Medical Image Analysis
Arnaud Arindra Adiyoso Setio, Alberto Traverso, Thomas de Bel, Moira S.N. Berens, Cas van den Bogaard, Piergiorgio Cerello, Hao Chen, QiDou, Maria Evelina Fantacci, Bram Geurts, Robbert van der Gugten, Pheng Ann Heng, Bart Jansen, Michael M.J. de Kaste, Valentin Kotov, Jack Yu-Hung Lin, Jeroen T.M.C. Manders, Alexander Sóñora-Mengana, Juan Carlos García-Naranjo, Evgenia Papavasileiou, Mathias Prokop, Marco Saletta, Cornelia M Schaefer-Prokop, Ernst T. Scholten, Luuk Scholten, Miranda M. Snoeren, Ernesto Lopez Torres, Jef Vandemeulebroucke, Nicole Walasek, Guido C.A. Zuidhof, Bram van Ginneken & Colin Jacobs
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.
Disclaimers:
Chest Suite comprises multiple applications including Veye Lung Nodules, Veye Reporting, DeepHealth Chest CT AI, DeepHealth Viewer, DeepHealth Reporting, DeepHealth Worklist and HealthCCSng. Veye Lung Nodules and Veye Reporting are manufactured by Aidence B.V. and distributed by DeepHealth Inc. DeepHealth Viewer is manufactured by eRAD, Inc. and distributed by DeepHealth, Inc, HealthCCSng is manufactured by Nanox AI Ltd. and distributed by DeepHealth, Inc, DeepHealth Chest CT AI is 510(k) pending.
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