Revision of ImageCLEF 2009 medical annotation task from Fri, 01/23/2009 - 23:49

In 2009, ImageCLEF will offer two medically oriented tasks that require visual techniques.

This year will be the inaugural year for the lung nodule detection task

Introduction

The goal of this task is to compare the performance of lung nodule detection techniques with a gold standard of manually identified nodules. Runs submitted can be: fully automatic, methods with minimal user interaction and interactive segmentation methods. However, participants requested to provide information about the nature of their runs.

Data
The data for this task will be a subset of the LIDC (http://imaging.cancer.gov/programsandresources/InformationSystems/LIDC) database, a set of images that have been manually annotated for nodules by multiple radiologists. We will provide training, development and test data consisting of CT scans and associated annotation indicating the presence of nodules.

ImageCLEF will offer again this year for the last time a Medical Image Annotation Task. It will be a survey on the last four years experience and we warmly invite all the participants groups of the last editions to take part to the challenge. We are going to contact an editor to prepare and publish a book describing the most interesting and successful approaches developed during the last years in this task.

Automatic image annotation or image classification can be an important step when searching for images from a database. Automatic techniques able to identify acquisition modality, body orientation, body region, and biological system examined based on the images could be used for multilingual image
annotations as well as for DICOM header corrections in medical image acquisition routine.

The training image set will be based on the IRMA project as usual, and will contain about 7000 common images of the 2005-2008 training databases. This set will be accompanied by three different classification label sets considering:

- 57 classes as in 2005
- 116 classes as in 2006 and 2007
- 197 classes as in 2008

About 1500 radiographs for which classification labels are not available to the participants have to be classified according to the three different schemes. We will ask each group to submit runs based only one algorithm which should be optimized to face the three different classification problems. The aim is to understand how each algorithm answers to the increasing number of classes and to the unbalancing. It would also be possible to evaluate which is the best way to exploit the hierarchy.

More details on the tasks to be provided soon.

Tentative Schedule

15.1.2009: registration opens for all CLEF task
15.3.2009: training data and task release
15.5.2009: test data release
01.6.2009: submission of runs
15.7.2009: release of results
15.8.2009: submission of working notes papers
30.9-2.10.2009: CLEF workshop in Corfu, Greece.

Organizers
Barbara Caputo, Idiap Research Institute, Martigny, Switzerland, bcaputo@idiap.ch
Tatiana Tommasi, Idiap Research Institute, Martigny, Switzerland, ttommasi@idiap.ch
Henning Mueller, University and University Hospitals of Geneva, Switzerland, henning.mueller@sim.hcuge.ch
Thomas M. Deserno, RWTH Aachen University, Medical Informatics, IRMA group, deserno@ieee.org
Jayashree Kalpathy-Cramer, Oregon Health & Science University, Portland, OR, USA, kalpathy@ohsu.edu