UANV: UNet-based attention network for thoracolumbar vertebral compression fracture angle measurement.
Authors
Affiliations (6)
Affiliations (6)
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
- Department of Neurosurgery, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
- Department of Computer Science and Engineering, Soonchunhyang University, Asan, Republic of Korea. [email protected].
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea. [email protected].
- Department of Neurosurgery, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea. [email protected].
Abstract
Kyphosis is a prevalent spinal condition where the spine curves in the sagittal plane, resulting in spine deformities. Curvature estimation provides a powerful index to assess the deformation severity of scoliosis. In current clinical diagnosis, the standard curvature estimation method for quantitatively assessing the curvature is performed by measuring the vertebral angle, which is the angle between two lines, drawn perpendicular to the upper and lower endplates of the involved vertebra. However, manual Cobb angle measurement requires considerable time and effort, along with associated problems such as interobserver and intraobserver variations. Hence, in this study, we propose UNet-based Attention Network for Thoracolumbar Vertebral Compression Fracture Angle (UANV), a vertebra angle measuring model using lateral spinal X-ray based on a deep convolutional neural network (CNN). Specifically, we considered the detailed shape of each vertebral body with an attention mechanism and then recorded each edge of each vertebra to calculate vertebrae angles.