Performance analysis for computer-aided lung nodule detection on LIDC data

Roland Opfer; Rafael Wiemker

doi:10.1117/12.708210

20 March 2007 Performance analysis for computer-aided lung nodule detection on LIDC data

Roland Opfer, Rafael Wiemker

Author Affiliations +

Proceedings Volume 6515, Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment; 65151C (2007) https://doi.org/10.1117/12.708210
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

For more than one decade computer aided detection (CAD) for pulmonary nodules has been an active research area. There are numerous publications dedicated to this topic. Most authors have created their own database with their own ground truth for validation. This makes it hard to compare the performance of different systems with each other. It is a known fact that the performance of a CAD system can differ significantly depending on which data it is tested and on the underlying ground truth. The lung image data base consortium (LIDC) has recently released 93 publicly available lung images with ground truth lists from 4 different radiologists. This data base will make it possible to compare the performance of different CAD algorithms. In this paper we do the first step to use the LIDC data as a benchmark test. We present a CAD algorithm with a validation study on these data sets. The CAD performance was analyzed by virtue of multiple Free Response Receiver Operator Characteristic (FROC) curves for different lower thresholds of the nodule diameter. There are different ways to merge the ground truth lists of the 4 radiologists and we discuss the performance of our CAD algorithm for several of these possibilities. For nodules with a volume-equivalent diameter ≥4mm which have been simultaneously confirmed by all four radiologists our CAD system shows a detection rate of 89 % at a median false positive rate of 2 findings per patient.

Citation Download Citation

Roland Opfer and Rafael Wiemker "Performance analysis for computer-aided lung nodule detection on LIDC data", Proc. SPIE 6515, Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, 65151C (20 March 2007); https://doi.org/10.1117/12.708210

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