Automated 3D Reconstruction and Segmentation from Optical Coherence Tomography

被引：0

作者：

Eichel, Justin A. ^{[1
]}

Bizheva, Kostadinka K. ^{[2
]}

Clausi, David A. ^{[1
]}

Fieguth, Paul W. ^{[1
]}

机构：

[1] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada

[2] Univ Waterloo, Dept Phys & Astron, Waterloo, ON N2L 3G1, Canada

来源：

COMPUTER VISION-ECCV 2010, PT III | 2010年 / 6313卷

基金：

加拿大自然科学与工程研究理事会;

关键词：

OCT; UHROCT; cornea; non-invasive imaging; statistical modelling; segmentation; reconstruction; CONFOCAL MICROSCOPY; IMAGES; CORNEA;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Ultra-High Resolution Optical Coherence Tomography is a novel imaging technology that allows non-invasive, high speed, cellular resolution imaging of anatomical structures in the human eye, including the retina and the cornea. A three-dimensional study of the cornea, for example, requires the segmentation and mutual alignment of a large number of two-dimensional images. Such segmentation has, until now, only been undertaken by hand for individual two-dimensional images; this paper presents a method for automated segmentation, opening substantial opportunities for 3D corneal imaging and analysis, using many hundreds of 2D slices.

引用

页码：44 / +

页数：3

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