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Image Analysis for Ophthalmology: Segmentation and Quantification of Retinal Vascular Systems

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Ocular Fluid Dynamics

Abstract

The retina is directly connected to the central nervous system and the vascular circulation, which uniquely enables three-dimensional retinal tissue structures and blood flow dynamics to be imaged and visualized from the exterior using non-invasive imaging modalities. Rapid advances in the types of diagnostic imaging modalities, combined with image processing, computer vision, artificial intelligence, and machine learning algorithms for quantitative image analytics are opening up a host of new possibilities for early diagnosis and treatment of a broad range of eye and systemic diseases with clinical impact. Incorporating patient-specific imaging to estimate geometric structures of vessel morphology and boundary conditions as input to the mathematical and computational fluid-dynamics modeling frameworks described in earlier chapters will enable new ways to predict treatment outcomes and model physiological effects at the systemic level. This chapter describes a set of widely used retinal imaging modalities, including fundoscopy, fluorescein angiography (FA), and optical coherence tomography (OCT), along with emerging modalities to measure retinal blood flow dynamics like optical coherence tomography angiography (OCTA) and laser speckle flowgraphy (LSFG). We use vessel segmentation and quantification as a prototypical ophthalmology image analysis pipeline that can be applied across imaging modalities, to describe processing techniques for measuring geometrical vascular structures. Current challenges and future opportunities especially in using artificial intelligence and deep learning architectures for patient optimized precision medicine and clinical efficacy are highlighted.

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Acknowledgements

Partial support was provided by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award NIH R01NS110915 (KP). The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. The authors appreciate the valuable collaboration and discussions with colleagues and lab members on microvasculature image analysis including Virginia H. Huxley, Olga V. Glinskii, V.B. Surya Prasath, Yasmin S. Kassim, and Rengarajan Pelapur.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA

    Kannappan Palaniappan, Filiz Bunyak & Shyam S. Chaurasia

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  1. Kannappan Palaniappan
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  2. Filiz Bunyak
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  3. Shyam S. Chaurasia
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Correspondence to Kannappan Palaniappan .

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  1. Department of Math Sciences, Indiana University – Purdue University I, Indianapolis, IN, USA

    Giovanna Guidoboni

  2. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Alon Harris

  3. Department of Mathematics, Polytechnic University of Milan, Milan, Italy

    Riccardo Sacco

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Palaniappan, K., Bunyak, F., Chaurasia, S.S. (2019). Image Analysis for Ophthalmology: Segmentation and Quantification of Retinal Vascular Systems. In: Guidoboni, G., Harris, A., Sacco, R. (eds) Ocular Fluid Dynamics. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser, Cham. https://doi.org/10.1007/978-3-030-25886-3_22

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