Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers
Deep learning strategies, particularly convolutional neural networks (CNNs), are especially
suited to finding patterns in images and using those patterns for image classification. The …
suited to finding patterns in images and using those patterns for image classification. The …
Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search
The manual segmentation of individual retinal layers within optical coherence tomography
(OCT) images is a time-consuming task and is prone to errors. The investigation into …
(OCT) images is a time-consuming task and is prone to errors. The investigation into …
Automatic choroid layer segmentation from optical coherence tomography images using deep learning
The choroid layer is a vascular layer in human retina and its main function is to provide
oxygen and support to the retina. Various studies have shown that the thickness of the …
oxygen and support to the retina. Various studies have shown that the thickness of the …
Fully convolutional boundary regression for retina OCT segmentation
A major goal of analyzing retinal optical coherence tomography (OCT) images is retinal
layer segmentation. Accurate automated algorithms for segmenting smooth continuous layer …
layer segmentation. Accurate automated algorithms for segmenting smooth continuous layer …
Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography
Abstract Background and objectives: Spectral Domain Optical Coherence Tomography (SD-
OCT) is a volumetric imaging technique that allows measuring patterns between layers such …
OCT) is a volumetric imaging technique that allows measuring patterns between layers such …
Uncertainty-aware domain alignment for anatomical structure segmentation
Automatic and accurate segmentation of anatomical structures on medical images is crucial
for detecting various potential diseases. However, the segmentation performance of …
for detecting various potential diseases. However, the segmentation performance of …
MDAN-UNet: multi-scale and dual attention enhanced nested U-Net architecture for segmentation of optical coherence tomography images
W Liu, Y Sun, Q Ji - Algorithms, 2020 - mdpi.com
Optical coherence tomography (OCT) is an optical high-resolution imaging technique for
ophthalmic diagnosis. In this paper, we take advantages of multi-scale input, multi-scale side …
ophthalmic diagnosis. In this paper, we take advantages of multi-scale input, multi-scale side …
Embedded residual recurrent network and graph search for the segmentation of retinal layer boundaries in optical coherence tomography
For the study of various retinal diseases, an accurate quantitative analysis of the retinal layer
is essential for assessing the severity of the disease and diagnosing the progression of the …
is essential for assessing the severity of the disease and diagnosing the progression of the …
Automated assessment of breast cancer margin in optical coherence tomography images via pretrained convolutional neural network
The benchmark method for the evaluation of breast cancers involves microscopic testing of a
hematoxylin and eosin (H&E)‐stained tissue biopsy. Resurgery is required in 20% to 30% of …
hematoxylin and eosin (H&E)‐stained tissue biopsy. Resurgery is required in 20% to 30% of …
DME-DeepLabV3+: a lightweight model for diabetic macular edema extraction based on DeepLabV3+ architecture
Y Bai, J Li, L Shi, Q Jiang, B Yan, Z Wang - Frontiers in Medicine, 2023 - frontiersin.org
Introduction Diabetic macular edema (DME) is a major cause of vision impairment in the
patients with diabetes. Optical Coherence Tomography (OCT) is an important ophthalmic …
patients with diabetes. Optical Coherence Tomography (OCT) is an important ophthalmic …