Deep learning in retinal optical coherence tomography (OCT): A comprehensive survey
Retinal optical coherence tomography (OCT) images provide fundamental information
regarding the health of the posterior eye (eg, the retina and choroid). Thus, the development …
regarding the health of the posterior eye (eg, the retina and choroid). Thus, the development …
Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review
KC Pavithra, P Kumar, M Geetha… - Biocybernetics and …, 2023 - Elsevier
Abstract Diabetic Macular Edema (DME) is a potentially blinding consequence of Diabetic
Retinopathy (DR) as well as the leading cause of vision loss in diabetics. DME is …
Retinopathy (DR) as well as the leading cause of vision loss in diabetics. DME is …
MDC-net: A new convolutional neural network for nucleus segmentation in histopathology images with distance maps and contour information
Accurate segmentation of nuclei in digital pathology images can assist doctors in diagnosing
diseases and evaluating subsequent treatments. Manual segmentation of nuclei from …
diseases and evaluating subsequent treatments. Manual segmentation of nuclei from …
TSSK-Net: Weakly supervised biomarker localization and segmentation with image-level annotation in retinal OCT images
The localization and segmentation of biomarkers in OCT images are critical steps in retina-
related disease diagnosis. Although fully supervised deep learning models can segment …
related disease diagnosis. Although fully supervised deep learning models can segment …
RetiFluidNet: a self-adaptive and multi-attention deep convolutional network for retinal OCT fluid segmentation
Optical coherence tomography (OCT) helps ophthalmologists assess macular edema,
accumulation of fluids, and lesions at microscopic resolution. Quantification of retinal fluids is …
accumulation of fluids, and lesions at microscopic resolution. Quantification of retinal fluids is …
Deep learning with multiresolution handcrafted features for brain MRI segmentation
The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo
computed tomography (CT) images which are used to achieve positron emission …
computed tomography (CT) images which are used to achieve positron emission …
[HTML][HTML] LOCTseg: A lightweight fully convolutional network for end-to-end optical coherence tomography segmentation
This article presents a novel end-to-end automatic solution for semantic segmentation of
optical coherence tomography (OCT) images. OCT is a non-invasive imaging technology …
optical coherence tomography (OCT) images. OCT is a non-invasive imaging technology …
Joint disease classification and lesion segmentation via one-stage attention-based convolutional neural network in OCT images
X Liu, Y Bai, J Cao, J Yao, Y Zhang, M Wang - … Signal Processing and …, 2022 - Elsevier
Optical coherence tomography (OCT) is a useful tool for the diagnosis of macular diseases.
It is necessary to identify macular diseases and segment lesion areas for assisting …
It is necessary to identify macular diseases and segment lesion areas for assisting …
Brain MR images segmentation using 3D CNN with features recalibration mechanism for segmented CT generation
The segmentation of MR (magnetic resonance) images is a simple approach to create
Pseudo CT images which are useful for many medical imaging analysis applications. One of …
Pseudo CT images which are useful for many medical imaging analysis applications. One of …
Weakly-supervised localization and classification of biomarkers in OCT images with integrated reconstruction and attention
The retina biological markers play a crucial role in managing chronic eye conditions, and
optical coherence tomography (OCT) is widely used for ophthalmic diseases. However, the …
optical coherence tomography (OCT) is widely used for ophthalmic diseases. However, the …