Adiyoso Setio, A. A., Traverso, A., de Bel, T., Berens , M., van den Bogaard, C., Cerello, P., Chen , H., Dou , Q., Fantacci , M. E., Geurts , B., van der Gugten , R., Heng, P. A., Jansen, B., de Kaste, M. M. J., Kotov , V., Lin , J. Y.-H., Manders , J. T. M. C., Sóñora Mengana, A., Garcia Naranjo, J. C., ... Jacobs , C. (2017). Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Medical Image Analysis, 42, 1-13. https://doi.org/10.1016/j.media.2017.06.015

@article{252aac2414c64e7c9715f06e9cec0c6c,
title = "Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge",
abstract = "Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems.",
keywords = "Computed tomography, Computer-aided detection, Convolutional networks, Deep learning, Medical image challenges, Pulmonary nodules",
author = "{Adiyoso Setio}, {Arnaud Arinda} and Alberto Traverso and {de Bel}, Thomas and Moira Berens and {van den Bogaard}, Cas and Piergiorgio Cerello and Hao Chen and Qi Dou and Fantacci, {Maria Evelina} and Bram Geurts and {van der Gugten}, Robbert and Heng, {Pheng Ann} and Bart Jansen and {de Kaste}, {Michael M.J.} and Valentin Kotov and Lin, {Jack Yu-Hung} and Manders, {Jeroen T.M.C.} and {S{\'o}{\~n}ora Mengana}, Alexander and {Garcia Naranjo}, {Juan Carlos} and Evgenia Papavasileiou and Mathias Prokop and Marco Saletta and Schaefer-Prokop, {Cornelia M} and Scholten, {Ernst T.} and Luuk Scholten and Snoeren, {Miranda M.} and {Lopez Torres}, Ernesto and Jef Vandemeulebroucke and Nicole Walasek and Zuidhof, {Guido C.A.} and {van Ginneken}, Bram and Colin Jacobs",
year = "2017",
month = dec,
doi = "10.1016/j.media.2017.06.015",
language = "English",
volume = "42",
pages = "1--13",
journal = "Medical Image Analysis",
issn = "1361-8415",
publisher = "Elsevier",
}