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首页> 外文会议>International conference on medical imaging computing and computer-assisted intervention >3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes
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3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes

机译：3D各向异性混合网络：将卷积特征从2D图像转移到3D各向异性体

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摘要

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.
机译：尽管深卷积神经网络（CNN）已成功应用于2D图像分析，但是将其应用于3D医学图像仍然具有挑战性，尤其是当切片内分辨率远高于切片间分辨率时。我们提出了一种3D各向异性混合网络（AH-Net），该网络将从2D图像中学到的卷积特征转移到3D各向异性体积。这种传输继承了切片内信息所需的强大泛化能力，同时自然利用切片间信息进行更有效的建模。我们在两种不同的医学图像分析任务上尝试使用提出的3D AH-Net，即从数字乳房断层合成体积中进行病变检测，以及从计算机断层扫描体积中进行肝脏和肝肿瘤分割，并获得最新的结果。

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