afids-CNN

Leveraging the recent release of the anatomical fiducial framework for developing an open software infrastructure to solve the landmark regression problem on 3D MRI images

Preparation

1- install poetry and configure cache directory 2- poetry install and shell to activate environment

Processing imaging data for training can be found in the following repo (https://github.com/afids/autoafids_prep)

1 - rigid registraion to MNI template 2 - conforming image to 1mm iso res 3 - intensity normalization (i.e., WM to 110) followed by minmax norm

Processing landmark data (AFIDs)

1 - extract points from landmark file (.fcsv is supported) 2 - extact a landmark Euclidean distance map (could be considered probability map; each voxel communicates the distance to a AFID of interest)

Machine learning

1 - a standard 3D Unet