Feasibility of the soft attention-based models for automatic segmentation of OCT kidney images

Feasibility of the soft attention-based models for automatic segmentation of OCT kidney images

Clinically, optical coherence tomography (OCT) has been utilized to obtain the images of the kidney’s proximal convoluted tubules (PCTs), which can be used to quantify the morphometric parameters such as tubular density and diameter. Such parameters are useful for evaluating the status of the donor...

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Bibliographic Details
Main Authors: Chen, Y. (Author), Du, X. (Author), Huan, T. (Author), Moradi, M. (Author)
Format: Article
Language:English
Published: Optica Publishing Group (formerly OSA) 2022
Subjects:
article
attention
Automatic image analysis
Automatic segmentations
clinical evaluation
Convolution
convolutional neural network
Convolutional neural network
Convolutional neural networks
deep learning
Deep learning
feasibility study
human
human experiment
Human readers
image analysis
Image segmentation
kidney
Learning models
Morphometric parameters
Neural-networks
optical coherence tomography
Optical tomography
pattern recognition
Pattern recognition
Performance
recall
reliability
Segmentation accuracy
transplantation
Tubulars
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