Swept-Source OCT for Evaluating the Lamina Cribrosa

Takusagawa, Hana L.; Hoguet, Ambika; Junk, Anna K.; Nouri‐Mahdavi, Kouros; Sudhakar, R.; Chen, Teresa C.

doi:10.1016/j.ophtha.2019.03.044

Cited by 39 publications

(13 citation statements)

References 39 publications

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“…However, compared with SD-OCT, SS-OCT has faster-scan speeds and greater depth penetration, allowing for scanning of larger areas and deeper layers. SS-OCT uses a laser light with a center wavelength of approximately 1050 nm [9–11,12 ▪▪ ,13,14].…”

Section: Technology Overview: Spectral Domain and Swept Source Optica...mentioning

confidence: 99%

New views on three-dimensional imaging technologies for glaucoma: an overview

Aparicio

Chen

2021

Current Opinion in Ophthalmology

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Purpose of review To summarize the literature on three-dimensional (3D) technological advances in ophthalmology, the quantitative methods associated with this, and their improved ability to help detect glaucoma disease progression. Recent findings Improvements in measuring glaucomatous structural changes are the result of dual innovations in optical coherence tomography (OCT) imaging technology and in associated quantitative software. Summary Compared with two-dimensional (2D) OCT parameters, newer 3D parameters provide more data and fewer artifacts.

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Section: Technology Overview: Spectral Domain and Swept Source Optica...mentioning

confidence: 99%

New views on three-dimensional imaging technologies for glaucoma: an overview

Aparicio

Chen

2021

Current Opinion in Ophthalmology

Self Cite

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“…In contrast, optical coherence tomography (OCT) has the advantages of high-resolution (1–20 μm), real-time and large-depth (1–2 mm) imaging which is suitable for imaging oral mucosa. OCT has been applied in biomedical fields since it was first introduced in 1991 [ 14 ], such as ophthalmology [ 15 ], cardiology [ 16 ], gastroenterology [ 17 ], and dermatology [ 18 ]. In the oral cavity, studies based on OCT have been attempted to differentiate benign and OSCC by different structural or optical indicators, including the thickness of the epithelium, the intactness of basement membrane, or optical scattering properties [ 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Automated Identification and Visualization of Oral Cancer in Optical Coherence Tomography Images

et al. 2023

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Early detection and diagnosis of oral cancer are critical for a better prognosis, but accurate and automatic identification is difficult using the available technologies. Optical coherence tomography (OCT) can be used as diagnostic aid due to the advantages of high resolution and non-invasion. We aim to evaluate deep-learning-based algorithms for OCT images to assist clinicians in oral cancer screening and diagnosis. An OCT data set was first established, including normal mucosa, precancerous lesion, and oral squamous cell carcinoma. Then, three kinds of convolutional neural networks (CNNs) were trained and evaluated by using four metrics (accuracy, precision, sensitivity, and specificity). Moreover, the CNN-based methods were compared against machine learning approaches through the same dataset. The results show the performance of CNNs, with a classification accuracy of up to 96.76%, is better than the machine-learning-based method with an accuracy of 92.52%. Moreover, visualization of lesions in OCT images was performed and the rationality and interpretability of the model for distinguishing different oral tissues were evaluated. It is proved that the automatic identification algorithm of OCT images based on deep learning has the potential to provide decision support for the effective screening and diagnosis of oral cancer.

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“…Many researchers have been studying how LC responds to changes and causes of eye diseases [16,17]. However, all the methods for the quantification of the LC structure are still conventional, traditional, and manual.…”

Section: Introductionmentioning

confidence: 99%

Automatic Quantification of Anterior Lamina Cribrosa Structures in Optical Coherence Tomography Using a Two-Stage CNN Framework

Rahman

Jeong

Kim

et al. 2021

Sensors

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In this study, we propose a new intelligent system to automatically quantify the morphological parameters of the lamina cribrosa (LC) of the optical coherence tomography (OCT), including depth, curve depth, and curve index from OCT images. The proposed system consisted of a two-stage deep learning (DL) model, which was composed of the detection and the segmentation models as well as a quantification process with a post-processing scheme. The models were used to solve the class imbalance problem and obtain Bruch’s membrane opening (BMO) as well as anterior LC information. The detection model was implemented by using YOLOv3 to acquire the BMO and LC position information. The Attention U-Net segmentation model is used to compute accurate locations of the BMO and LC curve information. In addition, post-processing is applied using polynomial regression to attain the anterior LC curve boundary information. Finally, the numerical values of morphological parameters are quantified from BMO and LC curve information using an image processing algorithm. The average precision values in the detection performances of BMO and LC information were 99.92% and 99.18%, respectively, which is very accurate. A highly correlated performance of R2 = 0.96 between the predicted and ground-truth values was obtained, which was very close to 1 and satisfied the quantification results. The proposed system was performed accurately by fully automatic quantification of BMO and LC morphological parameters using a DL model.

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Swept-Source OCT for Evaluating the Lamina Cribrosa

Cited by 39 publications

References 39 publications

New views on three-dimensional imaging technologies for glaucoma: an overview

New views on three-dimensional imaging technologies for glaucoma: an overview

Deep-Learning-Based Automated Identification and Visualization of Oral Cancer in Optical Coherence Tomography Images

Automatic Quantification of Anterior Lamina Cribrosa Structures in Optical Coherence Tomography Using a Two-Stage CNN Framework

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