Prasoon Adhish, Petersen Kersten, Igel Christian, Lauze François, Dam Erik, Nielsen Mads
Department of Computer Science, University of Copenhagen, Denmark.
Biomediq, Denmark.
Med Image Comput Comput Assist Interv. 2013;16(Pt 2):246-53. doi: 10.1007/978-3-642-40763-5_31.
Segmentation of anatomical structures in medical images is often based on a voxel/pixel classification approach. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images that fosters categorization. We propose a novel system for voxel classification integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D image, respectively. We applied our method to the segmentation of tibial cartilage in low field knee MRI scans and tested it on 114 unseen scans. Although our method uses only 2D features at a single scale, it performs better than a state-of-the-art method using 3D multi-scale features. In the latter approach, the features and the classifier have been carefully adapted to the problem at hand. That we were able to get better results by a deep learning architecture that autonomously learns the features from the images is the main insight of this study.
医学图像中解剖结构的分割通常基于体素/像素分类方法。深度学习系统，如卷积神经网络（CNN），可以推断出有助于分类的图像层次表示。我们提出了一种用于体素分类的新型系统，该系统集成了三个二维CNN，它们分别与三维图像的xy、yz和zx平面一一对应。我们将我们的方法应用于低场膝关节MRI扫描中胫骨软骨的分割，并在114次未见过的扫描上进行了测试。尽管我们的方法仅使用单一尺度的二维特征，但它的表现优于使用三维多尺度特征的先进方法。在后一种方法中，特征和分类器已针对手头的问题进行了精心调整。我们能够通过一个从图像中自动学习特征的深度学习架构获得更好的结果，这是本研究的主要见解。
