Chapter 12
Segmentation of Symptomatic
Exudate-Associated Derangements in 3D
OCT Images
Lingjiao Pan and Xinjian Chen
Exudative AMD is an advanced form of AMD which can cause severe vision loss.
Quantitative analysis of the exudates is essential for the treatment and follow-up.
This chapter presents an automatic method for segmentation of the exudate regions
in 3D SD-OCT images using on a graph-based model.
12.1 Introduction
The primary cause of vision loss and blindness among the adults (>50 years old) is
Age-related macular degeneration (AMD) [1]. Exudative AMD or neovascular AMD
is an advanced form of AMD, due to the growth of abnormal blood vessels from the
choroidal vasculature, leading to sub- and intra-retinal leakage of vascular fluid.
Recently, anti-vascular endothelial growth factor agents (including Ranibizumab
and bevacizumab) [2–4] through intra-vitreal injection, as a treatment of exudative
AMD has become available. It leads to a regression of the neovascularization and
resulting resorption of fluid. The frequency of the injections is primarily guided by
the amount of intra-retinal fluid. The amount of intra-retinal fluid can be clinically
estimated subjectively from a limited number of spectral domain optical coherence
tomography (SD-OCT) slices [5–8]. The intra- and inter-observer variability is high
potentially leading to substantial inconsistency in treatment, and automated fluid segmentation has the potential to improve this [9, 10]. In this chapter, we use the term
symptomatic exudate-associated derangement (SEAD) for the main retinal manifestations of AMD, including subretinal fluid, intraretinal fluid, and pigment epithelial
L. Pan · X. Chen (B)
School of Electronics and Information Engineering, Soochow University, Suzhou, China
e-mail: xjchen@suda.edu.cn
X. Chen
State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China
© Science Press and Springer Nature Singapore Pte Ltd. 2019
X. Chen et al. (eds.), Retinal Optical Coherence Tomography Image Analysis,
Biological and Medical Physics, Biomedical Engineering,
https://doi.org/10.1007/978-981-13-1825-2_12
345
Segmentation of Symptomatic
Exudate-Associated Derangements in 3D
OCT Images
Lingjiao Pan and Xinjian Chen
Exudative AMD is an advanced form of AMD which can cause severe vision loss.
Quantitative analysis of the exudates is essential for the treatment and follow-up.
This chapter presents an automatic method for segmentation of the exudate regions
in 3D SD-OCT images using on a graph-based model.
12.1 Introduction
The primary cause of vision loss and blindness among the adults (>50 years old) is
Age-related macular degeneration (AMD) [1]. Exudative AMD or neovascular AMD
is an advanced form of AMD, due to the growth of abnormal blood vessels from the
choroidal vasculature, leading to sub- and intra-retinal leakage of vascular fluid.
Recently, anti-vascular endothelial growth factor agents (including Ranibizumab
and bevacizumab) [2–4] through intra-vitreal injection, as a treatment of exudative
AMD has become available. It leads to a regression of the neovascularization and
resulting resorption of fluid. The frequency of the injections is primarily guided by
the amount of intra-retinal fluid. The amount of intra-retinal fluid can be clinically
estimated subjectively from a limited number of spectral domain optical coherence
tomography (SD-OCT) slices [5–8]. The intra- and inter-observer variability is high
potentially leading to substantial inconsistency in treatment, and automated fluid segmentation has the potential to improve this [9, 10]. In this chapter, we use the term
symptomatic exudate-associated derangement (SEAD) for the main retinal manifestations of AMD, including subretinal fluid, intraretinal fluid, and pigment epithelial
L. Pan · X. Chen (B)
School of Electronics and Information Engineering, Soochow University, Suzhou, China
e-mail: xjchen@suda.edu.cn
X. Chen
State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China
© Science Press and Springer Nature Singapore Pte Ltd. 2019
X. Chen et al. (eds.), Retinal Optical Coherence Tomography Image Analysis,
Biological and Medical Physics, Biomedical Engineering,
https://doi.org/10.1007/978-981-13-1825-2_12
345