The objective of this research is to evaluate and compare the performance of our automated detection algorithm on isolated and attached nodules in whole lung CT scans. Isolated nodules are surrounded by the lung parenchyma with no attachment to large solid structures such as the chest wall or mediastinum surface, while attached nodules are adjacent to these structures. The detection algorithm involves three major stages. First, the region of the image space where pulmonary nodules are to be found is identified. This involves segmenting the lung region and generating the pleural surface. In the second stage, which is the hypothesis generation stage, nodule candidate locations are identified and their sizes are estimated. The nodule candidates are successively refined in the third stage a sequence of filters of increasing complexity. The algorithm was tested on a dataset containing 250 low-dose whole lung CT scans with 2.5mm slice thickness. A scan is composed of images covering the whole lung region for a single person. The dataset was partitioned into 200 and 50 scans for training and testing the algorithm. Only solid nodules were considered in this study. Experienced chest radiologists identified a total of 447 solid nodules. 345 and 102 of the nodules were from the training and testing datasets respectively. 126(28.2%) of the nodules in the dataset were attached nodules. The detection performance was then evaluated separately for isolated and attached nodule types considering different size ranges. For nodules 3mm and larger, the algorithm achieved a sensitivity of 97.8% with 2.0 false positives (FPs) per scan and 95.7% with 19.3 FPs per scan for isolated and attached nodules respectively. For nodules 4mm and larger, a sensitivity of 96.6% with 1.5 FP per scan and a 100% sensitivity with 13 FPs per scan were obtained for isolated and attached nodule types respectively. The results show that our algorithm detects isolated and attached nodules with comparable sensitivity but differing number of false positives per scan. The high number of false positives for attached nodule detection was mainly due to the complexity of the mediastinum lung surface.
[1]
H. K. Huang,et al.
Knowledge-Based Lung Nodule Detection from Helical CT
,
1999,
Bildverarbeitung für die Medizin.
[2]
S. Armato,et al.
Computerized detection of pulmonary nodules on CT scans.
,
1999,
Radiographics : a review publication of the Radiological Society of North America, Inc.
[3]
Noboru Niki,et al.
Computer assisted diagnosis of lung cancer using helical X-ray CT
,
1994,
Proceedings of IEEE Workshop on Biomedical Image Analysis.
[4]
Kang-Ping Lin,et al.
Object-based deformation technique for 3D CT lung nodule detection
,
1999,
Medical Imaging.
[5]
Matthew T. Freedman,et al.
Artificial convolution neural network techniques and applications for lung nodule detection
,
1995,
IEEE Trans. Medical Imaging.
[6]
Li Fan,et al.
Automatic detection of lung nodules from multislice low-dose CT images
,
2001,
SPIE Medical Imaging.
[7]
Berkman Sahiner,et al.
Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system.
,
2002,
Medical physics.
[8]
Hiroshi Fujita,et al.
Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique
,
2001,
IEEE Transactions on Medical Imaging.
[9]
O S Miettinen,et al.
Early Lung Cancer Action Project
,
2001,
Cancer.
[10]
Manuel G. Penedo,et al.
Computer-aided diagnosis: a neural-network-based approach to lung nodule detection
,
1998,
IEEE Transactions on Medical Imaging.
[11]
C. Henschke.
Early lung cancer action project
,
2000,
Cancer.
[12]
Noboru Niki,et al.
Lung cancer detection based on helical CT images using curved-surface morphology analysis
,
1999,
Medical Imaging.