Abstract
We present a new Adaptive Error Correction Net (AEC-Net) to formulate the estimation of Cobb anges from spinal X-rays as a high-precision regression task. Our AEC-Net introduces two novel innovations. (1) The AEC-Net contains two networks calculating landmarks and Cobb angles separately, which robustly solve the disadvantage of ambiguity in X-rays since these networks focus on more features. It effectively handles the nonlinear relationship between input images and quantitative outputs, while explicitly capturing the intrinsic features of input images. (2) Based on the two estimated angles, the AEC-Net proposed a new loss function to calculate the final Cobb angles. The optimization of the loss function is based on a high-precision calculation method. The deep learning structure is used to complete this optimization, which achieves higher accuracy and efficiency. We validate our method with the spinal X-rays dataset of 581 subjects with signs of scoliosis at varying extents. The proposed method achieves high accuracy and robustness on the Cobb angle estimations. Comparing to the exsiting conventional methods suffering from tremendous variability and low reliability caused by high ambiguity and variability around boundaries of the vertebrae, the AEC-Net obtain Cobb angles accurately and robustly, which indicates its great potential in clinical use. The highly accurate Cobb angles produced by our framework can be used by clinicians for comprehensive scoliosis assessment, and possibly be further extended to other clinical applications.
Highlights
Cobb angles are widely used for scoliosis diagnosis and treatment decisions
Comparing to the exsiting conventional methods suffering from tremendous variability and low reliability caused by high ambiguity and variability around boundaries of the vertebrae, the Adaptive Error Correction Net (AEC-Net) obtain Cobb angles accurately and robustly, which indicates its great potential in clinical use
The highly accurate Cobb angles produced by our framework can be used by clinicians for comprehensive scoliosis assessment, and possibly be further extended to other clinical applications
Summary
Cobb angles are widely used for scoliosis diagnosis and treatment decisions. Scoliosis is a structural, lateral, rotated curvature of the spine, which especially arises in children at or around puberty and leads to disability [1]. Cobb angles derived from a posteroanterior (back to front) X-ray and measured by selecting the most tilted vertebra at the top and bottom of the spine with respect to the horizontal line are typically used to quantify the magnitude of spinal deformities [3]. Conventional landmark-based manual measurement [4], [5] involves the indirect calculation of identifying the vertebrae and measuring angles, which suffers from high inter- and intra-observer variability while being time-. The accuracy of Cobb angles is often affected by many factors such as the selection of vertebrae, the bias of observer, the accuracy of landmark measurement, as well as image quality. These variabilities in measurements can affect diagnosis significantly when assessing scoliosis progression. It is important to provide accurate and robust quantitative measurements for Cobb angles
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