title: Pathology hinting as the combination of automatic segmentation with a statistical shape model.
abstract: With improvements in acquisition speed and quality, the amount of medical image data to be screened by clinicians is starting to become challenging in the daily clinical practice. To quickly visualize and find abnormalities in medical images, we propose a new method combining segmentation algorithms with statistical shape models. A statistical shape model built from a healthy population will have a close fit in healthy regions. The model will however not fit to morphological abnormalities often present in the areas of pathologies. Using the residual fitting error of the statistical shape model, pathologies can be visualized very quickly. This idea is applied to finding drusen in the retinal pigment epithelium (RPE) of optical coherence tomography (OCT) volumes. A segmentation technique able to accurately segment drusen in patients with age-related macular degeneration (AMD) is applied. The segmentation is then analyzed with a statistical shape model to visualize potentially pathological areas. An extensive evaluation is performed to validate the segmentation algorithm, as well as the quality and sensitivity of the hinting system. Most of the drusen with a height of 85.5 microm were detected, and all drusen at least 93.6 microm high were detected.
authors.affiliation: ARTORG Center for Biomedical Engineering Research, Ophthalmic Technologies, University of Bern, 3010 Bern, Switzerland. pascal.dufour@artorg.unibe.ch
authors.forename: Pascal A
authors.initials: PA
authors.lastname: Dufour
authors.forename: Hannan
authors.initials: H
authors.lastname: Abdillahi
authors.forename: Lala
authors.initials: L
authors.lastname: Ceklic
authors.forename: Ute
authors.initials: U
authors.lastname: Wolf-Schnurrbusch
authors.forename: Jens
authors.initials: J
authors.lastname: Kowal
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D015594:Optic Disk Drusen / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D041623:Tomography, Optical Coherence / Q000379:methods*