Code documentation of study: effects of optical defocus on choroidal thickness
Purpose: to compile and to analyses topographical choroidal maps
Jupyter notebook, Python: average_rnfl_layers_after_im_registration.jpynb
Aim: find macula centre (low RNFL thickness) for landmark matching of choroidal thickness maps
Jupyter notebook, Python: gif_from_projection_of_anterior_segment.jpynb
Aim: create gif from intra participant z-projection of anterior segment of the eye from OCT scans to estimate quality of transformation, i.e., finding shifts between scans
Jupyter notebook, Python: averaged_choroid_thickess_data_from_reg_scansV2.jpynb
Aim: use transformation info from z-projections to align intra participant choroidal thickness maps
Jupyter notebook, Python: macula_matching_averaging_of_layer_thickness_V3.jpynb
Aim: compile topographical choroidal thickness map
- Use macula centre position to match inter participant choroidal thickness maps
- Disregard data from optic nerve head using annotated z-projections
- Apply outlier filtering
- Calculating residuals for post and pre intervention
Jupyter notebook, Python: choroidal_thickness_residual_visualisation.jpynb
Aim: visualise data and proceed stats
- Statistical analyses on interparticipant landmark matched choroid thickness residual data
- Visualization of the residual of interparticipant landmark matched choroid thickness data post and pre intervention
Dependencies of jpynb:
from oct_helpers_lib import OctDataAccess as get_px_meta
- Easy access of participant’s meta data e.g., file location from oct_helpers_lib import TopconSegmentationData as TSD
- Extracting specific data from OCT data extractor output file