Mr. Martin Dolejsi Profile

Martin Dolejsi

at Czech Technical Univ in Prague

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Proceedings Article | 4 March 2009 Paper

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

Martin Dolejsi, Jan Kybic, Michal Polovincak, Stanislav Tuma

Proceedings Volume 7260, 72601U (2009) https://doi.org/10.1117/12.811645

KEYWORDS: Lung, Computed tomography, Databases, Lung cancer, Computer aided diagnosis and therapy, Sensors, Classification systems, Detection and tracking algorithms, Algorithm development, Lung imaging

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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.

Proceedings Article | 30 March 2007 Paper

Automatic two-step detection of pulmonary nodules

Martin Dolejší, Jan Kybic

Proceedings Volume 6514, 65143J (2007) https://doi.org/10.1117/12.709161

KEYWORDS: Lung, Sensors, Computed tomography, Image segmentation, Image filtering, Computer aided diagnosis and therapy, Solid modeling, Ferroelectric LCDs, Lung cancer, Electronic filtering

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We present a computer-aided diagnosis (CAD) system to detect small-size (from 2mm to around 10mm) pulmonary nodules from helical CT scans. A pulmonary nodule is a small, round (parenchymal nodule) or worm (juxta-pleural) shaped lesion in the lungs. Both have greater radio density than lungs parenchyma. Lung nodules may indicate a lung cancer and its detection in early stage improves survival rate of patients. CT is considered to be the most accurate imaging modality for detection of nodules. However, the large amount of data per examination makes the interpretation difficult. This leads to omission of nodules by human radiologist. CAD system presented is designed to help lower the number of omissions. Our system uses two different schemes to locate juxtapleural nodules and parenchymal nodules. For juxtapleural nodules, morphological closing and thresholding is used to find nodule candidates. To locate non-pleural nodule candidates, 3D blob detector uses multiscale filtration. Ellipsoid model is fitted on nodules. To define which of the nodule candidates are in fact nodules, an additional classification step is applied. Linear and multi-threshold classifiers are used. System was tested on 18 cases (4853 slices) with total sensitivity of 96%, with about 12 false positives/slice. The classification step reduces number of false positives to 9 per slice without significantly decreasing sensitivity (89,6%).

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