OCTID: Optical coherence tomography image database

Gholami, Peyman; Roy, Priyanka; Parthasarathy, Mohana Kuppuswamy; Lakshminarayanan, Vasudevan

doi:10.1016/j.compeleceng.2019.106532

Cited by 76 publications

(49 citation statements)

References 12 publications

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“…Convenience sampling was used, and both prevalence of prior community contributions (Kaggle) and citations were considered. Four datasets were selected, including two retinal fundus photograph datasets (Messidor-2, n = 1,744; EyePACS, n = 35,108) and two OCT datasets (Waterloo, n = 572; Kermany, n = 101,418) representing small and large dataset sizes for each respective modality 16,[44][45][46][47] . Patient demographics and inclusion criteria for each of these datasets are published in accordance with the source datasets.…”

Section: Methodsmentioning

confidence: 99%

Code-free deep learning for multi-modality medical image classification

et al. 2021

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A number of large technology companies have created code-free cloud-based platforms that allow researchers and clinicians without coding experience to create deep learning algorithms. In this study, we comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification models. The mean (s.d.) F1 scores across platforms for all model–dataset pairs were as follows: Amazon, 93.9 (5.4); Apple, 72.0 (13.6); Clarifai, 74.2 (7.1); Google, 92.0 (5.4); MedicMind, 90.7 (9.6); Microsoft, 88.6 (5.3). The platforms demonstrated uniformly higher classification performance with the optical coherence tomography modality. Potential use cases given proper validation include research dataset curation, mobile ‘edge models’ for regions without internet access, and baseline models against which to compare and iterate bespoke deep learning approaches.

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Section: Methodsmentioning

confidence: 99%

Code-free deep learning for multi-modality medical image classification

et al. 2021

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“…The computer is equipped with Windows 10 platform with an Intel Core i7 CPU at 2.5 GHz and 8 GB RAM. The OCT images used in this work all came from a public source called Optical Coherence Tomography Image Retinal Database (OCTID) [41], [42]. The high-resolution JPEG images have 2 mm scan length and 500x750 pixel size (1 pixel ∼ 3.9 µm).…”

Section: Methodsmentioning

confidence: 99%

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images

Mendes

Lucena

et al. 2021

IEEE Access

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The retina is a part of the ocular system responsible for vision. In the central region of the retina is the macula, that enables detailed view. There is a distinct macular disease called Macular Hole (MH). It causes a condition of low vision related to the weakening of the fovea, high myopia, eye trauma and severe exposure to the sun. A surgery depends of the size and shape of the MH. A macular hole can be identified in Optical Coherence Tomography (OCT) images through the top boundaries of the Internal Limiting Membrane (ILM) and the Retinal Pigment Epithelium (RPE). Manual segmentation of OCT images is time consuming whereas automatic segmentation is fast and has a low computational cost, and consequently of interest to specialists. Thus, the main objective of this work is to develop an algorithm that automatically segments the ILM boundary layer and the area of the MH in OCT images. Another objective that was also pursued included the automatic acquisition of MH measurements. The segmentation was performed through a set of techniques involving shortest distance from a point to a curve (Euclidean Distance), Flood Fill and Border Following algorithms. The proposed method reached satisfactory results for all applications made. The automatic segmentation of MH and the extraction of its measures is a significant contribution to aid the medical diagnosis of the macular hole pathology.

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“…The images are in a high resolution (jpeg format) that can be downloaded and used with no need for preprocessing. The database contains more than 500 spectral-domain OCT volumetric scans, divided into ve datasets: Normal 206 images, Agerelated Macular Degeneration 55 images, Macular Hole 102 images, Diabetic Retinopathy 107 images and Central Serous Retinopathy 102 images [16]. The images were collected from a raster scan protocol with size 512 × 1024 pixels.…”

Section: Methodsmentioning

confidence: 99%

Classification of OCT Images for Detecting Diabetic Retinopathy Disease Using Machine Learning

Aldahami

Alqasemi

2020

Preprint

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Background Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease because of their capability of capturing micrometer-resolution.Method An automated technique was introduced to differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160 images were used for classifiers’ training, and 54 images were used for testing. Different features were extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features.Results The experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal retina with ROC Area Under the Curve (AUC) of 100%.Conclusions The retinal OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP features has a significant impact on the achieved results. The result has better performance than previously proposed methods in the literature.

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OCTID: Optical coherence tomography image database

Cited by 76 publications

References 12 publications

Code-free deep learning for multi-modality medical image classification

Code-free deep learning for multi-modality medical image classification

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images

Classification of OCT Images for Detecting Diabetic Retinopathy Disease Using Machine Learning

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