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首页> 外文会议>International Conference on Medical Image Computing and Computer-Assisted Intervention >Learning 3D Features with 2D CNNs via Surface Projection for CT Volume Segmentation
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Learning 3D Features with 2D CNNs via Surface Projection for CT Volume Segmentation

机译：使用2D CNNS学习3D功能，通过表面投影进行CT卷分割

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摘要

3D features are desired in nature for segmenting CT volumes. It is, however, computationally expensive to employ a 3D convolutional neural network (CNN) to learn 3D features. Existing methods hence learn 3D features by still relying on 2D CNNs while attempting to consider more 2D slices, but up until now it is difficulty for them to consider the whole volumetric data, resulting in information loss and performance degradation. In this paper, we propose a simple and effective technique that allows a 2D CNN to learn 3D features for segmenting CT volumes. Our key insight is that all boundary voxels of a 3D object form a surface that can be represented by using a 2D matrix, and therefore they can be perfectly recognized by a 2D CNN in theory. We hence learn 3D features for recognizing these boundary voxels by learning the projection distance between a set of prescribed spherical surfaces and the object's surface, which can be readily performed by a 2D CNN. By doing so, we can consider the whole volumetric data when spherical surfaces are sampled sufficiently dense, without any information loss. We assessed the proposed method on a publicly available dataset. The experimental evidence shows that the proposed method is effective, outperforming existing methods.
机译：用于分割CT卷的性质本质上需要3D功能。然而，使用3D卷积神经网络（CNN）来学习3D特征是计算昂贵的。因此，现有方法通过依赖于2D CNN来依赖于2D CNN来考虑更多2D切片，但是直到现在，它们难以考虑整个容积数据，导致信息丢失和性能下降。在本文中，我们提出了一种简单有效的技术，允许2D CNN学习用于分割CT卷的3D特征。我们的关键洞察力是3D对象的所有边界体素形成可以通过使用2D矩阵表示的表面，因此它们可以理论上的2D CNN完全识别。因此，我们通过学习一组规定的球面和物体表面之间的投影距离来学习3D特征来识别这些边界体素，这可以通过2D CNN容易地进行。通过这样做，我们可以考虑球面表面被采样充分密集的全部体积数据，而无需任何信息丢失。我们在公开的数据集中评估了所提出的方法。实验证据表明，该方法是有效的，现有方法表现出。

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