TY - GEN
T1 - Overview of the CLEF 2009 medical image retrieval track
AU - Müller, Henning
AU - Kalpathy-Cramer, Jayashree
AU - Eggel, Ivan
AU - Bedrick, Steven
AU - Radhouani, Saïd
AU - Bakke, Brian
AU - Kahn, Charles E.
AU - Hersh, William
PY - 2010/11/5
Y1 - 2010/11/5
N2 - 2009 was the sixth year for the ImageCLEF medical retrieval task. Participation was strong again with 38 registered research groups. 17 groups submitted runs and thus participated actively in the tasks. The database in 2009 was similar to the one used in 2008, containing scientific articles from two radiology journals, Radiology and Radiographics. The size of the database was increased to a total of 74,902 images. For the first time, 5 case-based topics were provided as an exploratory task. These topics' unit of retrieval was intended to be the source article and not the image itself. Case-based topics are designed to be closer to the clinical workflow, as clinicians often seek information about patient cases using incomplete information consisting of symptoms, findings, and a set of images. Supplying cases to a clinician from the scientific literature that are similar to the case (s)he is treating models what may become an important application of image retrieval in the future. We also introduced a lung nodule detection task in 2009. This task used the CT slices from the Lung Imaging Data Consortium (LIDC) includeding ground truth in the form of manual annotations. The goal of this task was to create algorithms to automatically detect lung nodules. Although there seemed to be significant interest in the task only two groups submitted results with a proprietary software from an industry participant achieving very good results.
AB - 2009 was the sixth year for the ImageCLEF medical retrieval task. Participation was strong again with 38 registered research groups. 17 groups submitted runs and thus participated actively in the tasks. The database in 2009 was similar to the one used in 2008, containing scientific articles from two radiology journals, Radiology and Radiographics. The size of the database was increased to a total of 74,902 images. For the first time, 5 case-based topics were provided as an exploratory task. These topics' unit of retrieval was intended to be the source article and not the image itself. Case-based topics are designed to be closer to the clinical workflow, as clinicians often seek information about patient cases using incomplete information consisting of symptoms, findings, and a set of images. Supplying cases to a clinician from the scientific literature that are similar to the case (s)he is treating models what may become an important application of image retrieval in the future. We also introduced a lung nodule detection task in 2009. This task used the CT slices from the Lung Imaging Data Consortium (LIDC) includeding ground truth in the form of manual annotations. The goal of this task was to create algorithms to automatically detect lung nodules. Although there seemed to be significant interest in the task only two groups submitted results with a proprietary software from an industry participant achieving very good results.
UR - http://www.scopus.com/inward/record.url?scp=78049349476&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78049349476&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15751-6_8
DO - 10.1007/978-3-642-15751-6_8
M3 - Conference contribution
AN - SCOPUS:78049349476
SN - 3642157505
SN - 9783642157509
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 72
EP - 84
BT - Multilingual Information Access Evaluation II
T2 - 10th Workshop of the Cross-Language Evaluation Forum, CLEF 2009
Y2 - 30 September 2009 through 2 October 2009
ER -