A multimodal imaging platform with integrated simultaneous photoacoustic microscopy, optical coherence tomography, optical Doppler tomography and fluorescence microscopy

Dadkhah, Arash; Zhou, Jun; Yeasmin, Nusrat; Jiao, Shuliang

doi:10.1117/12.2289211

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…Emerging multimodal techniques, such as combined OCT and SLO, 127 and OCT with photoacoustic microscopy (PAM), optical Doppler tomography (ODT), 128 and fluorescence microscopy, 129 enable interesting joint training from complementary modalities with each of them having different strengths. For example, in practice, the lower quality but inexpensive modality could be computationally enhanced.…”

Section: Embedded Ophthalmic Devicesmentioning

confidence: 99%

“…Emerging multimodal techniques such as combined OCT and SLO 123 , and OCT with photoacoustic microscopy (PAM), optical Doppler tomography (ODT) 124 , and fluorescence microscopy 125 , enable interesting joint training from complimentary modalities with each of them having different strengths. For example, in practice the lower quality but inexpensive modality could be computationally enhanced 126 Inter-vendor differences could be further addressed by repeating each measurement with different OCT machines as taken into account with clinical diagnosis network by De Fauw et al 8 .…”

Section: Physics-based Ground Truthsmentioning

confidence: 99%

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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: Embedded Ophthalmic Devicesmentioning

confidence: 99%