基于多分支混合Transformer网络的角膜内皮细胞分割
A Multi-Branch Hybrid Transformer Networkfor Corneal Endothelial Cell Segmentation
摘要：
角膜内皮细胞分割在定量临床指标如细胞密度、变异系数和六边形中起着至关重要的作用。然而，角膜内皮的不均匀反射和受试者的震颤和运动导致图像中细胞边缘模糊，难以分割，需要更多的细节和上下文信息来解决这个问题。由于局部卷积和连续下采样的感受野有限，现有的深度学习分割方法不能充分利用全局上下文，遗漏了很多细节。提出了一种基于Transformer和体边支路的多支路混合变压器网络（MBT-Net）。首先，利用卷积块进行局部纹理特征提取，并通过Transformer和残差连接建立空间、通道和层上的长程依赖关系。此外，我们使用身体边缘分支来提高局部一致性和提供边缘位置信息。在自采集数据集TM-EM 3000和公共Alisarine数据集上，与其他SOTA方法相比，该方法取得了一定的改进。
英文摘要：
Corneal endothelial cell segmentation plays a vital role inquantifying clinical indicators such as cell density, coefficient of variation,and hexagonality. However, the corneal endothelium's uneven reflectionand the subject's tremor and movement cause blurred cell edges in theimage, which is difficult to segment, and need more details and contextinformation to release this problem. Due to the limited receptive field oflocal convolution and continuous downsampling, the existing deep learn-ing segmentation methods cannot make full use of global context andmiss many details. This paper proposes a Multi-Branch hybrid Trans-former Network (MBT-Net) based on the transformer and body-edgebranch. Firstly, We use the convolutional block to focus on local tex-ture feature extraction and establish long-range dependencies over space,channel, and layer by the transformer and residual connection. Besides,We use the body-edge branch to promote local consistency and to provideedge position information. On the self-collected dataset TM-EM3000 andpublic Alisarine dataset, compared with other State-Of-The-Art (SOTA)methods, the proposed method achieves an improvement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
