Computer-aided detection of polyps in CT colonography using logistic regression
| Autor: | V.F. van Ravesteijn, C. van Wijk, Jaap Stoker, Roel Truyen, Joost Frederik Peters, L.J. van Vliet, Frans M. Vos |
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| Přispěvatelé: | Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam Neuroscience, Radiology and Nuclear Medicine, Cancer Center Amsterdam |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Virtual colonoscopy
Computer science Feature vector Colonic Polyps Computed tomography Logistic regression Sensitivity and Specificity Pattern Recognition Automated Computed Tomography Colonography Image Processing Computer-Assisted medicine False positive paradox Humans polyp detection Electrical and Electronic Engineering computer aided diagnosis Radiological and Ultrasound Technology Contextual image classification medicine.diagnostic_test business.industry logistic regression pattern recognition Colon wall Pattern recognition digestive system diseases Computer Science Applications Logistic Models Computer-aided diagnosis computed tomography (CT) colonography Artificial intelligence business Colonography Computed Tomographic Software |
| Zdroj: | IEEE transactions on medical imaging, 29(1), 120-131. Institute of Electrical and Electronics Engineers Inc. IEEE Transactions on Medical Imaging, 29 (1), 2010 |
| ISSN: | 0278-0062 |
| Popis: | We present a computer-aided detection (CAD) system for computed tomography colonography that orders the polyps according to clinical relevance. The CAD system consists of two steps: candidate detection and supervised classification. The characteristics of the detection step lead to specific choices for the classification system. The candidates are ordered by a linear logistic classifier (logistic regression) based on only three features: the protrusion of the colon wall, the mean internal intensity, and a feature to discard detections on the rectal enema tube. This classifier can cope with a small number of polyps available for training, a large imbalance between polyps and non-polyp candidates, a truncated feature space, unbalanced and unknown misclassification costs, and an exponential distribution with respect to candidate size in feature space. Our CAD system was evaluated with data sets from four different medical centers. For polyps larger than or equal to 6 mm we achieved sensitivities of respectively 95%, 85%, 85%, and 100% with 5, 4, 5, and 6 false positives per scan over 86, 48, 141, and 32 patients. A cross-center evaluation in which the system is trained and tested with data from different sources showed that the trained CAD system generalizes to data from different medical centers and with different patient preparations. This is essential to application in large-scale screening for colorectal polyps. |
| Databáze: | OpenAIRE |
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