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Lung nodule detection in low-dose and thin-slice computed tomography
Lung nodule detection in low-dose and thin-slice computed tomography
- Publication Year :
- 2008
-
Abstract
- A computer-aided detection (CAD) system for the identification of small pulmonary nodules in low-dose and thin-slice CT scans has been developed. The automated procedure for selecting the nodule candidates is mainly based on a filter enhancing spherical-shaped objects. A neural approach based on the classification of each single voxel of a nodule candidate has been purposely developed and implemented to reduce the amount of false-positive findings per scan. The CAD system has been trained to be sensitive to small internal and sub-pleural pulmonary nodules collected in a database of low-dose and thin-slice CT scans. The system performance has been evaluated on a data set of 39 CT containing 75 internal and 27 sub-pleural nodules. The FROC curve obtained on this data set shows high values of sensitivity to lung nodules (80-85% range) at an acceptable level of false positive findings per patient (10-13 FP/scan).
- Subjects :
- medicine.medical_specialty
Nodule detection
Lung Neoplasms
Health Informatics
CAD
Image processing
Thin-slice CT
Radiation Dosage
Sensitivity and Specificity
Pattern Recognition, Automated
Low-dose computed tomography (LDCT)
Imaging, Three-Dimensional
Artificial Intelligence
Computer-aided detection (CAD)
Image Processing, Computer-Assisted
Medicine
Humans
Mass Screening
Diagnosis, Computer-Assisted
Lung
Mass screening
business.industry
Solitary Pulmonary Nodule
Nodule (medicine)
Computer Science Applications
Data set
medicine.anatomical_structure
Italy
ROC Curve
Tomography
Radiology
Neural Networks, Computer
medicine.symptom
business
Nuclear medicine
Tomography, Spiral Computed
Algorithms
Software
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....993149c84b1f952670baf35ce369309b