Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework

详细信息    查看全文

• 作者：Sieun Lee^a ; Nicolas Charon^b ; Benjamin Charlier^c ; Karteek Popuri^a ; Evgeniy Lebed^a ; Marinko V. Sarunic^a ; Alain Trouvé ; ^d ; Mirza Faisal Beg ; ^a ; ^{mfbeg@sfu.ca" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Atlas-based shape analysis ; Atlas generation ; Computational anatomy ; Classification ; Optical coherence tomography
• 刊名：Medical Image Analysis
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：35
• 期：Complete
• 页码：570-581
• 全文大小：3846 K

文摘

We propose a novel approach for quantitative shape variability analysis in retinal optical coherence tomography images using the functional shape (fshape) framework. The fshape framework uses surface geometry together with functional measures, such as retinal layer thickness defined on the layer surface, for registration across anatomical shapes. This is used to generate a population mean template of the geometry-function measures from each individual. Shape variability across multiple retinas can be measured by the geometrical deformation and functional residual between the template and each of the observations. To demonstrate the clinical relevance and application of the framework, we generated atlases of the inner layer surface and layer thickness of the Retinal Nerve Fiber Layer (RNFL) of glaucomatous and normal subjects, visualizing detailed spatial pattern of RNFL loss in glaucoma. Additionally, a regularized linear discriminant analysis classifier was used to automatically classify glaucoma, glaucoma-suspect, and control cases based on RNFL fshape metrics.