This code performs a semi-automated image analysis procedure to identify microtubules in serial section transmission electron micrographs (ssTEM) of a C. elegans PLM neuron.
Note, this code is based on Python 3.7
Scientific paper using this code: https://doi.org/10.1016/j.bpj.2022.11.2946
Files:
- Automated_Analysis: Performs automated template matching to identify microtubules in the full image set.
- Manual_Edits: Allows the user to make edits to the automated results by clicking missed microtubules and drawing boxes around false positives.
- Registration: Registers microtubule match locations in adjacent images to trace microtubules throughout the full image stack.
- RealisticGeometry_ABAQUSInput: Adds randomly dispersed crosslinks to the ssTEM-based microtubule geometry and converts the results into an ABAQUS mesh. Generates an ABAQUS input file.
- IdealizedGeometry_ABAQUSInput: Generates an idealized geometry of regularly spaced, equally-sized microtubules with randomly dispersed crosslinks. Generates an ABAQUS input file.
Dependencies:
OpenCV v3.4.2: https://github.com/opencv/opencv
NumPy v1.18.1
pandas v1.0.3
SciPy v1.4.1
scikit-learn v0.22.1
openpyxl v3.0.6
pycpd: https://github.com/siavashk/pycpd