NODE21

This dataset is provided for NODE21 public challenge. Node21 dataset consists of frontal chest radiographs with annotated bounding boxes around nodules. It consists of 4882 frontal chest radiographs, where 1134 CXR images (1476 nodules) are annotated with bounding boxes around nodules and the remaining 3748 images are free of nodules hence representing the negative class. The images in this set are from public datasets that allow us to remix and redistribute. They come from the following sources:

• JSRT [1]
• PadChest [2]
• Chestx-ray14 [3]
• Open-I [4]

The annotations were provided by our chest radiologists. We provide both original and preprocessed versions of the dataset.

Further, for the generation track, we provide a public set of NODE21 CT patches. These are patches of nodules from CT scans, originate from the LUNA16 dataset [5][6] .

For more detailed descriptions of the data, please refer to the challenge website: NODE21 

[1] Shiraishi, J., Katsuragawa, S., Ikezoe, J., Matsumoto, T., Kobayashi, T., Komatsu, K.i., Matsui, M., Fujita, H., Kodera, Y., Doi, K., 2000. Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. American Journal of Roentgenology 174, 71–74. doi:10.2214/ajr.174.1.1740071.

[2] Bustos, A., Pertusa, A., Salinas, J.M., de la Iglesia-Vaya, M., 2020. PadChest: ´ A large chest x-ray image dataset with multi-label annotated reports. Medical Image Analysis 66, 101797. doi:10.1016/j.media.2020.101797.

[3] Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M., 2017b. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106. doi:10.1109/cvpr.2017.369.

[4] Demner-Fushman, D., Antani, S., Simpson, M., Thoma, G.R., 2012. Design and Development of a Multimodal Biomedical Information Retrieval System. Journal of Computing Science and Engineering 6, 168–177. doi:10.5626/JCSE.2012.6.2.168.

[5] Andrey Fedorov, Matthew Hancock, David Clunie, Mathias Brochhausen, Jonathan Bona, Justin Kirby, John Freymann, Steve Pieper, Hugo Aerts, Ron Kikinis1, Fred Prior, 2019. Standardized representation of the LIDC annotations using DICOM. The Cancer Imaging Archive. doi: 10.7937/TCIA.2018.H7UMFURQ

[6] Setio et al., Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images:: The LUNA16 challenge, Medical Image Analysis 42, doi:: 10.1016/j.media.2017.06.015