# HTN: Hybrid Transformer Network for Curvature of Cervical Spine Estimation
## 1 Introduction  
Cervical spondylis is one of the top ten chronic diseases in the world, which can trigger neck pain, arm pain, numbness, and limited neck movement, seriously affecting people's lives and work [1]. In previous decades, it more frequently occurred in middle-aged and elderly people. However, with the popularization of electronic products and long-term poor sitting posture for desk-work and study, the mean age of onset has shown a younger trend in recent years. Therefore, analysis of the cervical spine has become a hotspot in medical-image processing. Abnormal cervical spine curvature is often the first imaging feature of cervical spine degeneration. Therefore, cervical spinal curvature is a widespread clinical method for assessing the state of the cervical spine [2]. Accurate measurement and analysis of cervical spine curvature can provide an essential reference and basis for the early diagnosis of cervical spondylis. They also play a crucial role in the preoperative evaluation of cervical spine biomechanics research. The Cobb angle method is common for assessing cervical spinal curvature because it is easier to perform and has better reliability within, and between, groups than other methods. Presently, clinicians still manually measure the Cobb angle or use specific computer-assisted tools and software to perform measurements [3, 4, 5]. However, these methods are not automatic since observers need to draw lines or landmarks on online X-rays manually. These methods are also tedious and time-consuming, and errors caused by subjective factors may occur [6]. To implement automated spine image analysis, a growing body of research has employed deep-learning technology to process images for diagnosis automatically. Recently, some pioneers [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16] have applied landmark detection methods to the field of spinal images. MVC-Net [16] creatively designed multi-view convolution layers to extract global spinal information by aggregating multi-view features from both AP and LAT X-rays. Based on this idea, MVE-Net [7] proposed an error-controlled loss function to speed up convergence and achieve high accuracy. AEC-Net [8] used a large convolutional kernel to learn the boundary features of spines, which a landmark detector then used to regress landmarks. [PERSON] et al. [17] introduced the residual corrector component to correct the location and landmark estimations. Spatial Configuration Net [11] generated the location of the target landmark by multiplying the local appearance and spatial configuration. Nevertheless, existing studies [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16] on the Cobb angle only use the symmetric mean absolute percentage error (SMAPE) as the evaluation index. SMAPE is the officially announced evaluation method for this dataset. However, it is not sufficient from a clinical point of view. The value of SMAPE is not intuitive compared with the angle. We propose more performance metrics in our work to evaluate the model's performance, such as mean absolute difference, Bland-Altman difference diagrams, and Pearson correlation coefficient. In addition, CNNs have been widely used for various segmentation-related tasks and achieved encouraging performance in medical-image segmentation in the past decade. We have witnessed the remarkable success of fully convolutional networks [18], U-Net [19], and their variants [20]. Both architectures benefit from the encoder-decoder structure. However, they fail to build long-range dependencies and global contexts in images; this impacts the further improvement of segmentation accuracy due to the limitation of the receptive field in convolution operation and the inherent inductive biases presented in the convolutional architectures. Generally, a transformer can solve the problem of global dependencies. Although the transformer was originally designed for sequence-to-sequence modeling in natural language processing (NLP) models, now it is generally considered a more flexible alternative to CNNs for processing various vision tasks.