Detection of diabetic neovascularisation using single-capture 65°-widefield optical coherence tomography angiography

Stino, Heiko; Niederleithner, Michael; Iby, Johannes; Sedova, Aleksandra; Schlegl, Thomas; Steiner, Irene; Sacu, Stefan; Drexler, Wolfgang; Schmoll, Tilman; Leitgeb, Rainer A.; Schmidt‐Erfurth, Ursula; Pollreisz, Andreas

doi:10.1136/bjo-2022-322134

Cited by 11 publications

(6 citation statements)

References 29 publications

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“…Widefield OCTA scans provide information of the peripheral retinal areas, while the latest fast acquisition times allow for comfortable imaging of the patients. 3,4 artificial intelligence-algorithms have been utilized in the past to classify DR and even its severity. Most algorithms, however, require precise labels indicating obvious or known lesions.…”

Section: Introductionmentioning

confidence: 99%

Stable classification of diabetic structures from incorrectly labeled optical coherence tomography angiography en face images using multi instance learning

Matten¹,

Scherer²,

Schlegl³

et al. 2023

Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII

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We present a multiple instance learning-based network, MIL-ResNet14, detecting biomarkers for diabetic retinopathy in a widefield optical coherence tomography angiography dataset with high accuracy, without the necessity of annotations other than the information of whether a scan stems from a diabetic patient or not. Previously introduced deep learning-based classifiers were able to support the detection of diabetic biomarkers in OCTA images, however, require expert labeling on a pixel-level, a labor-intensive and expensive process. We evaluated our proposed architecture against two proven-capable classifiers, ResNet14 and VGG16. The dataset we applied for this study was acquired with a MHz A-Scan rate widefield Swept Source-OCT device. We utilized a total of 352 en face images, displaying retinal vasculature over a field of view of 18 mm x 18 mm. MIL-ResNet14 outperformed both other networks with an F-score of 0.95, a precision of 0.909 and an Area Under the Curve of 0.973. In addition, we could show via saliency overlays of gradient-weighted class activation mappings onto the en face images, that MIL-ResNet14 pays special attention to clinically relevant biomarkers like ischemic areas and retinal vessel anomalies. This could therefore function as a vigorous diagnostic decision support tool for clinical ophthalmologic screenings.

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Section: Introductionmentioning

confidence: 99%

Stable classification of diabetic structures from incorrectly labeled optical coherence tomography angiography en face images using multi instance learning

Matten¹,

Scherer²,

Schlegl³

et al. 2023

Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII

Self Cite

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“…Optical coherence tomography angiography (OCTA), a non-invasive functional extension of optical coherence tomography (OCT), is emerging as a promising tool for the diagnosis of DR. 3 Its ability to visualize the retinal vasculature allows trained personnel to locate areas containing lesions. Recent findings show that lesions occur in the retinal periphery in the early stages of DR. 4 This has led to an increasing demand for large imaging fields. Recent developments in laser technology allow fast and comfortable scanning of large field of views (FoVs) in patients.…”

Section: Introductionmentioning

confidence: 99%

Stable classification of diabetic structures from incorrectly labeled OCTA en face images using multi instance learning

Matten¹,

Scherer²,

Schlegl³

et al. 2023

Medical Imaging 2023: Image Processing

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Previously introduced deep learning classifiers were able to support diabetic biomarker detection in OCTA en face images, but require pixel-by-pixel expert labeling, which is a labor-intensive and expensive process. We present a multiple-instance learning-based network, MIL-ResNet,14 that detects clinically relevant diabetic retinopathy biomarkers in a wide-angle (65°) OCTA dataset with high accuracy without annotation. We evaluated our proposed architecture against two well-established machine learning classifiers, ResNet14 and VGG16. The dataset we used for this study was acquired with a MHz A-scan rate swept source OCT device. We used a total of 352 en face images representing the retinal vasculature over an 18 mm x 18 mm field of view. MIL-ResNet14 outperformed the other two networks with an F-score of 0.95, a precision of 0.909 and an area under the curve of 0.973. In addition, we were able to demonstrate that MIL-ResNet14 paid special attention to relevant biomarkers such as ischemic areas and retinal vascular abnormalities by saliency overlay of gradient-weighted class activation maps on top of the en face images. Thus, OCTA could be used as a powerful diagnostic decision support tool for clinical ophthalmic screening in combination with our MIL approach.

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“…Diabetic retinopathy (DR) is the leading cause of vision loss in working-age adults. Optical Coherence Tomography-Angiography (OCTA), a non-invasive functional extension of optical coherence tomography, is emerging as the potentially most promising technique for diagnosing DR [1,2]. Recent technological advances allow for ever-increasing large OCTA scans of the retina including its periphery [3,4].…”

Section: Introductionmentioning

confidence: 99%

Stable detection of diabetic lesions in widefield optical coherence tomography angiography en face images using a multiple instance learning binary classifier

Matten,

Scherer,

Schlegl

et al. 2023

Optical Coherence Imaging Techniques and Imaging in Scattering Media V

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Detection of diabetic neovascularisation using single-capture 65°-widefield optical coherence tomography angiography

Cited by 11 publications

References 29 publications

Stable classification of diabetic structures from incorrectly labeled optical coherence tomography angiography en face images using multi instance learning

Stable classification of diabetic structures from incorrectly labeled optical coherence tomography angiography en face images using multi instance learning

Stable classification of diabetic structures from incorrectly labeled OCTA en face images using multi instance learning

Stable detection of diabetic lesions in widefield optical coherence tomography angiography en face images using a multiple instance learning binary classifier

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