{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/validation-comparison-and-combination-of","title":"Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge","arxiv_id":"1612.08012","date":"2016-12-23","proceeding":null,"authors":["Arnaud Arindra Adiyoso Setio","Alberto Traverso","Thomas de Bel","Moira S. N. Berens","Cas van den Bogaard","Piergiorgio Cerello","Hao Chen","Qi Dou","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 Sónora-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)\nscans has been an active area of research for the last two decades. However,\nthere have only been few studies that provide a comparative performance\nevaluation of different systems on a common database. We have therefore set up\nthe LUNA16 challenge, an objective evaluation framework for automatic nodule\ndetection algorithms using the largest publicly available reference database of\nchest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their\nalgorithm and upload their predictions on 888 CT scans in one of the two\ntracks: 1) the complete nodule detection track where a complete CAD system\nshould be developed, or 2) the false positive reduction track where a provided\nset of nodule candidates should be classified. This paper describes the setup\nof LUNA16 and presents the results of the challenge so far. Moreover, the\nimpact of combining individual systems on the detection performance was also\ninvestigated. It was observed that the leading solutions employed convolutional\nnetworks and used the provided set of nodule candidates. The combination of\nthese solutions achieved an excellent sensitivity of over 95% at fewer than 1.0\nfalse positives per scan. This highlights the potential of combining algorithms\nto improve the detection performance. Our observer study with four expert\nreaders has shown that the best system detects nodules that were missed by\nexpert readers who originally annotated the LIDC-IDRI data. We released this\nset of additional nodules for further development of CAD systems.","url_abs":"http://arxiv.org/abs/1612.08012v4","url_pdf":"http://arxiv.org/pdf/1612.08012v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"}],"methods":[],"datasets_introduced":[{"slug":"luna","name":"LUNA","full_name":""},{"slug":"luna16","name":"LUNA16","full_name":"LUNA16"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.08012","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}