A pre-trained model for volumetric (3D) detection of the lung nodule from CT image.
This model is trained on LUNA16 dataset (https://luna16.grand-challenge.org/Home/), using the RetinaNet (Lin, Tsung-Yi, et al. "Focal loss for dense object detection." ICCV 2017. https://arxiv.org/abs/1708.02002).
The dataset we are experimenting in this example is LUNA16 (https://luna16.grand-challenge.org/Home/), which is based on LIDC-IDRI database [3,4,5].
LUNA16 is a public dataset of CT lung nodule detection. Using raw CT scans, the goal is to identify locations of possible nodules, and to assign a probability for being a nodule to each location.
Disclaimer: We are not the host of the data. Please make sure to read the requirements and usage policies of the data and give credit to the authors of the dataset! We acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health, and their critical role in the creation of the free publicly available LIDC/IDRI Database used in this study.
We follow the official 10-fold data splitting from LUNA16 challenge and generate data split json files using the script from nnDetection.
Please download the resulted json files from https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/LUNA16_datasplit-20220615T233840Z-001.zip.
In these files, the values of "box" are the ground truth boxes in world coordinate.
The raw CT images in LUNA16 have various of voxel sizes. The first step is to resample them to the same voxel size. In this model, we resampled them into 0.703125 x 0.703125 x 1.25 mm.
Please following the instruction in Section 3.1 of https://github.com/Project-MONAI/tutorials/tree/main/detection to do the resampling.
The mhd/raw original data can be downloaded from LUNA16. The DICOM original data can be downloaded from LIDC-IDRI database [3,4,5]. You will need to resample the original data to start training.
Alternatively, we provide resampled nifti images and a copy of original mhd/raw images from LUNA16 for users to download.
The training was performed with the following:
In Training Mode: A dictionary of classification and box regression loss.
In Evaluation Mode: A list of dictionaries of predicted box, classification label, and classification score.
Coco metric is used for evaluating the performance of the model. The pre-trained model was trained and validated on data fold 0. This model achieves a mAP=0.853, mAR=0.994, AP(IoU=0.1)=0.862, AR(IoU=0.1)=1.0.
The validation accuracy in this curve is the mean of mAP, mAR, AP(IoU=0.1), and AR(IoU=0.1) in Coco metric.
In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.
For more details usage instructions, visit the MONAI Bundle Configuration Page.
python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
trainconfig to execute evaluation with the trained model:
python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json']" --logging_file configs/logging.conf
python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
With the same command, we can execute inference on original LUNA16 images by setting
"whether_raw_luna16": true in
inference.json. Remember to also set
"data_list_file_path": "$@bundle_root + '/LUNA16_datasplit/mhd_original/dataset_fold0.json'" and change
Note that in inference.json, the transform "LoadImaged" in "preprocessing" and "AffineBoxToWorldCoordinated" in "postprocessing" has
This depends on the input images. LUNA16 needs
It is possible that your inference dataset should set
 Lin, Tsung-Yi, et al. "Focal loss for dense object detection." ICCV 2017. https://arxiv.org/abs/1708.02002)
 Baumgartner and Jaeger et al. "nnDetection: A self-configuring method for medical object detection." MICCAI 2021. https://arxiv.org/pdf/2106.00817.pdf
 Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., Van Beek, E. J. R., Yankelevitz, D., Biancardi, A. M., Bland, P. H., Brown, M. S., Engelmann, R. M., Laderach, G. E., Max, D., Pais, R. C. , Qing, D. P. Y. , Roberts, R. Y., Smith, A. R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G. W., Jude, C. M., Munden, R. F., Petkovska, I., Quint, L. E., Schwartz, L. H., Sundaram, B., Dodd, L. E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A. V., Gupte, S., Sallam, M., Heath, M. D., Kuhn, M. H., Dharaiya, E., Burns, R., Fryd, D. S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B. Y., Clarke, L. P. (2015). Data From LIDC-IDRI [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX
 Armato SG 3rd, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Van Beeke EJ, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DP, Roberts RY, Smith AR, Starkey A, Batrah P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallamm M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics, 38: 915--931, 2011. DOI: https://doi.org/10.1118/1.3528204
 Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7
This training and inference pipeline was developed by NVIDIA. It is based on a model developed by NVIDIA researchers. This software has not been cleared or approved by FDA or any regulatory agency. MONAI pre-trained models are for developmental purposes only and cannot be used directly for clinical procedures.
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