# Lenke Classification of Scoliosis Based on Segmentation Network and Adaptive Shape Descriptor
###### Abstract
The second is Cobb angle measurement or spinal curvature estimation with geometric calculation, which is generally based on the segmentation results [20]. [PERSON] et al. [21] proposed a novel method to compute the Cobb angles monitoring the connection relationships among the segmented vertebrae in the X-ray images. [PERSON] et al. [22] proposed a spinal curvature estimation method built on top of the segmentation model. [PERSON] et al. [23] developed a spine curve assessment method by using a confidence map and vertebral tilt field, which also provided local and global spine structural information. Although these methods provide an excellent means to automatically compute the Cobb angle, there is still room for improvement as they heavily rely on the segmentation results. However, accu  
Figure 1: The normal spine and six Lenke types of scoliosis: (**a**) Lenke 1; (**b**) Lenke 2; (**c**) Lenke 3; (**d**) Lenke 4; (**e**) Lenke 5; (**f**) Lenke 6; and (**g**) normal. rately segmenting verteb is extremely difficult owing to the unclear vertebral boundary in the X-ray images. In addition, these methods are difficult to directly apply to the Lenke classification of scoliosis, as they have high requirements for the Cobb angle accuracy and need more additional features. The third is feature representation for spinal images, which are beneficial for evaluating scoliosis severity. These features include the shape, texture, spatial relationship, and others. For example, [PERSON] et al. [24] proposed a label method for the cervical, thoracic, and lumbar vertebrae using both texture and inter vertebral spatial information. [PERSON] et al. [25] proposed to describe the spine using curve fitting, with the parameters of the fitted curve used for shape classification. Recently, some work has focused on the classifying scoliosis using the above visual techniques. For example, [PERSON] et al. [26] proposed a classification scheme of mild AIS by using the bending asymmetry index (BAI) based on 3D ultrasound imaging. In [27], another Lenke classification system based on BAI and cobb angle measurement was designed and achieved promising performance in X-ray images. However, in the above method, the BAI calculations were semi-automatic and relied on manual annotation. [PERSON] et al. [28] proposed an effective method for the cluster analysis of Lenke types based on spinal and torso shape representation, but only Lenke type 1 was involved in this study. [PERSON] et al. [29] designed a semi-automatic classification framework for 3D surface trunk data by analyzing the asymmetry distance of the complete trunk as a predictor for scoliosis severity. [PERSON] et al. [30] proposed a computerized Lenke classification approach based on Cobb angle measurement, which required the user to click the mouse to locate the vertebrae. To sum up, although various approaches have been proposed for classifying scoliosis with spine images, these methods still face many problems. First, most methods mainly provide some characteristics of the scoliosis as the output, such as the Cobb angle and most tilted vertebra [31; 32]. Some recent work has focused on complete classification systems, but most are semi-automatic or rely on three-dimensional data; a fully automatic Lenke classification system with only X-ray images is rarely reported. In addition, the accuracy of Lenke classification greatly depends on the segmentation performance, and the design and selection of segmentation networks is challenging. Finally, the visual feature representation of scoliosis is another issue that must be carefully considered, as scoliosis has visually similar shapes and is difficult to distinguish in X-ray images. Therefore, it would be necessary to develop a complete automatic Lenke classification system that provides an accurate and efficient Lenke diagnosis in a visually interpretable manner. To this end, we propose an automatic Lenke classification algorithm for scoliosis based on a segmentation network and an adaptive shape descriptor.