Cai Sijing, Wu Yi, Chen Guannan
Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, China.
School of Electronic & Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, China.
Front Aging Neurosci. 2022 Mar 10;14:841297. doi: 10.3389/fnagi.2022.841297. eCollection 2022.
Medical image segmentation is of important support for clinical medical applications. As most of the current medical image segmentation models are limited in the U-shaped structure, to some extent the deep convolutional neural network (CNN) structure design is hard to be accomplished. The design in this study mimics the way the wave is elastomeric propagating, extending the structure from both the horizontal and spatial dimensions for realizing the Elastomeric UNet (EUNet) structure. The EUNet can be divided into two types: horizontal EUNet and spatial EUNet, based on the propagation direction. The advantages of this design are threefold. First, the training structure can be deepened effectively. Second, the independence brought by each branch (a U-shaped design) makes the flexible design redundancy available. Finally, a horizontal and vertical series-parallel structure helps on feature accumulation and recursion. Researchers can adjust the design according to the requirements to achieve better segmentation performance for the independent structural design. The proposed networks were evaluated on two datasets: a self-built dataset (multi-photon microscopy, MPM) and publicly benchmark retinal datasets (DRIVE). The results of experiments demonstrated that the performance of EUNet outperformed the UNet and its variants.
医学图像分割对临床医学应用具有重要支持作用。由于当前大多数医学图像分割模型都局限于U形结构，在一定程度上深度卷积神经网络（CNN）的结构设计难以实现。本研究的设计模仿了波的弹性传播方式，从水平和空间维度扩展结构以实现弹性U-Net（EUNet）结构。基于传播方向，EUNet可分为两种类型：水平EUNet和空间EUNet。这种设计具有三个优点。首先，可以有效加深训练结构。其次，每个分支（U形设计）带来的独立性使得灵活设计冗余成为可能。最后，水平和垂直的串并联结构有助于特征积累和递归。研究人员可以根据需求调整设计，以实现独立结构设计的更好分割性能。所提出的网络在两个数据集上进行了评估：一个自建数据集（多光子显微镜，MPM）和公开的视网膜基准数据集（DRIVE）。实验结果表明，EUNet的性能优于U-Net及其变体。
