add reference for LIDC dataset

README.md

@@ -16,17 +16,18 @@ LUNA16 is a public dataset of CT lung nodule detection. Using raw CT scans, the
 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!
 
 ## Data
-The dataset we are experimenting in this example is LUNA16 (https://luna16.grand-challenge.org/Home/).
+The dataset we are experimenting in this example is LUNA16 (https://luna16.grand-challenge.org/Home/), which is based on [LIDC/IDRI database](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI) [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!
+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](https://github.com/MIC-DKFZ/nnDetection/blob/main/projects/Task016_Luna/scripts/prepare.py).
 The resulted json files can be downloaded 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. The code can be found in Section 3.1 of https://github.com/Project-MONAI/tutorials/tree/main/detection
+In this model, we resampled them into 0.703125 x 0.703125 x 1.25 mm. The code of resampling can be found in Section 3.1 of https://github.com/Project-MONAI/tutorials/tree/main/detection
 
 ## Training configuration
 The training was performed with at least 12GB-memory GPUs.
@@ -83,3 +84,9 @@ This is an example, not to be used for diagnostic purposes.
 [1] Lin, Tsung-Yi, et al. "Focal loss for dense object detection." ICCV 2017. https://arxiv.org/abs/1708.02002)
 
 [2] Baumgartner and Jaeger et al. "nnDetection: A self-configuring method for medical object detection." MICCAI 2021. https://arxiv.org/pdf/2106.00817.pdf
+
+[3] 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
+
+[4] 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
+
+[5] 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

configs/metadata.json

@@ -1,15 +1,17 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.1.0",
+    "version": "0.1.1",
     "changelog": {
-        "0.1.0": "complete the model package"
+        "0.1.0": "complete the model package",
+        "0.1.1": "add reference for LIDC dataset"
     },
     "monai_version": "0.9.1",
     "pytorch_version": "1.12.0",
     "numpy_version": "1.22.4",
     "optional_packages_version": {
         "nibabel": "4.0.1",
-        "pytorch-ignite": "0.4.9"
+        "pytorch-ignite": "0.4.9",
+        "torchvision": "0.13.0"
     },
     "task": "CT lung nodule detection",
     "description": "A pre-trained model for volumetric (3D) detection of the lung lesion from CT image on LUNA16 dataset",

docs/README.md

@@ -9,17 +9,18 @@ LUNA16 is a public dataset of CT lung nodule detection. Using raw CT scans, the
 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!
 
 ## Data
-The dataset we are experimenting in this example is LUNA16 (https://luna16.grand-challenge.org/Home/).
+The dataset we are experimenting in this example is LUNA16 (https://luna16.grand-challenge.org/Home/), which is based on [LIDC/IDRI database](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI) [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!
+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](https://github.com/MIC-DKFZ/nnDetection/blob/main/projects/Task016_Luna/scripts/prepare.py).
 The resulted json files can be downloaded 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. The code can be found in Section 3.1 of https://github.com/Project-MONAI/tutorials/tree/main/detection
+In this model, we resampled them into 0.703125 x 0.703125 x 1.25 mm. The code of resampling can be found in Section 3.1 of https://github.com/Project-MONAI/tutorials/tree/main/detection
 
 ## Training configuration
 The training was performed with at least 12GB-memory GPUs.
@@ -76,3 +77,9 @@ This is an example, not to be used for diagnostic purposes.
 [1] Lin, Tsung-Yi, et al. "Focal loss for dense object detection." ICCV 2017. https://arxiv.org/abs/1708.02002)
 
 [2] Baumgartner and Jaeger et al. "nnDetection: A self-configuring method for medical object detection." MICCAI 2021. https://arxiv.org/pdf/2106.00817.pdf
+
+[3] 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
+
+[4] 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
+
+[5] 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

docs/license.txt

@@ -4,3 +4,8 @@ Third Party Licenses
 /*********************************************************************/
 i. LUng Nodule Analysis 2016
    https://luna16.grand-challenge.org/Home/
+   https://creativecommons.org/licenses/by/4.0/
+
+ii.  Lung Image Database Consortium image collection (LIDC-IDRI)
+   https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI
+   https://creativecommons.org/licenses/by/3.0/