mind Centre & Faculty of Medicine and Health School of Medical Sciences, The University of Sydney, Sydney, NSW 2050, Australia;
- 2Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815
Protocol Citation: Anastasia Filimontseva, Zac Chatterton, Glenda Halliday, YuHong Fu 2024. TruAI Neuromelanin Quantification. protocols.io https://dx.doi.org/10.17504/protocols.io.5qpvo345bv4o/v3Version created by Anastasia Filimontseva
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: January 11, 2024
Last Modified: November 13, 2024
Protocol Integer ID: 112006
Keywords: ASAPCRN
Funders Acknowledgement:
Michael J. Fox Foundation for Parkinson’s Research (MJFF)
Grant ID: ASAP-020505
Abstract
Quantification of area and optical density of intracellular neuromelanin with TruAI.
Loading training label function in TruAI software
Loading training label function in TruAI software
Open scanned images with Olympus VS200 Desktop (EVIDENT Technology GmbH, ver. 4.1.1 build 29408).
Under the ‘Detect’ window, select ‘Training Labels’.
Creating NM foreground training label class
Creating NM foreground training label class
Create a new training label class by selecting the star icon.
A new foreground class will appear under level 1.
Rename as ‘NM’ referring to neuromelanin.
Optimize background and NM training label class
Optimize background and NM training label class
Under the automatically created ‘background’ level, select the fill icon and outline an area of the section with approximately 50-100 NM granules. The outlined area must be continuous.
Select the ‘NM’ class and use the same fill option to outline the shape of each NM granule as closely as possible.
To ensure maximum accuracy of the neural training, NM granules of all sizes and densities should be drawn.
Further, all NM granules in each background area should be drawn.
Save and export this NM training class set and apply it to 5-10 scanned sections.
Repeat steps 1-5 identically for each image.
Deep learning training
Deep learning training
In the ‘Deep Learning’ window, select ‘New Training’ and pick ‘Image Segmentation’ option.
In the ‘New Training: Input and Output’ pop-up window, load all images used in the ‘Optimize background and NM training label class’ section.
Ensure the input channel is RGB.
Select ‘Specific Network (RGB)’ under ‘Training Configuration’.
Start training and run until at least 0.85 similarity is reached.
Applying NM neural network to the scanned sections
Applying NM neural network to the scanned sections
After successful completion of deep learning training, open a scanned brightfield section with Olympus VS200 Desktop (EVIDENT Technology GmbH, ver. 4.1.1 build 29408).
In the ‘Detect’ window, select the ‘Count and Measure’ drop down menu and pick the ‘New ROI’ option to create ROIs for further anatomical delineation.
Once all the ROIs have been drawn, select the ‘Neural Network Segmentation’ option above.
In the ‘Neural Network Segmentation’ pop-up window, load the saved NM neural network and adjust the ‘Detection threshold’ to 0%.
Proceed by selecting ‘Count and Measure on ROI’.
Thresholding and analysis of intracellular NM granules
Thresholding and analysis of intracellular NM granules
The generated results appear in the ‘Count and Measure Results’.
The corresponding ROI for each NM granule can be found in the ‘ROI’ column. Other computed parameters relevant to size and intensity of NM are also listed, eg. ‘Area µm2’, ‘Mean (Color Intensity Value)’, ‘Mean (Saturation)’, and ‘Mean (Hue)’.
To identify the intracellular NM population from the total NM granules detected, the modality of the area distribution was used. The code for generating this threshold can be found on doi:10.5281/zenodo.10494622.
It was determined that the threshold for intracellular NM was above 78.3 µm2. This size cutoff was used in this analysis.