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

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cris.virtualsource.author-orcid0b33e7c5-4427-46ea-bfe6-1763eb61b1e6
dc.contributor.authorDufour, Pascal André
dc.contributor.authorAbdillahi, Hannan
dc.contributor.authorCeklic, Lala
dc.contributor.authorWolf-Schnurrbusch, Ute
dc.contributor.authorKowal, Horst Jens
dc.contributor.editorAyache, Nicholas
dc.contributor.editorDelingette, Hervé
dc.contributor.editorGolland, Polina
dc.contributor.editorMori, Kensaku
dc.date.accessioned2024-10-11T13:27:01Z
dc.date.available2024-10-11T13:27:01Z
dc.date.issued2012
dc.description.abstractWith 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.
dc.description.numberOfPages8
dc.description.sponsorshipUniversitätsklinik für Augenheilkunde
dc.description.sponsorshipARTORG Center - Ophthalmic Technology Lab
dc.identifier.doi10.48350/13697
dc.identifier.pmid23286180
dc.identifier.publisherDOI10.1007/978-3-642-33454-2_74
dc.identifier.urihttps://boris-portal.unibe.ch/handle/20.500.12422/83775
dc.language.isoen
dc.publisherSpringer
dc.publisher.placeBerlin
dc.relation.isbn978-3-642-33454-2
dc.relation.ispartofbookMedical Image Computing and Computer-Assisted Intervention – MICCAI 2012
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.issn0302-9743
dc.relation.organization62822F8A0D47476EBC8D9ECC5A1D9508
dc.relation.organizationDCD5A442BB12E17DE0405C82790C4DE2
dc.titlePathology hinting as the combination of automatic segmentation with a statistical shape model
dc.typebook_section
dspace.entity.typePublication
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oaire.citation.endPage606
oaire.citation.issuePt 3
oaire.citation.startPage599
oaire.citation.volume7512
oairecerif.author.affiliationARTORG Center - Ophthalmic Technology Lab
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oairecerif.author.affiliationUniversitätsklinik für Augenheilkunde
oairecerif.author.affiliationARTORG Center - Ophthalmic Technology Lab
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unibe.date.licenseChanged2023-05-23 09:38:09
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unibe.eprints.legacyId13697
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