The Lung TIME: annotated lung nodule dataset and nodule detection framework

Martin Dolejsi; Jan Kybic; Michal Polovincak; Stanislav Tuma

doi:10.1117/12.811645

3 March 2009 The Lung TIME: annotated lung nodule dataset and nodule detection framework

Martin Dolejsi, Jan Kybic, Michal Polovincak, Stanislav Tuma

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72601U (2009) https://doi.org/10.1117/12.811645
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

The Lung Test Images from Motol Environment (Lung TIME) is a new publicly available dataset of thoracic CT scans with manually annotated pulmonary nodules. It is larger than other publicly available datasets. Pulmonary nodules are lesions in the lungs, which may indicate lung cancer. Their early detection significantly improves survival rate of patients. Automatic nodule detecting systems using CT scans are being developed to reduce physicians' load and to improve detection quality. Besides presenting our own nodule detection system, in this article, we mainly address the problem of testing and comparison of automatic nodule detection methods. Our publicly available 157 CT scan dataset with 394 annotated nodules contains almost every nodule types (pleura attached, vessel attached, solitary, regular, irregular) with 2-10mm in diameter, except ground glass opacities (GGO). Annotation was done consensually by two experienced radiologists. The data are in DICOM format, annotations are provided in XML format compatible with the Lung Imaging Database Consortium (LIDC). Our computer aided diagnosis system (CAD) is based on mathematical morphology and filtration with a subsequent classification step. We use Asymmetric AdaBoost classifier. The system was tested using TIME, LIDC and ANODE09 databases. The performance was evaluated by cross-validation for Lung TIME and LIDC, and using the supplied evaluation procedure for ANODE09. The sensitivity at chosen working point was 94.27% with 7.57 false positives/slice for TIME and LIDC datasets combined, 94.03% with 5.46 FPs/slice for the Lung TIME, 89.62% sensitivity with 12.03 FPs/slice for LIDC, and 78.68% with 4,61 FPs/slice when applied on ANODE09.

Citation Download Citation

Martin Dolejsi, Jan Kybic, Michal Polovincak, and Stanislav Tuma "The Lung TIME: annotated lung nodule dataset and nodule detection framework", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72601U (3 March 2009); https://doi.org/10.1117/12.811645

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