Recently, developing more accurate and more efficient deep learning algorithms for medical images segmentation attracts more and more attentions of researchers. Most of methods increase the depth of the network to replace with acquiring multi-information. The costs of training images annotation are too expensive to label by hand. In this paper, we propose a multi-scale and better performance deep architecture for medical image segmentation, named Res2-UNeXt. Our architecture is an encoder-decoder network with Res2XBlocks. The Res2XBlocks aim at acquiring multi-scale information better in images. To cooperate with Res2-UNeXt, we put forward a simple and efficient method of data augmentation. The data augmentation method, based on the process of cell movement and deformation, has biological implications in away. We evaluate Res2-UNeXt in comparison with recent variants of U-Net: UNet++, CE-Net and LadderNet and the method that different from U-Net architecture: FCN and DFANet on the dataset of ISBI cell tracking challenge 2019 via four different cell images. The experimental results demonstrate that Res2-UNeXt can achieve better performance than both recent variants of U-Net and non-U-Net architecture methods. Besides, the proposed architecture and the data augmentation method have been proven efficiently by the ablation experiments.

最近，开发用于医学图像分割的更准确，更高效的深度学习算法吸引了越来越多的研究人员关注。大多数方法会增加网络的深度，以代替获取多信息。训练图像注释的成本太高，无法手工标记。在本文中，我们提出了一种用于医学图像分割的多尺度，性能更好的深度架构，名为Res2-UNeXt。我们的体系结构是带有Res2XBlocks的编码器-解码器网络。Res2XBlocks旨在更好地获取图像中的多尺度信息。为了与Res2-UNeXt合作，我们提出了一种简单有效的数据扩充方法。基于细胞运动和变形过程的数据增强方法已经具有生物学意义。我们将Res2-UNeXt与U-Net的最新变体进行比较：UNet ++，CE-Net和LadderNet，以及与U-Net体系结构不同的方法：FCN和DFANet，通过四个不同的细胞图像在ISBI细胞跟踪挑战2019的数据集。实验结果表明，Res2-UNeXt可以实现比最近的U-Net和非U-Net体系结构方法都更好的性能。此外，所提出的架构和数据扩充方法已经通过消融实验得到了有效证明。实验结果表明，Res2-UNeXt可以实现比最近的U-Net和非U-Net体系结构方法都更好的性能。此外，所提出的架构和数据扩充方法已经通过消融实验得到了有效证明。实验结果表明，Res2-UNeXt可以实现比最近的U-Net和非U-Net体系结构方法都更好的性能。此外，所提出的架构和数据扩充方法已经通过消融实验得到了有效证明。