TITLE: Quantification of area and optical density of intracellular neuromelanin with TruAI in H&amp;E stained sections
AUTHORS: Marta Gonzalez-Sepulveda, Thais Cuadros, Miquel Vila

[DESCRIPTION]
Quantification of area and optical density of intracellular neuromelanin with TruAI in H&E stained sections

[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 27564). 
SECTION: Loading training label function in TruAI Software
2. Under the ‘Detect’ window, select ‘Training Labels’.  
SECTION: Creating training label classes
3. Create a new training label class by selecting the star icon. Change the class name and the color by double click on it.
SECTION: Creating training label classes
4. A new foreground class will appear under level 1.
SECTION: Creating training label classes
5. Rename as ‘NM’ referring to intracellular neuromelanin.
SECTION: Creating training label classes
6. Repeat steps 3-5 for all the label classes: extracellular NM, Non-pigmented cells and background.
SECTION: Creating training label classes
7. Once you have added all your classes you have to save the set of training classes to use it in several images. It is very important that the classes you use in different images have exactly the same names and colors and the easy way to do it is by saving the set.
SECTION: Creating training label classes
8. Once you have the set you can load it in all your images of interest by clicking the folder icon once the image is opened.
SECTION: Manage sets of training label classes
9. The hand icon lets you manage the label sets: here you can export a label class to be able to import it in other computer. This is interesting to export a set of label classes from the workstation to your own computer. As we said before, to have exactly the same label classes is very important to analyze a set of images because, if not, the algorithm will not be correctly processed.
SECTION: Optimize background and all training label classes
10. 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 all training label classes
11. 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 all training label classes
12. To ensure maximum accuracy of the neural training, NM granules of all sizes and densities should be drawn.
SECTION: Optimize background and all training label classes
13. Further, all NM granules in each background area should be drawn. 
SECTION: Optimize background and all training label classes
14. Save and export this NM training class set and apply it to 5-10 scanned sections. 
SECTION: Optimize background and all training label classes
15. Repeat steps 11-14 identically for each image and for each training class.
SECTION: Deep learning training
16. In the ‘Deep Learning’ window, select ‘New Training’ and pick ‘Image Segmentation’ option. 
SECTION: Deep learning training
17. In the ‘New Training: Input and Output’ pop-up window, load all images used in the ‘Optimize background and all training label classes’ subsection. 
SECTION: Deep learning training
18. Ensure the input channel is RGB. 
SECTION: Deep learning training
19. Select ‘Specific Network (RGB)’ under ‘Training Configuration’. 
SECTION: Deep learning training
20. Start training and run until at least 0.85 similarity is reached.
SECTION: Applying neural network to the scanned sections:
21. After successful completion of deep learning training, open a scanned brightfield section with Olympus VS200 Desktop (EVIDENT Technology GmbH, ver. 4.1.1 build 27564).

SECTION: Applying neural network to the scanned sections:
22. 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 neural network to the scanned sections:
23. Once all the ROIs have been drawn, select the ‘Neural Network Segmentation’ option above.
SECTION: Applying neural network to the scanned sections:
24. In the ‘Neural Network Segmentation’ pop-up window, load the saved neural network and adjust the ‘Detection threshold’ to 0%. Proceed by selecting ‘Count and Measure on ROI’.
SECTION: Thresholding and analysis of the results
25. 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, e.g. ‘Area µm2’, ‘Mean (Color Intensity Value)’, ‘Mean (Saturation)’, and ‘Mean (Hue)’.