Lung nodule CT false positive reduction

This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set concerns the classification of lung nodules on screening thoracic computed tomography (CT) scans and was derived from the 2016 Lung Nodule Analysis (LUNA) challenge. The data set was previously introduced and described by Setio et al. (2017) and no images or patient information were added. Only the "false positive reduction" track was considered, where a provided set of nodule candidates should be classified. Data was restructured in compliance with the AUC23 challenge format. The data set was collected from the largest publicly available reference database for lung nodules: The Lung Image Database Consortium image collection.

Images are 3D tensors:

• 0: 3D axial screening CT scan (cropped to candidate nodule's region of interest)

Classification labels:

• 0: Is a nodule
• 1: Is not a nodule

Folder structure:

imagesTr (root folder with all patients and studies)
    ├── LUNA16_0000_0000.mha  (axial CT imaging for nodule 0000)
    ├── LUNA16_0000_0002.mha  (axial CT imaging for nodule 0002)
    ├── ...
 

Please cite the following article if you are using the 2016 Lung Nodule Analysis (LUNA) challenge false positive reduction track data set:

Setio AAA, Traverso A, de Bel T, Berens MSN, Bogaard CVD, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B, Gugten RV, Heng PA, Jansen B, de Kaste MMJ, Kotov V, Lin JY, Manders JTMC, Sóñora-Mengana A, García-Naranjo JC, Papavasileiou E, Prokop M, Saletta M, Schaefer-Prokop CM, Scholten ET, Scholten L, Snoeren MM, Torres EL, Vandemeulebroucke J, Walasek N, Zuidhof GCA, Ginneken BV, Jacobs C. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med Image Anal. 2017 Dec;42:1-13. doi: 10.1016/j.media.2017.06.015. Epub 2017 Jul 13. PMID: 28732268.