Establishing spatial correspondence for the analysis of images from highly deforming anatomy.
This invited presentation summarizes recent advances in the incorporation of knowledge of the geometry, tissue mechanical properties and imaging characteristics in establishing spatial correspondence between multiple images of highly deforming, soft tissue structures. Spatial correspondence is used to aid diagnosis and in the extraction of quantitative parameters for disease detection, monitoring disease progression and assessing therapeutic response. The work is illustrated through clinical examples of multi-modal imaging of the breast, assessment of small bowel motility and polyp detection in the large bowel.
Centre for Medical Image Computing (CMIC), UCL, Gower Street, London, WC1E 6BT, UK.
David J
DJ
Hawkes
Thomy
T
Mertzanidou
John
J
Hipwell
David
D
Atkinson
Holger
H
Roth
Tom
T
Hampshire
Jamie
J
McClelland
D001940:Breast / Q000473:pathology*; D003113:Colonoscopy; D003424:Crohn Disease / Q000000981:diagnostic imaging / Q000473:pathology / Q000503:physiopathology; D005260:Female; D005769:Gastrointestinal Motility; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007417:Intestinal Polyps / Q000000981:diagnostic imaging / Q000503:physiopathology; D007421:Intestine, Small / Q000473:pathology* / Q000503:physiopathology; D008279:Magnetic Resonance Imaging; D008327:Mammography; D009928:Organ Specificity; D016684:Prone Position; D016683:Supine Position; D014057:Tomography, X-Ray Computed