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Erschienen in:
2018 | OriginalPaper | Buchkapitel
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes
verfasst von : Siqi Liu, Daguang Xu, S. Kevin Zhou, Olivier Pauly, Sasa Grbic, Thomas Mertelmeier, Julia Wicklein, Anna Jerebko, Weidong Cai, Dorin Comaniciu
Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
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Abstract
While deep convolutional neural networks (CNN) have been successfully applied to 2D image analysis, it is still challenging to apply them to 3D medical images, especially when the within-slice resolution is much higher than the between-slice resolution. We propose a 3D Anisotropic Hybrid Network (AH-Net) that transfers convolutional features learned from 2D images to 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for within-slice information while naturally exploiting between-slice information for more effective modelling. We experiment with the proposed 3D AH-Net on two different medical image analysis tasks, namely lesion detection from a Digital Breast Tomosynthesis volume, and liver and liver tumor segmentation from a Computed Tomography volume and obtain state-of-the-art results.