- January, 2018: We have decided to stop processing new LUNA16 submissions. Read more ...
- September, 2017: We have decided to stop processing new LUNA16 submissions without a clear description article. Read more ...
- June, 2017: The overview paper has been accepted for publication in Medical Image Analysis: https://doi.org/10.1016/j.media.2017.06.015
- May, 2017: Kaggle has held a competition that may be of interest for participants of LUNA16: https://www.kaggle.com/c/data-science-bowl-2017
LUng Nodule Analysis 2016
Lung cancer is the leading cause of cancer-related death worldwide. Screening high risk individuals for lung cancer with low-dose CT scans is now being implemented in the United States and other countries are expected to follow soon. In CT lung cancer screening, many millions of CT scans will have to be analyzed, which is an enormous burden for radiologists. Therefore there is a lot of interest to develop computer algorithms to optimize screening.
A vital first step in the analysis of lung cancer screening CT scans is the detection of pulmonary nodules, which may or may not represent early stage lung cancer. Many Computer-Aided Detection (CAD) systems have already been proposed for this task. The LUNA16 challenge will focus on a large-scale evaluation of automatic nodule detection algorithms on the LIDC/IDRI data set.
The LIDC/IDRI data set is publicly available, including the annotations of nodules by four radiologists. The LUNA16 challenge is therefore a completely open challenge. We have tracks for complete systems for nodule detection, and for systems that use a list of locations of possible nodules. We provide this list to also allow teams to participate with an algorithm that only determines the likelihood for a given location in a CT scan to contain a pulmonary nodule.
Number of users: 8363
How to Participate
- Teams can register to participate in the challenge. This registration is a mandatory step before downloading data and submitting results to the challenge.
- After registration, teams can download the dataset, including scans, annotations, and (optional) a list of candidates. The content of the dataset is described in this page.
- Results of CAD systems on those scans, consisting of a list of locations in the scans and a degree of suspicion that this location is a nodule, can be submitted. The format of the submission file is described in this page.
- The submitted results will be processed and will be published on the results page.
- Colin Jacobs (Radboud University Medical Center, Nijmegen, The Netherlands)
- Arnaud Arindra Adiyoso Setio (Radboud University Medical Center, Nijmegen, The Netherlands)
- Alberto Traverso (Polytechnic University of Turin and Turin Section of INFN, Turin, Italy)
- Bram van Ginneken (Radboud University Medical Center, Nijmegen, The Netherlands)