Reference projects:

https://github.com/brendan0403/BonesSegmentation

https://github.com/zhixuhao/unet

Pre_processing.py: Python script to separate (train, validate, test) and pre-process image data

training.py: train the network, check how well the predictions are with test data and save result masks.

Usage notes:

Scripts are currently set for one orientation and one tissue at a time.

Using patella cartilage as test set.

Modified preprocessing script to use .nii files and pull slices with masks of interest. Original script needed every slice to have an associated mask. Modified script can be given original .nii.

Using accuracy instead of dice coefficient

create ‘samplelabel’, ‘samplevolume’ and ‘data/npydata’ folders in folder with scripts: for convenience.

Put sample data and segmentation nii files in samplevolume and samplelabel folders.

File names have to be updated so that matching mask/segmentation/label and image volume are read in the same sequence.

‘results’ folder is created with predicted masks.

2021-06-23T10:05:55.755000000