Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography

Venhuizen, Freerk G.; Ginneken, Bram van; Liefers, Bart; Asten, Freekje van; Schreur, Vivian; Fauser, Sascha; Hoyng, Carel B.; Theelen, Thomas; Sánchez, Clara I.

doi:10.1364/boe.9.001545

Cited by 126 publications

(76 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notably, the algorithm detected all urgent referral cases within the patient cohort [82]. With the development of DL, some researchers have extended their algorithms to perform segmentation of pigment epithelium detachment, fluid and vessels [83][84][85].…”

Section: Optical Coherence Tomography (Oct)mentioning

confidence: 99%

Application of machine learning in ophthalmic imaging modalities

Tong

Lü

et al. 2020

Eye and Vis

View full text Add to dashboard Cite

In clinical ophthalmology, a variety of image-related diagnostic techniques have begun to offer unprecedented insights into eye diseases based on morphological datasets with millions of data points. Artificial intelligence (AI), inspired by the human multilayered neuronal system, has shown astonishing success within some visual and auditory recognition tasks. In these tasks, AI can analyze digital data in a comprehensive, rapid and non-invasive manner. Bioinformatics has become a focus particularly in the field of medical imaging, where it is driven by enhanced computing power and cloud storage, as well as utilization of novel algorithms and generation of data in massive quantities. Machine learning (ML) is an important branch in the field of AI. The overall potential of ML to automatically pinpoint, identify and grade pathological features in ocular diseases will empower ophthalmologists to provide high-quality diagnosis and facilitate personalized health care in the near future. This review offers perspectives on the origin, development, and applications of ML technology, particularly regarding its applications in ophthalmic imaging modalities.

show abstract

Section: Optical Coherence Tomography (Oct)mentioning

confidence: 99%

Application of machine learning in ophthalmic imaging modalities

Tong

Lü

et al. 2020

Eye and Vis

View full text Add to dashboard Cite

show abstract

“…A fully convolutional network was proposed for semantic segmentation of retinal OCT B-scans into seven layers and fluid masses [43]. A deep learning algorithm to quantify and segment the intraretinal cystoid fluid in SD-OCT images using FCNN is proposed by [44]. Another study is focused on Geographic Atrophy (GA) segmentation method using a deep network [45].…”

Section: Related Workmentioning

confidence: 99%

Characterization of coronary artery pathological formations from OCT imaging using deep learning

Abdolmanafi

Duong

Dahdah

et al. 2018

Biomed. Opt. Express

View full text Add to dashboard Cite

Coronary artery disease is the number one health hazard leading to the pathological formations in coronary artery tissues. In severe cases, they can lead to myocardial infarction and sudden death. Optical Coherence Tomography (OCT) is an interferometric imaging modality, which has been recently used in cardiology to characterize coronary artery tissues providing high resolution ranging from 10 to 20 µm. In this study, we investigate different deep learning models for robust tissue characterization to learn the various intracoronary pathological formations caused by Kawasaki disease (KD) from OCT imaging. The experiments are performed on 33 retrospective cases comprising of pullbacks of intracoronary cross-sectional images obtained from different pediatric patients with KD. Our approach evaluates deep features computed from three different pre-trained convolutional networks. Then, a majority voting approach is applied to provide the final classification result. The results demonstrate high values of accuracy, sensitivity, and specificity for each tissue (up to 0.99 ± 0.01). Hence, deep learning models and especially, the majority voting method are robust for automatic interpretation of the OCT images.

show abstract

“…In more recent times, deep learning techniques have also been applied, in works like the ones proposed by Lee et al [19], Venhuizen et al [20], Roy et al [21], Lu et al [22], and Chen et al [23], which aimed too for a precise segmentation and most of them based on the U-Net architecture of Ronnenberger et al [24], an encoder-decoder architecture with skip connections to preserve relevant features from the encoder in the decoder.…”

Section: Introductionmentioning

confidence: 99%

Intraretinal Fluid Pattern Characterization in Optical Coherence Tomography Images

Moura

Vidal

Novo

et al. 2020

Sensors

View full text Add to dashboard Cite

Optical Coherence Tomography (OCT) has become a relevant image modality in the ophthalmological clinical practice, as it offers a detailed representation of the eye fundus. This medical imaging modality is currently one of the main means of identification and characterization of intraretinal cystoid regions, a crucial task in the diagnosis of exudative macular disease or macular edema, among the main causes of blindness in developed countries. This work presents an exhaustive analysis of intensity and texture-based descriptors for its identification and classification, using a complete set of 510 texture features, three state-of-the-art feature selection strategies, and seven representative classifier strategies. The methodology validation and the analysis were performed using an image dataset of 83 OCT scans. From these images, 1609 samples were extracted from both cystoid and non-cystoid regions. The different tested configurations provided satisfactory results, reaching a mean cross-validation test accuracy of 92.69%. The most promising feature categories identified for the issue were the Gabor filters, the Histogram of Oriented Gradients (HOG), the Gray-Level Run-Length matrix (GLRL), and the Laws’ texture filters (LAWS), being consistently and considerably selected along all feature selector algorithms in the top positions of different relevance rankings.

show abstract

Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography

Cited by 126 publications

References 53 publications

Application of machine learning in ophthalmic imaging modalities

Application of machine learning in ophthalmic imaging modalities

Characterization of coronary artery pathological formations from OCT imaging using deep learning

Intraretinal Fluid Pattern Characterization in Optical Coherence Tomography Images

Contact Info

Product

Resources

About