TITLE: TruAI Neuromelanin Quantification
AUTHORS: Anastasia Filimontseva, Zac Chatterton, Glenda Halliday, YuHong Fu

[DESCRIPTION]
Quantification of area and optical density of intracellular neuromelanin with TruAI. 

[STEPS]
SECTION: Loading training label function in TruAI software
1. Open scanned images with Olympus VS200 Desktop (EVIDENT Technology GmbH, ver. 4.1.1 build 29408).
SECTION: Loading training label function in TruAI software
2. Under the ‘Detect’ window, select ‘Training Labels’.
SECTION: Creating NM foreground training label class
3. Create a new training label class by selecting the star icon.
SECTION: Creating NM foreground training label class
4. A new foreground class will appear under level 1.
SECTION: Creating NM foreground training label class
5. Rename as ‘NM’ referring to neuromelanin.
SECTION: Optimize background and NM training label class
6. 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.
SECTION: Optimize background and NM training label class
7. Select the ‘NM’ class and use the same fill option to outline the shape of each NM granule as closely as possible.
SECTION: Optimize background and NM training label class
8. To ensure maximum accuracy of the neural training, NM granules of all sizes and densities should be drawn.
SECTION: Optimize background and NM training label class
9. Further, all NM granules in each background area should be drawn.
SECTION: Optimize background and NM training label class
10. Save and export this NM training class set and apply it to 5-10 scanned sections.
SECTION: Optimize background and NM training label class
11. Repeat steps 1-5 identically for each image.
SECTION: Deep learning training
12. In the ‘Deep Learning’ window, select ‘New Training’ and pick ‘Image Segmentation’ option.
SECTION: Deep learning training
13. In the ‘New Training: Input and Output’ pop-up window, load all images used in the ‘Optimize background and NM training label class’ section.
SECTION: Deep learning training
14. Ensure the input channel is RGB.
SECTION: Deep learning training
15. Select ‘Specific Network (RGB)’ under ‘Training Configuration’.
SECTION: Deep learning training
16. Start training and run until at least 0.85 similarity is reached.
SECTION: Applying NM neural network to the scanned sections
17. 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). 
SECTION: Applying NM neural network to the scanned sections
18. 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.
SECTION: Applying NM neural network to the scanned sections
19. Once all the ROIs have been drawn, select the ‘Neural Network Segmentation’ option above.
SECTION: Applying NM neural network to the scanned sections
20. In the ‘Neural Network Segmentation’ pop-up window, load the saved NM neural network and adjust the ‘Detection threshold’ to 0%.
SECTION: Applying NM neural network to the scanned sections
21. Proceed by selecting ‘Count and Measure on ROI’.
SECTION: Thresholding and analysis of intracellular NM granules
22. The generated results appear in the ‘Count and Measure Results’.
SECTION: Thresholding and analysis of intracellular NM granules
23. 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)’.
SECTION: Thresholding and analysis of intracellular NM granules
24. To identify the intracellular NM population from the total NM granules detected, the modality of the area distribution was used. 
SECTION: Thresholding and analysis of intracellular NM granules
25. It was determined that minimum 78.3 µm2 was the threshold area for intracellular NM and was used as the cut-off in this analysis.