Introduction¶

pyKNEEr is an image analysis workflow for open and reproducible research on femoral knee cartilage

Characteristics of pyKNEEr are exensively covered in our paper. Find below a concise summary.

Workflow¶

pyKNEEr workflow is composed of three modules:

Preprocessing to homogenize spatial characteristics and enhance intensities
Segmentation of femoral knee cartilage using an atlas-based method
Analysis of cartilage morphometry (thickness and volume) and relaxometry (fitting and EPG modeling)

Code¶

pyKNEEr:

Uses Jupyter notebook as a user-interface. If you are not familiar with Jupyter notebook, find a quick introduction on YouTube and basic commands here
Is written in python with SimpleITK to process images
Calls elastix, a toolbox for image registration used for atlas-based segmentation

Jupyter Notebooks¶

Jupyter notebooks are the user-interface of pyKNEEr. Once run, you can attached them directly to your paper for computational reproducibility of the results.

For each module of pyKNEEr there are several Jupyter notebooks (*.ipynb), independent from each other:

All notebooks have a similar structure:

Data structure¶

pyKNEEr requires data folders to be structured as follows:

Where:

reference: contains the reference (or target) image for the atlas-based segmentation
original: contains dicom folders of the images to analyze
preprocessed: contains images after preprocessing
registered: contains by-products of registration and it can be deleted after computations
segmented: contains segmentation masks
morphology: contains cartilage surfaces, thickness, and volume
relaxometry: contains masked relaxometry maps

Data file formats¶

Inputs and output files are in open formats:

dicom (.dcm): Format of original images to be processed
metafile (.mha): Format of images throughout the workflow
text (.txt): Format of image list inputs and morphology outputs
tabular (.csv): Format of output descriptive statistics in morphology and relaxometry analysis