Bridging 2D and 3D segmentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5D solutions

Recently, deep convolutional neural networks have achieved great success for medical image segmentation. However, unlike segmentation of natural images, most medical images such as MRI and CT are volumetric data. In order to make full use of volumetric information, 3D CNNs are widely used. However,...

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Bibliographic Details
Main Authors:	Ding, L. (Author), Liao, Q. (Author), Zhang, J. (Author), Zhang, Y. (Author)
Format:	Article
Language:	English
Published:	Elsevier Ltd 2022
Subjects:	2.5d segmentation method 2.5D Segmentation Methods 2D segmentation article Computation costs Computational efficiency Computation-efficient learning Computation-Efficient Learning Computerized tomography controlled study Convolution convolutional neural network Convolutional neural network Convolutional Neural Network Convolutional neural networks Deep neural networks Efficient learning Empirical studies empiricism image segmentation Image segmentation Information use intermethod comparison learning Magnetic resonance imaging Medical image segmentation Medical Image Segmentation Medical imaging Segmentation methods Volumetric analysis Volumetrics
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Bridging 2D and 3D segmentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5D solutions

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