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
Online Access:	View Fulltext in Publisher

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