IEEE Trans Med Imaging. 2018 Nov;37(11):2453-2462. doi: 10.1109/TMI.2018.2835303. Epub 2018 May 10.
Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The U-Net architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. The U-Net architecture consists of standard convolution layers, pooling layers, and upsampling layers. These convolution layers learn representative features of input images and construct segmentations based on the features. However, the features learned by standard convolution layers are not distinctive when the differences among different categories are subtle in terms of intensity, location, shape, and size. In this paper, we propose a novel CNN architecture, called Dense-Res-Inception Net (DRINet), which addresses this challenging problem. The proposed DRINet consists of three blocks, namely a convolutional block with dense connections, a deconvolutional block with residual inception modules, and an unpooling block. Our proposed architecture outperforms the U-Net in three different challenging applications, namely multi-class segmentation of cerebrospinal fluid on brain CT images, multi-organ segmentation on abdominal CT images, and multi-class brain tumor segmentation on MR images.
卷积神经网络 (CNN) 在过去几年中彻底改变了医学图像分析。U-Net 架构是用于语义分割的最知名的 CNN 架构之一，在许多不同的医学图像分割应用中取得了显著的成功。U-Net 架构由标准卷积层、池化层和上采样层组成。这些卷积层学习输入图像的代表性特征，并基于这些特征构建分割。然而，当不同类别之间的差异在强度、位置、形状和大小方面很细微时，标准卷积层学习到的特征并不明显。在本文中，我们提出了一种新的 CNN 架构，称为密集残差 inception 网络 (DRINet)，旨在解决这一具有挑战性的问题。所提出的 DRINet 由三个块组成，即具有密集连接的卷积块、具有残差 inception 模块的反卷积块和上采样块。我们的架构在三个具有挑战性的应用中优于 U-Net，即脑 CT 图像上的脑脊液多类分割、腹部 CT 图像上的多器官分割和 MR 图像上的多类脑肿瘤分割。
