Lenke Classification of Scoliosis Based on Segmentation Network and Adaptive Shape Descriptor
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Medical Imaging and Analysis · Scoliosis diagnosis and treatment · Spinal Fractures and Fixation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems, Xiangnan University, Chenzhou 423300, China
School of Computer and Artificial Intelligence, Xiangnan University, Chenzhou 423300, China
Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou 423300, China
School of Physics and Electronic Electrical Engineering, Xiangnan University, Chenzhou 423000, China
Funding
Scientific Research Fund of Hunan Provincial Education Department · Key Research Project of Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems
References (47)
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