Automatic detection and classification of hypodense hepatic lesions on contrast-enhanced venous-phase CT
| Autor: | Sandy Napel, R. Brooke Jeffrey, Christopher F. Beaulieu, Terry S. Desser, Michel Bilello, Salih Burak Gokturk |
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| Rok vydání: | 2004 |
| Předmět: |
Male
medicine.medical_specialty Contrast Media Information Storage and Retrieval Hepatic Veins Sensitivity and Specificity Pattern Recognition Automated Lesion Hemangioma Text mining Artificial Intelligence medicine Medical imaging Cluster Analysis Humans Computer Simulation Cyst Computed radiography business.industry Liver Diseases Liver Neoplasms Reproducibility of Results Numerical Analysis Computer-Assisted Signal Processing Computer-Assisted General Medicine Image segmentation Middle Aged medicine.disease Radiographic Image Enhancement Injections Intravenous Radiographic Image Interpretation Computer-Assisted Radiology Tomography medicine.symptom Tomography X-Ray Computed business Algorithms |
| Zdroj: | Medical Physics. 31:2584-2593 |
| ISSN: | 0094-2405 |
| DOI: | 10.1118/1.1782674 |
| Popis: | The objective of this work was to develop and validate algorithms for detection and classification of hypodense hepatic lesions, specifically cysts, hemangiomas, and metastases from CT scans in the portal venous phase of enhancement. Fifty-six CT sections from 51 patients were used as representative of common hypodense liver lesions, including 22 simple cysts, 11 hemangiomas, 22 metastases, and 1 image containing both a cyst and a hemangioma. The detection algorithm uses intensity-based histogram methods to find central lesions, followed by liver contour refinement to identify peripheral lesions. The classification algorithm operates on the focal lesions identified during detection, and includes shape-based segmentation, edge pixel weighting, and lesion texture filtering. Support vector machines are then used to perform a pair-wise lesion classification. For the detection algorithm, 80% lesion sensitivity was achieved at approximately 0.3 false positives (FP) per slice for central lesions, and 0.5 FP per slice for peripheral lesions, giving a total of 0.8 FP per section. For 90% sensitivity, the total number of FP rises to about 2.2 per section. The pair-wise classification yielded good discrimination between cysts and metastases (at 95% sensitivity for detection of metastases, only about 5% of cysts are incorrectly classified as metastases), perfect discrimination between hemangiomas and cysts, and was least accurate in discriminating between hemangiomas and metastases (at 90% sensitivity for detection of hemangiomas, about 28% of metastases were incorrectly classified as hemangiomas). Initial implementations of our algorithms are promising for automating liver lesion detection and classification. |
| Databáze: | OpenAIRE |
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