Book chapter
Region-Based Segmentation of Capillary Density in Optical Coherence Tomography Angiography
Ophthalmic Medical Image Analysis, pp.18-25
Lecture Notes in Computer Science, Springer International Publishing
10/08/2019
DOI: 10.1007/978-3-030-32956-3_3
Abstract
Microvascular changes are one of the early symptoms of retinal diseases. Recently developed optical coherence tomography angiography (OCTA) technology allows visualization and analysis of the retinal microvascular network in a non-invasive way. However, automated analysis of microvascular changes in OCTA is not a trivial task. Current approaches often attempt to directly segment the microvasculature. These approaches generally have problems in cases of poor image quality and limited visibility of the vasculature. Evaluating the quality of the results is also challenging because of the difficulty of manually tracing the microvasculature, especially in cases of low image quality or with images with a larger field of view. In this work, we develop an automated deep-learning approach to assign each pixel within human OCTA en-face images the probability of belonging to a microvascular density region of each of the following categories: avascular, hypovascular, and capillary-dense. The AUCs (area under the receiver operating characteristic curves) were 0.99 (avascular), 0.93 (hypovascular), and 0.97 (capillary-dense) for segmenting each of the categories. The results show very good performance and enables global and region-based quantitative estimates of microvascular density even in relatively low-quality en-face images.
Details
- Title: Subtitle
- Region-Based Segmentation of Capillary Density in Optical Coherence Tomography Angiography
- Creators
- Wenxiang Deng - Iowa City VA Health Care System, Iowa City, USAMichelle R Tamplin - Department of Internal Medicine, The University of Iowa, Iowa City, USAIsabella M Grumbach - Department of Internal Medicine, The University of Iowa, Iowa City, USARandy H Kardon - Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, USAMona K Garvin - Iowa City VA Health Care System, Iowa City, USA
- Resource Type
- Book chapter
- Publication Details
- Ophthalmic Medical Image Analysis, pp.18-25
- Publisher
- Springer International Publishing; Cham
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-030-32956-3_3
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 10/08/2019
- Academic Unit
- Cardiovascular Medicine; Ophthalmology and Visual Sciences; Electrical and Computer Engineering; Internal Medicine; Radiation Oncology; Iowa Neuroscience Institute
- Record Identifier
- 9984070991702771
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