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).
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 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=0.852, mAR=0.998, AP(IoU=0.1)=0.858, AR(IoU=0.1)=1.0.
Please note that this bundle is non-deterministic because of the max pooling layer used in the network. Therefore, reproducing the training process may not get exactly the same performance. Please refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducibility for more details about reproducibility.
The validation accuracy in this curve is the mean of mAP, mAR, AP(IoU=0.1), and AR(IoU=0.1) in Coco metric.
lung_nodule_ct_detection bundle supports acceleration with TensorRT through the ONNX-TensorRT method. The table below displays the speedup ratios observed on an A100 80G GPU. Please note that when using the TensorRT model for inference, the
force_sliding_window parameter in the
inference.json file must be set to
true. This ensures that the bundle uses the
SlidingWindowInferer during inference and maintains the input spatial size of the network. Otherwise, if given an input with spatial size less than the
infer_patch_size, the input spatial size of the network would be changed.
|method||torch_fp32(ms)||torch_amp(ms)||trt_fp32(ms)||trt_fp16(ms)||speedup amp||speedup fp32||speedup fp16||amp vs fp16|
model computationmeans the speedup ratio of model's inference with a random input without preprocessing and postprocessing
end2endmeans run the bundle end-to-end with the TensorRT based model.
torch_ampare for the PyTorch models with or without
trt_fp16are for the TensorRT based models converted in corresponding precision.
speedup fp16are the speedup ratios of corresponding models versus the PyTorch float32 model
amp vs fp16is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.
Currently, the only available method to accelerate this model is through ONNX-TensorRT. However, the Torch-TensorRT method is under development and will be available in the near future.
This result is benchmarked under:
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 --config_file configs/train.json
Please note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using
python -m monai.bundle run --config_file configs/train.json --dataset_dir <actual dataset path>
trainconfig to execute evaluation with the trained model:
python -m monai.bundle run --config_file "['configs/train.json','configs/evaluate.json']"
python -m monai.bundle run --config_file configs/inference.json
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
python -m monai.bundle trt_export --net_id network_def --filepath models/model_trt.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json --precision <fp32/fp16> --input_shape "[1, 1, 512, 512, 192]" --use_onnx "True" --use_trace "True" --onnx_output_names "['output_0', 'output_1', 'output_2', 'output_3', 'output_4', 'output_5']" --network_def#use_list_output "True"
python -m monai.bundle run --config_file "['configs/inference.json', 'configs/inference_trt.json']"
 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
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