# Lenke Classification of Scoliosis Based on Segmentation Network and Adaptive Shape Descriptor
###### Abstract  
Scoliosis is a common spinal deformity that seriously affects patients' physical and mental health. An accurate Lenke classification is greatly significant for evaluating and treating scoliosis. Currently, the clinical diagnosis mainly relies on manual measurement; however, using computer vision assists with an intelligent diagnosis. Due to the complex rules of Lenke classification and the characteristics of medical imaging, the fully automated Lenke classification of scoliosis remains a considerable challenge. Herein, a novel Lenke classification method for scoliosis using X-rays based on segmentation networks and adaptive shape descriptors is proposed. Three aspects of our method should be noted in comparison with the previous approaches. We used Unet++ to segment the vertebrae and designed a post-processing operation to improve the segmentation effect. Then, we proposed a new shape descriptor to extract the shape features for segmented vertebrae in greater detail. Finally, we proposed a new Lenke classification framework for scoliosis that contains two schemes based on Cobb angle measurement and shape classification, respectively. After rigorous experimental evaluations on a public dataset, our method achieved the best performance and outperformed other sophisticated approaches. Lenke classification; image segmentation; deep learning; scoliosis; shape descriptor; medical image analysis; X-ray +
Footnote †: journal: Medical Imaging and Artificial Intelligence  
an accurate Lenke classification of scoliosis is integral for selecting the treatment modality, especially the surgical strategy. The key steps for the Lenke classification of scoliosis include determining the location of each vertebra, computing the Cobb angle, and extracting the characteristic of the curve. In traditional methods, the key anatomical features of the spine are located and manually measured on the X-ray image using a ruler, protractor, and marker; then, using the Lenke classification criteria, the Lenke type is further determined by professional radiologists. However, manual measurement has many disadvantages, such as being time-consuming and having a large amount of errors, strong subjectivity, and high reliance on physician experience. With the rapid development of artificial intelligence, computer vision application provides a more efficient way of diagnosing spinal diseases. Based on a comprehensive literature survey, three key visual techniques were identified as being involved in the Lenke classification of scoliosis. The first is segmenting the spine image, that is, segmenting each vertebra from the spine image. Unet [5] is a remarkable deep learning model that employs an \"encoder-decoder\" architecture based on fully convolutional networks (FCN) [6]. Since then, numerous variants of Unet, such as UNet++ [7], MAnet [8], RMS-Unet [9], X-Net [10], and MQANet [11], have been developed for use in medical image segmentation and other scenarios. Many researchers have also attempted to improve Unet for spinal image segmentation [12]. For example, [PERSON] et al. [13] proposed an SIUNet with an improvised inception block and new dense skip pathways, which achieved promising performance in spine image segmentation. [PERSON] et al. [14] developed a coarse-to-fine approach for vertebrae localization and segmentation in CT images based on Unet, which achieved the best results in the MICCAI 2019 Vertebrae Segmentation Challenge [15]. Recently, another deep learning method, transformers and their variants, has gained great attention and achieved significant success in image segmentation [16]. Segmenter [17] is a superior transformer model for segmentation that is built on top of the recent vision transformer [18], which achieved state of the art in two scene image datasets. Recently, [PERSON] et al. [19] proposed a new transformer for labeling and segmenting of 3D vertebrae for arbitrary field-of-view CT images, thus demonstrating the great potential of transformer methods in spine image segmentation.