# A Multi-Branch Hybrid Transformer Networkfor Corneal Endothelial Cell Segmentation

arXiv: https://arxiv.org/abs/2106.07557 · HTML: https://mlsanity.com/paper/2106.07557/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019)
- [Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation](https://mlsanity.com/paper/2003.07853/) (2020)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [Boundary-Aware Feature Propagation for Scene Segmentation](https://mlsanity.com/paper/1909.00179/) (2019)
- [DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation](https://mlsanity.com/paper/1604.02677/) (2016)
- [Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform](https://mlsanity.com/paper/1511.03328/) (2015)
- [Multi-Context Deep Network for Angle-Closure Glaucoma Screening in Anterior Segment OCT](https://mlsanity.com/paper/1809.03239/) (2018)

## Built on by (most cited first, 18 of 18)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer](https://mlsanity.com/paper/2109.04335/) (2021)
- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://mlsanity.com/paper/2204.12406/) (2022)
- [Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images](https://mlsanity.com/paper/2203.02846/) (2022)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation](https://mlsanity.com/paper/2312.15182/) (2023)
- [DATR: Domain-adaptive transformer for multi-domain landmark detection](https://mlsanity.com/paper/2203.06433/) (2022)
- [SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution](https://mlsanity.com/paper/2406.03359/) (2024)
- [SA2-Net: Scale-aware Attention Network for Microscopic Image Segmentation](https://mlsanity.com/paper/2309.16661/) (2023)
- [Trichomonas Vaginalis Segmentation in Microscope Images](https://mlsanity.com/paper/2207.00973/) (2022)
- [ConvTransSeg: A Multi-resolution Convolution-Transformer Network for Medical Image Segmentation](https://mlsanity.com/paper/2210.07072/) (2022)
- [A New Perspective to Boost Vision Transformer for Medical Image Classification](https://mlsanity.com/paper/2301.00989/) (2023)
- [Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopy](https://mlsanity.com/paper/2404.08549/) (2024)
- [MM-UNet: A Mixed MLP Architecture for Improved Ophthalmic Image Segmentation](https://mlsanity.com/paper/2408.08600/) (2024)
- [Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-aware Network](https://mlsanity.com/paper/2305.18865/) (2023)
- [P-MSDiff: Parallel Multi-Scale Diffusion for Remote Sensing Image Segmentation](https://mlsanity.com/paper/2405.20443/) (2024)

## Abstract (arXiv)

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.