U-net and its variants for medical image segmentation: A review of theory and applications
U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …
tasks. These traits provide U-net with a high utility within the medical imaging community …
Generative adversarial networks in medical image segmentation: A review
S Xun, D Li, H Zhu, M Chen, J Wang, J Li… - Computers in biology …, 2022 - Elsevier
Abstract Purpose Since Generative Adversarial Network (GAN) was introduced into the field
of deep learning in 2014, it has received extensive attention from academia and industry …
of deep learning in 2014, it has received extensive attention from academia and industry …
RETOUCH: The retinal OCT fluid detection and segmentation benchmark and challenge
H Bogunović, F Venhuizen, S Klimscha… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
Retinal swelling due to the accumulation of fluid is associated with the most vision-
threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of …
threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of …
Global and local feature reconstruction for medical image segmentation
Learning how to capture long-range dependencies and restore spatial information of down-
sampled feature maps are the basis of the encoder-decoder structure networks in medical …
sampled feature maps are the basis of the encoder-decoder structure networks in medical …
Intra-and inter-slice contrastive learning for point supervised oct fluid segmentation
OCT fluid segmentation is a crucial task for diagnosis and therapy in ophthalmology. The
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …
Multi-modal retinal image classification with modality-specific attention network
Recently, automatic diagnostic approaches have been widely used to classify ocular
diseases. Most of these approaches are based on a single imaging modality (eg, fundus …
diseases. Most of these approaches are based on a single imaging modality (eg, fundus …
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 …
Automatic fluid segmentation in retinal optical coherence tomography images using attention based deep learning
Optical coherence tomography (OCT) is one of the most commonly used ophthalmic
diagnostic techniques. Macular Edema (ME) is the swelling of the macular region in the eye …
diagnostic techniques. Macular Edema (ME) is the swelling of the macular region in the eye …
Methodological challenges of deep learning in optical coherence tomography for retinal diseases: a review
Artificial intelligence (AI)-based automated classification and segmentation of optical
coherence tomography (OCT) features have become increasingly popular. However, its 3 …
coherence tomography (OCT) features have become increasingly popular. However, its 3 …
Multi-scale pathological fluid segmentation in OCT with a novel curvature loss in convolutional neural network
G Xing, L Chen, H Wang, J Zhang… - … on Medical Imaging, 2022 - ieeexplore.ieee.org
The segmentation of pathological fluid lesions in optical coherence tomography (OCT),
including intraretinal fluid, subretinal fluid, and pigment epithelial detachment, is of great …
including intraretinal fluid, subretinal fluid, and pigment epithelial detachment, is of great …