Quickstart

Sarcomere structure analysis

More detailed instructions see Structural analysis.

from sarcasm import *

# initialize Structure object for tif-file
filename = '/path/to/file.tif'
sarc_obj = Structure(filename)

# detect sarcomere Z-bands, M-bands, orientation, sarcomere mask and cell mask by deep learning
sarc_obj.detect_sarcomeres()

# analyze cell mask
sarc_obj.analyze_cell_mask()

# analyze Z-band morphology
sarc_obj.analyze_z_bands()

# analyze sarcomere vectors
sarc_obj.analyze_sarcomere_vectors()

# analyze myofibrils
sarc_obj.analyze_myofibrils()

# analyze sarcomere domains
sarc_obj.analyze_sarcomere_domains()

Sarcomere motion analysis

More detailed instruction see Motion analysis.

from sarcasm import *

# initialize Structure object for tif-file
filename = '/path/to/file.tif'
sarc_obj = Structure(filename)

# automatically detect lines of interest (LOIs) for sarcomere tracking
sarc_obj.detect_lois(n_lois=4)

# get list of LOIs and select single LOI
list_lois = sarc_obj.get_list_lois()
file, loi = list_lois[0]

# initialize Motion object for LOI
mot_obj = Motion(file, loi)

# track individual Z-bands
mot_obj.detect_peaks()
mot_obj.track_z_bands()

# predict contraction intervals using neural network ContractionNet and analyze contractions
mot_obj.detect_analyze_contractions()

# calculate sarcomere length change and velocity of individual sarcomeres and average
mot_obj.get_trajectories()

# analyze individual and average sarcomere trajectories
mot_obj.analyze_trajectories()