Gerhard Kohl

Automatic detection of lung nodules from multislice low-dose CT images

We describe in this paper a novel, efficient method to automatically detect lung nodules from low-dose, high- resolution CT (HRCT) images taken with a multi-slice scanner. First, the program identifies initial anatomical seeds, including lung nodule candidates, airways, vessels, and other features that appear as bright opacities in CT images. Next, a 3D region growing method is applied to each seed. The thresholds for segmentation are adaptively adjusted based upon automatic analysis of the local histogram. Once an object has been examined, vessels and other non-nodule objects are quickly excluded from future study, thus saving computation time. Finally, extracted 3D objects are classified a nodule candidates or non-nodule structures. Anatomical knowledge and multiple measurements, such as volume and sphericity, are used to categorize each object. The detected nodules are presented to the user for examination and verification. The proposed method was applied to 14 low dose HRCT patient studies. Since the CT images were taken with a multi-slice scanner, the average number of slices per study was 292. In every case the x-ray exposure was about 20 mAs, a suitable dosage for screening. In our preliminary results, the method detected an average of 8 nodules per study, with an average size of 3.3 mm in diameter.

Knowledge-based automatic detection of multitype lung nodules from multidetector CT studies

Multi-slice computed tomography (CT) provides a promising technology for lung cancer detection and treatment. To optimize automatic detections of a more complete spectrum of lung nodules on CT requires multiple specialized algorithms in a coherently integrated detection system. We have developed a knowledge-based system for automatic lung nodule detection and analysis, which coherently integrates several robust novel detection algorithms to detect different types of nodules, including those attached to the chest wall, nodules adjacent to or fed by vessels, and solitary nodules, simultaneously. The system architecture can be easily extended in the future to include a still greater range of nodule types, most importantly so-called ground-glass opacities (GGOs). In addition, automatic local adaptive histogram analysis, dynamic cross-correlation analysis, and the automatic volume projection analysis by using by data dimension reduction method, are used in nodule detection. The proposed system has been applied to 10 patients screened with low-dose multi-slice CT. Preliminary clinical tests show that (1) the false positive rate averages about 3.2 per study; and (2) by using the system radiologists are able to detect nearly twice the number of nodules as compared with working alone.

An interactive system for CT lung nodule identification and examination

Abstract Computed Tomography (CT) imaging provides a very promising technology for lung cancer detection and treatment. The newest multi-slice CT machines provide high-resolution scans quickly enough that the entire lung volume may be covered in a single breath-hold, and at a low enough radiation dose to make it practical for screening. However, the large amounts of data obtained by these machines present formidable challenges to physicians. A typical scan with slices taken 1 mm apart may have 300 or more image slices. If CT lung cancer screening becomes widespread, there will be tremendous demand for such examinations. Clearly, some sort of computer assistance will be highly beneficial to physicians in coping with such large amounts of data. We present a real time system for interactive assistance in diagnosing, monitoring, and measuring lung nodules in CT images. This interactive system allows physicians to incorporate their knowledge with the computers ability to perform rapid computations, in order to improve decision-making about diagnosis and treatment. This system may allow for a more efficient workflow, and enhance the acceptance of Computer-Aided Diagnosis.

Enhancement measurement of pulmonary nodules with multirow detector CT: precision assessment of a 3D algorithm compared to the standard procedure

Precise density measurement of pulmonary nodules with CT is an important prerequisite if the measurement of contrastenhancement is to be used to assess if a nodule is benign or malignant. The precision of a volume-based 3D measurement method was compared to the standard 2D method currently used in clinical practice. Two consecutive low-dose CT scans (inter-scan delay a few minutes) were obtained from 10 patients with 75 pulmonary nodules (size 5 - 32 mm). A four-slice CT was used (Siemens Somatom VZ, collimation 4 x 1 mm, normalized pitch 1.75, slice thickness 1.25 mm, reconstruction interval 0.8 mm). Mean density of each nodule was determined independently from both scans with two methods: 1) an automatic 3D segmentation method; 2) the standard 2D method as proposed in the literature and currently used in clinical practice, (3 mm slice thickness, oval region of interest). ROC analysis was used to compare these methods for the detection of an enhancement of 10, 30 and 50 Hounsfield units (HU). The mean absolute measurement error (± standard deviation) was 9.9 HU (±14.4 HU) for the 3D method and 26.4 HU (±42.0 HU) for the 2D method. ROC analysis yielded AZ values of 0.723 / 0.932 / 0.982 for the 3D method and 0.609 /0.773 / 0.850 for the 2D method for the detection of 10 / 30 / 50 HU enhancement respectively. Volume-based density determination has a significantly higher reproducibility than the currently used 2D ROI approach and should preferentially be used for enhancement measurements in pulmonary nodules.