Image content extraction: application to MR images of the brain.
A system for automatically extracting image content features was developed that combines registration to a labeled atlas with natural language processing of free-text radiology reports. The system was then tested with T1-weighted, spoiled gradient-echo magnetic resonance (MR) imaging studies of the brain performed in nine patients. The locations of 599 structures were visually assessed by an experienced radiologist and compared with the locations indicated by automated output. The in-plane accuracy of the contours was subjectively evaluated as either good, moderate, or poor. The criterion for classifying a structure as correctly located was that 90% or more of all the images containing the structure had to be correctly identified. For 98% of the structures, the images identified by the automated algorithm agreed with those identified by the radiologist, and in 83% of cases, image contours showed a good in-plane overlap. The results of this validation study demonstrate that this combination of registration and natural language processing is accurate in identifying relevant images from brain MR imaging studies. However, the range of applicability of this technique has yet to be determined by applying the technique to a large number of studies.
Sinha
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Department of Radiological Sciences, UCLA School of Medicine, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024, USA. usinha@itmedicine.net
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Yaghmai
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Taira
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Kangarloo
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D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001927:Brain Diseases / Q000175:diagnosis*; D001931:Brain Mapping; D001932:Brain Neoplasms / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D009323:Natural Language Processing; D012680:Sensitivity and Specificity; D012984:Software