DeepLung:

Please add paper into reference if the repository is helpful to you.

Zhu, Wentao, Chaochun Liu, Wei Fan, and Xiaohui Xie. "DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification." IEEE WACV, 2018.

Dependecies: Ubuntu 14.04, python 2.7, CUDA 8.0, cudnn 5.1, h5py (2.6.0), SimpleITK (0.10.0), numpy (1.11.3), nvidia-ml-py (7.352.0), matplotlib (2.0.0), scikit-image (0.12.3), scipy (0.18.1), pyparsing (2.1.4), pytorch (0.1.10+ac9245a) (anaconda is recommended)

Download LUNA16 dataset from https://luna16.grand-challenge.org/data/

Download LIDC-IDRI dataset from https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI

For preprocessing, run ./DeepLung/prepare.py. The parameters for prepare.py is in config_training.py. *_data_path is the unzip raw data path for LUNA16. *_preprocess_result_path is the save path for the preprocessing. *_annos_path is the path for annotations. *_segment is the path for LUNA16 segmentation, which can be downloaded from LUNA16 website.

Use run_training.sh to train the detector. You can use the resnet or dual path net model by revising --model attribute. After training and test are done, use the ./evaluationScript/frocwrtdetpepchluna16.py to validate the epoch used for test. After that, collect all the 10 folds' prediction, use ./evaluationScript/noduleCADEvaluationLUNA16.py to get the FROC for all 10 folds. You can directly run noduleCADEvaluationLUNA16.py, and get the performance in the paper.

The trained model is in ./detector/dpnmodel/ or ./detector/resmodel/ The performances on each fold are (these results are in the supplement)

Method Deep 3D Res18 Deep 3D DPN26 Fold 0 0.8610 0.8750 Fold 1 0.8538 0.8783 Fold 2 0.7902 0.8170 Fold 3 0.7863 0.7731 Fold 4 0.8795 0.8850 Fold 5 0.8360 0.8095 Fold 6 0.8959 0.8649 Fold 7 0.8700 0.8816 Fold 8 0.8886 0.8668 Fold 9 0.8041 0.8122

The performances on each average false positives in FROC compared with other approaches (these results are in the supplement)

Methods 0.125 0.25 0.5 1 2 4 8 FROC DIAG_ConvNet 0.692 0.771 0.809 0.863 0.895 0.914 0.923 0.838 ZENT 0.661 0.724 0.779 0.831 0.872 0.892 0.915 0.811 Aidence 0.601 0.712 0.783 0.845 0.885 0.908 0.917 0.807 MOT_M5Lv1 0.597 0.670 0.718 0.759 0.788 0.816 0.843 0.742 VisiaCTLung 0.577 0.644 0.697 0.739 0.769 0.788 0.793 0.715 Etrocad 0.250 0.522 0.651 0.752 0.811 0.856 0.887 0.676 Dou et al 2017 0.659 0.745 0.819 0.865 0.906 0.933 0.946 0.839 3D RES 0.662 0.746 0.815 0.864 0.902 0.918 0.932 0.834 3D DPN 0.692 0.769 0.824 0.865 0.893 0.917 0.933 0.842

For nodule classification, first clean the data from LIDC-IDRI. Use the ./data/extclsshpinfo.py to extract nodule labels. humanperformance.py is used to get the performance of doctors.

dimcls.py is used to get the classification based on diameter. nodclsgbt.py is used to get the performance based on GBM, nodule diameter and nodule pixel. pthumanperformance.py is used for patient-level diagnosis performance. kappatest.py is used for kappa value calculation in the paper.

For classification using DPN, use the code in main_nodcls.py. Use the testdet2cls.py to test the trained model. You may revise the code a little bit for different test settings.

For system's classification, that is classification based on detection. First, use the detection's test script in the run_training.sh to get the detected nodules for training CTs. Use the det2cls.py to train the model. And use the testdet2cls.py to test the trained model. You may revise the code a little bit for different test settings.

Feel free to ask any questions. Wentao Zhu, [email protected]