  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.
