Automated Vertebral Landmarks and Spinal Curvature Estimation using Non-directional Part Affinity Fields
Acquire the datasets (see below).
Unzip the train, val, data into dataroot/boostnet_labeldata
Unzip the test data into dataroot/submit_test_images folder
Merge train and val csv annotation, put into dataroot/trainval-labels folder
By default, dataroot = ../ScoliosisData
Run resize_images.py to apply augmentation and resize
Run train.py to train on "train" set
Run eval.py to produce heatmaps
Run resize_images.py to flipLR, resize
Run train.py --trainval to train
Run eval.py --trainval to produce heatmaps
Run cobb_angle_eval.py to evaluate landmark pairs and Cobb angles
Dataset provided by:
Wu, Hongbo, et al. "Automatic landmark estimation for adolescent idiopathic scoliosis assessment using BoostNet." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2017.