Pathology hinting as the combination of automatic segmentation with a statistical shape model

Dufour, Pascal A; Abdillahi, Hannan; Ceklic, Lala; Wolf-Schnurrbusch, Ute; Kowal, Jens (2012). Pathology hinting as the combination of automatic segmentation with a statistical shape model. In: Ayache, Nicholas; Delingette, Hervé; Golland, Polina; Mori, Kensaku (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Lecture Notes in Computer Science: Vol. 7512 (pp. 599-606). Berlin: Springer 10.1007/978-3-642-33454-2_74

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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.

Item Type:

Book Section (Book Chapter)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Image Guided Therapy > ARTORG Center - Ophthalmic Technology Lab
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Ophthalmology

UniBE Contributor:

Dufour, Pascal André; Wolf-Schnurrbusch, Ute and Kowal, Horst Jens

ISSN:

0302-9743

ISBN:

978-3-642-33454-2

Series:

Lecture Notes in Computer Science

Publisher:

Springer

Language:

English

Submitter:

Factscience Import

Date Deposited:

04 Oct 2013 14:34

Last Modified:

04 May 2014 02:49

Publisher DOI:

10.1007/978-3-642-33454-2_74

PubMed ID:

23286180

URI:

https://boris.unibe.ch/id/eprint/13697 (FactScience: 220295)

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