Spectral Domain Optical Coherence Tomography-Based Imaging Biomarkers and Hyperspectral Imaging

Ruia, Surabhi; Saxena, Sandeep

doi:10.1007/978-81-322-3610-8_7

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…This enables snapshot multispectral fundus imaging for retinal oximetry 105 . These additional spectral bands or custom illuminants could also be used to aid the image processing itself before clinical diagnostics 106 . For example, segmenting the macular region becomes easier with a spectral band around blue 460 nm, as the macular pigment absorbs strongly at that wavelength and appears darker than its background on this band 107 .…”

Section: High-dynamic Range (Hdr) Ophthalmic Imagingmentioning

confidence: 99%

Embedded deep learning in ophthalmology: making ophthalmic imaging smarter

et al. 2019

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Deep learning has recently gained high interest in ophthalmology due to its ability to detect clinically significant features for diagnosis and prognosis. Despite these significant advances, little is known about the ability of various deep learning systems to be embedded within ophthalmic imaging devices, allowing automated image acquisition. In this work, we will review the existing and future directions for ‘active acquisition’–embedded deep learning, leading to as high-quality images with little intervention by the human operator. In clinical practice, the improved image quality should translate into more robust deep learning–based clinical diagnostics. Embedded deep learning will be enabled by the constantly improving hardware performance with low cost. We will briefly review possible computation methods in larger clinical systems. Briefly, they can be included in a three-layer framework composed of edge, fog, and cloud layers, the former being performed at a device level. Improved egde-layer performance via ‘active acquisition’ serves as an automatic data curation operator translating to better quality data in electronic health records, as well as on the cloud layer, for improved deep learning–based clinical data mining.

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Section: High-dynamic Range (Hdr) Ophthalmic Imagingmentioning

confidence: 99%