Convolutional neural networks for medical image segmentation
It provides a tutorial overview for researchers in medical imaging, but is incremental as it synthesizes existing knowledge without novel contributions.
The paper reviews convolutional neural networks (CNNs) for medical image segmentation, discussing architecture, sampling, and historical developments of key models like FCN, U-Net, and DeepMedic, without presenting new experimental results.
In this article, we look into some essential aspects of convolutional neural networks (CNNs) with the focus on medical image segmentation. First, we discuss the CNN architecture, thereby highlighting the spatial origin of the data, voxel-wise classification and the receptive field. Second, we discuss the sampling of input-output pairs, thereby highlighting the interaction between voxel-wise classification, patch size and the receptive field. Finally, we give a historical overview of crucial changes to CNN architectures for classification and segmentation, giving insights in the relation between three pivotal CNN architectures: FCN, U-Net and DeepMedic.