# TTH-Net: Two-Stage Transformer-CNN Hybrid Network for Leaf Vein Segmentation
## 2 Related Work  
In this section, we first summarize the work on leaf vein segmentation and deeply analyze the existing algorithms. Additionally, the applications of two-stage algorithms are also introduced. Finally, the motivations of our work are listed. Leaf vein segmentation. The leaf vein segmentation task is crucial for applications in botany and biology. Many efforts [6; 18; 19; 20; 21; 22; 23] have been deliciated to improve the accuracy and the efficiency. [PERSON] et al. [18] were dedicated to automatically segmenting and analyzing leaf veins in images obtained via different imaging modalities such as microscopy and macro photography. It has designed a powerful and user-friendly image analysis tool called phenoVein, which greatly facilitates the study of plant phenotypes in ecology and genetics research. [PERSON] et al. [19] represented leaf veins as B-splines that contain hierarchical information about the vascular system. He accomplishes structure tracking by considering spatial information. The algorithm samples the previously estimated affine growth rates along the plant veins and perpendicular to them, representing them in physiological coordinates. To efficiently segment leaf vein patterns, [PERSON] et al. [6] designed a user-assisted software tool termed Leaf Extraction and Analysis Framework Graphical User Interface (LEAF GUI) to enhance researchers' understanding of leaf network structures. The LEAF GUI considers the size, position, and connectivity of network veins, as well as the size, shape, and position of surrounding pores. It follows a series of interactive thresholding and cleaning steps to return statistical data and information about the leaf vein network. [PERSON] et al. [20] developed theories for absorption- and phase-contrast X-ray imaging. By utilizing a synchrotron accelerator light source and two commercial X-ray instruments, they successfully segment the major and minor veins. [PERSON] et al. [21] considered leaf vein structure a crucial biological feature and combined it with the Hessian matrix to perform grouped structure segmentation of images. By analyzing the eigenvalues of pixels, the segmentation results based on the characteristics of the leaf vein structure were obtained. [PERSON] et al. [22] proposed a new leaf vein segmentation method that utilizes odd Gabor filters and morphological operations to achieve more precise vein segmentation. Compared to other algorithms, the Odd Gabor filter can provide more precise outputs due to its ability to effectively detect fine fibrous structures present in leaves, such as tertiary vein branches. Recently, with the development of CNN, [PERSON] et al. [14] utilized a convolutional neural network (CNN) to end-to-end segment leaf veins, which is automatic and accurate. While CNNs have significantly improved the automation level and achieved high accuracy in leaf vein segmentation, most current methods still rely on machine learning algorithms. Therefore, there is great potential for utilizing deep learning to further boost performance. Two-stage algorithm. The two-stage algorithms are widely used to solve image processing problems such as Image Super-Resolution [24], single-image reflection removal [25], segmentation task [26; 27], intrusion detection [28], eigenvalues Detection [29], image restoration [30] and image denoising [31; 32]. For example, [PERSON] et al. [24] achieved the optimal balance between accuracy and speed by employing a deep residual design with constrained depth and combining the two-stage structure with the lightweight two-layer PConv residual block design. [PERSON] et al. [25] proposed a novel two-stage network, named the Reflection-Aware Guided Network (RAGNet), for single-image reflection removal tasks. This approach alleviates the limitations of most existing methods in utilizing the results from the previous stage to guide transmission estimation. [PERSON] et al. [26] proposed a two-stage segmentation network to incorporate the global and local image region fitting energies by using popular active contours.