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python add self-supervised body-part regressor Mar 22, 2018
snapshots add self-supervised body-part regressor Mar 22, 2018
test_data
test_data_with_score
train_data
config.yml add self-supervised body-part regressor Mar 22, 2018
readme.md add self-supervised body-part regressor Mar 22, 2018
sample_results.png
solver.prototxt
test.prototxt
test_image_list_example.txt add self-supervised body-part regressor Mar 22, 2018
train.prototxt
train.sh
train_volume_list_example.txt

readme.md

Self-supervised body part regressor (SSBR)

Developed by Ke Yan (ke.yan@nih.gov, yanke23.com), Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health Clinical Center

  • Ke Yan, Le Lu, Ronald Summers, "Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks", IEEE ISBI, 2018, https://arxiv.org/abs/1707.03891
  • Ke Yan, Xiaosong Wang, Le Lu, Ling Zhang, Adam Harrison, Mohammadhad Bagheri, Ronald M. Summers, "Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database", IEEE CVPR, 2018, https://arxiv.org/abs/1711.10535

Function: Predict a continuous score for an axial slice in a CT volume which indicates its relative position in the body, e.g. the figure below. The actual correspondence between values and positions needs to be observed when using. See the paper.

Samples of unsupervisedly learned body-part scores: sample body-part scores

Usage:

  1. For inference (predicting the body-part score), put images in test_data/, then run python/deploy.py. A trained model is in snapshots folder.
  2. When training, see the requirements below and run train.sh.
  3. The provided trained model was trained on 4400 unlabeled CT volumes with various reconstruction filters, scan ranges, and pathological conditions. Random 2D patch cropping was used when training. It is expected to be more accurate in shoulder, chest, abdomen, and pelvis because of the training data.
  4. Input soft tissue window (-175~275 HU) 8-bit images with size 128x128. If your data are different in windowing, image size, scan range etc., it is easy to retrain the algorithm to get a better model for your application. It is also possible to extend the algorithm to sagittal/coronal planes, MR volumes, etc.
  5. The output of SSBR can be used to roughly locate slices of certain body-parts, input to other CAD algorithms as features, detect abnormal volumes, and so on. See paper.

Requirement:

  1. Standard caffe, put in caffe folder.
  2. (for training only) VGG-16 pretrained caffemodel (optional, because the algorithm works well even if trained from scratch given enough data).
  3. (for training only) Unlabeled training volumes, each volume stored in a folder of 2D slices named by <slice_index>.png. List the names of volume folders in a list file and put the list file's name in TRAIN_IMDB of train.sh. Specify the name of the folder containing all volumes in DATA_DIR of config.yml. If you want to use different data format, change data_layer.py.

Thanks to the code of py-faster-rcnn.