3D Medical Image Processing Algorithm Competition in Japan

This paper reports on a medical image processing algorithm competition that has been held annually in Japan from 2002 to 2010. It is the world’s oldest competition in the field of 3D medical image processing, and covers various targets: the liver, pancreas, hepatocellular carcinomas, and hepatic vascular and metastatic liver tumors. This paper presents the algorithm entries and results of the competition. The paper also discusses the benefits of the competition, among which the main benefit is that such a competition can help to rank existing algorithms by using an unknown image database. In addition, there are several secondary benefits. For example, an image database that is distributed by organizers helps researchers who suffer from a shortage of images to conduct training and carry out validation. Another example is the progress achieved in performance evaluation methodologies used for comparing algorithms. The Japanese competition employs a different evaluation scheme than other international competitions; the necessity and advantages of using this different scheme is explained in this paper. Another important benefit of the competition is the aggregation of the algorithms registered in the competition. Therefore, in this paper, we also present the results of the algorithm aggregation and its superiority over a winner’s algorithm.

[1]  Akinobu Shimizu,et al.  Medical Image Processing Competition in Japan , 2009 .

[2]  Osamu Watanabe,et al.  MadaBoost: A Modification of AdaBoost , 2000, COLT.

[3]  Raymond Lister,et al.  Grand challenges , 2005, SGCS.

[4]  Jun-ichiro Toriwaki,et al.  New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications , 1994, Pattern Recognit..

[5]  Akinobu Shimizu,et al.  Automated Segmentation of 3D CT Images Based on Statistical Atlas and Graph Cuts , 2010, MCV.

[6]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[7]  Martin Styner,et al.  Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.

[8]  B. Ginneken,et al.  3D Segmentation in the Clinic: A Grand Challenge , 2007 .

[9]  Qin Li,et al.  Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs , 2010, IEEE Transactions on Medical Imaging.

[10]  Akinobu Shimizu,et al.  Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography , 2009, International Journal of Computer Assisted Radiology and Surgery.

[11]  Akinobu Shimizu,et al.  Preliminary report of competition for liver region extraction algorithms from three-dimensional CT images , 2004, CARS.

[12]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.