Optical Coherence Tomography and Optical Coherence Tomography Angiography in Neglected Diseases

Mahendradas, Padmamalini; Acharya, Isha; Rana, Vipin; Bansal, Reema; Amor, H. Ben; Khairallah, Moncef

doi:10.1080/09273948.2023.2211161

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2023

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“…They surpass SD-OCT in all aspects, including the scanning speed, sensitivity, and imaging depth [ 2 ]. They are gradually becoming indispensable in the diagnosis of, and follow-up for, ophthalmic diseases [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

The Latest Updates in Swept-Source Optical Coherence Tomography Angiography

Xia,

Hua

2023

Diagnostics

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Section: Introductionmentioning

confidence: 99%

The Latest Updates in Swept-Source Optical Coherence Tomography Angiography

Xia,

Hua

2023

Diagnostics

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“…By scanning the laser beam in the X and Y directions, 3D morphological images of the sample are obtained. OCT is already in wide clinical use in ophthalmology to help in the diagnosis and treatment of various eye pathologies [19,20]. It is now being explored for applications within the field of otolaryngology, commonly known as Ear, Nose, and Throat [21].…”

Section: Introductionmentioning

confidence: 99%

Automated Segmentation of Optical Coherence Tomography Images of the Human Tympanic Membrane Using Deep Learning

Oghalai,

Long,

Kim

et al. 2023

Algorithms

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Optical Coherence Tomography (OCT) is a light-based imaging modality that is used widely in the diagnosis and management of eye disease, and it is starting to become used to evaluate for ear disease. However, manual image analysis to interpret the anatomical and pathological findings in the images it provides is complicated and time-consuming. To streamline data analysis and image processing, we applied a machine learning algorithm to identify and segment the key anatomical structure of interest for medical diagnostics, the tympanic membrane. Using 3D volumes of the human tympanic membrane, we used thresholding and contour finding to locate a series of objects. We then applied TensorFlow deep learning algorithms to identify the tympanic membrane within the objects using a convolutional neural network. Finally, we reconstructed the 3D volume to selectively display the tympanic membrane. The algorithm was able to correctly identify the tympanic membrane properly with an accuracy of ~98% while removing most of the artifacts within the images, caused by reflections and signal saturations. Thus, the algorithm significantly improved visualization of the tympanic membrane, which was our primary objective. Machine learning approaches, such as this one, will be critical to allowing OCT medical imaging to become a convenient and viable diagnostic tool within the field of otolaryngology.

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Optical Coherence Tomography and Optical Coherence Tomography Angiography in Neglected Diseases

Cited by 2 publications

References 51 publications

The Latest Updates in Swept-Source Optical Coherence Tomography Angiography

The Latest Updates in Swept-Source Optical Coherence Tomography Angiography

Automated Segmentation of Optical Coherence Tomography Images of the Human Tympanic Membrane Using Deep Learning

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