Content based image retrieval for lesion analysis

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for ac...

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Bibliographische Detailangaben
Hauptverfasser:	Golden, Daniel Irving, Krishnan, Anitha Priya, Beckers, Fabien Rafael David, Le, Matthieu, Newton, Robert George, Didonato, Matthew Joseph, Law, Shek Bun, Lau, Hok Kan, Sall, Sean Patrick, Taerum, Torin Arni, Lieman-Sifry, Jesse, Leibowitz, Carla Rosa, Calmon, Angélique Sophie, Axerio-Cilies, John
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA IMAGE DATA PROCESSING OR GENERATION, IN GENERAL INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS PHYSICS
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Beschreibung
Zusammenfassung:	Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.