Neuromelanin-positive Neuron Density in Substantia Nigra Image Analysis
Hemanth Ramesh Nelvagal, Toby J Curless, Zane Jaunmuktane
Published: 2023-12-08 DOI: 10.17504/protocols.io.14egn2kq6g5d/v1
Abstract
The protocol covers the steps to measure neuromelanin-positive neuron density in substantia nigra using image analysis tools including NZConnect (Hamamatsu), a web-based whole-slide image (WSI) viewer, Cellpose and QuPath.
Steps
Annotation and Deconvolution
1.
Manually annotate the substantia nigra on NZConnect (Hamamatsu), a web-based whole-slide image (WSI) viewer.
2.
Download the annotations using a Python script, and then import into QuPath [1] using a Groovy script
Segmentation and Calculating Neuromelanin-positive Cell Density
3.
4. References [1] Bankhead, P., Loughrey, M.B., Fernández, J.A. et al. QuPath: Open source software for digital pathology image analysis. Sci Rep 7, 16878 (2017). [1] Bankhead, P., Loughrey, M.B., Fernández, J.A. et al. QuPath: Open source software for digital pathology image analysis. Sci Rep 7, 16878 (2017). https://doi.org/10.1038/s41598-017-17204-5[2] Stringer, C., Wang, T., Michaelos, M. et al. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021). [2] Stringer, C., Wang, T., Michaelos, M. et al. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021). https://doi.org/10.1038/s41592-020-01018-x[3] Pachitariu, M., Stringer, C. Cellpose 2.0: how to train your own model. Nat Methods 19, 1634–1641 (2022). [3] Pachitariu, M., Stringer, C. Cellpose 2.0: how to train your own model. Nat Methods 19, 1634–1641 (2022). https://doi.org/10.1038/s41592-022-01663-4 [4] BIO/Pqupath-extension-cellposehttps://github.com/BIOP/qupath-extension-cellpose
Calculate neuromelanin-positive cell density by the number of neuromelanin-positive cells divided by the area of the region of interest (neuromelanin-positive cells per mm^2).
Note
