Detection of hard exudate using retinal optical coherence tomography (OCT) images

Patil, Ankita; Chakravorty, Chandrani

doi:10.1016/j.gltp.2021.08.067

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Our ensemble bagged tree approach performed better than the BT-WS method [36] because our method utilized a more comprehensive set of features, while BT-WS used only an intensity feature. In addition, the watershed segmentation used in the BT-WS method for outlining their boundaries was unsuitable for low-quality and noisy OCT images.…”

Section: Discussionmentioning

confidence: 93%

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Automatic exudate and aneurysm segmentation in OCT images using UNET++ and hyperreflective-foci feature based bagged tree ensemble

Tanthanathewin,

Wongrattanapipat,

Khaing

et al. 2024

PLoS ONE

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Diabetic retinopathy’s signs, such as exudates (EXs) and aneurysms (ANs), initially develop from under the retinal surface detectable from optical coherence tomography (OCT) images. Detecting these signs helps ophthalmologists diagnose DR sooner. Detecting and segmenting exudates (EXs) and aneurysms (ANs) in medical images is challenging due to their small size, similarity to other hyperreflective regions, noise presence, and low background contrast. Furthermore, the scarcity of public OCT images featuring these abnormalities has limited the number of studies related to the automatic segmentation of EXs and ANs, and the reported performance of such studies has not been satisfactory. This work proposes an efficient algorithm that can automatically segment these anomalies by improving key steps in the process. The potential area where these hyper-reflective EXs and ANs occur was scoped by our method using a deep-learning U-Net++ program. From this area, the candidates for EX-AN were segmented using the adaptive thresholding method. Nine features based on appearances, locations, and shadow markers were extracted from these candidates. They were trained and tested using bagged tree ensemble classifiers to obtain only EX-AN blobs. The proposed method was tested on a collection of a public dataset comprising 80 images with hand-drawn ground truths. The experimental results showed that our method could segment EX-AN blobs with average recall, precision, and F1-measure as 87.9%, 86.1%, and 87.0%, respectively. Its F1-measure drastically outperformed two comparative methods, binary thresholding and watershed (BT-WS) and adaptive thresholding with shadow tracking (AT-ST), by 78.0% and 82.1%, respectively.

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Section: Discussionmentioning

confidence: 93%

“…Patil and Chakravorty [36] proposed a detection method of EXs in DR's visual deficiency through OCT images. Pre-processing was first applied to the OCT images, followed by intensity thresholding to separate foreground and background.…”

Section: Exudates and Aneurysm Segmentationmentioning

confidence: 99%

Automatic exudate and aneurysm segmentation in OCT images using UNET++ and hyperreflective-foci feature based bagged tree ensemble

Tanthanathewin,

Wongrattanapipat,

Khaing

et al. 2024

PLoS ONE

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“…Moreover, follow-up imaging can evaluate a disease's recurrence and the efficacy of a treatment. The results and conclusions drawn from OCT images in the field of retinal diseases, and how to employ these conclusions in clinical settings, necessitate a review of the literature in this area [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Retinal lesions classification for diabetic retinopathy using custom ResNet-based classifier

Ajith Kumar,

Satheesh Kumar

2024

IJEECS

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<span>Failure to diagnose and treat retinal illnesses on time might lead to irreversible blindness. The focus is on three common retinal lesions associated with diabetic retinopathy (DR): microaneurysms (MAs), haemorrhages, and exudates. The proposed solution leverages deep learning, employing a customized residual network (ResNet) based classifier trained on real-time retinal images meticulously annotated and graded by ophthalmologists. Annotation noise was a significant obstacle addressed by downsampling and augmenting the data. Compared to cutting-edge techniques, this one performs better with test-set accuracy of 93.34% across all classes. This approach holds great promise for enhancing early detection and treatment of DR by automating the recognition of these vital retinal abnormalities. The ability to automatically classify these symptoms can aid clinicians in making more precise diagnosis and starting treatments sooner. This research shows that deep learning-based approaches are highly effective, especially when combined with a customised ResNet-based classifier and thorough pre-processing steps. We observed that this method provides the ability to better the lives of patients and lower the rate of permanent blindness resulting from retinal disorders.</span>

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Neural Networks and Saliency Maps in Diabetic Retinopathy Diagnosis

Szymkowski

2023

Lecture Notes in Computer Science

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Detection of hard exudate using retinal optical coherence tomography (OCT) images

Cited by 4 publications

References 8 publications

Automatic exudate and aneurysm segmentation in OCT images using UNET++ and hyperreflective-foci feature based bagged tree ensemble

Automatic exudate and aneurysm segmentation in OCT images using UNET++ and hyperreflective-foci feature based bagged tree ensemble

Retinal lesions classification for diabetic retinopathy using custom ResNet-based classifier

Neural Networks and Saliency Maps in Diabetic Retinopathy Diagnosis

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