To provide an efficient and accurate clinical tool to aid in the assessment of scoliosis.
We propose the S2VR to directly predict the Cobb angles and landmarks of the spine from spinal X-ray images.
Our proposed BoostNet performs direct spinal landmark estimation to achieve fully automatic clinical Adolescent Idiopathic Scoliosis assessment.
The proposed framework extracts image features from spinal X-ray images and outputs coordinates, angles, and landmarks. The regression task predicts the coordinates and Cobb angles from the given input image features.[1]
Fig. 2. The framework of the structured support vector regression (S2VR).[1]
Fig. 3. Our method overcomes huge variations and high ambiguities and achieves high accuracy in landmark detection.[1]
The BoostNet framework consists of three parts:
Fig. 1. Architecture of the BoostNet for landmark based AIS assessment. Relevant features are automatically extracted and any outlier features are removed by the Boost-Layer. A spinal structured multi-output layer is then applied to the output to capture the correlation between spinal landmarks.[2]
Fig. 2. Conceptualized diagram of our BoostLayer module. (a) The presence of outliers in the feature space impedes robust feature embedding. (b) The BoostLayer module detects outlier features based on a statistical properties. We use an orange dashed line to represent the outlier correction stage of the BoostLayer. For the sake of brevity, we did not include the biases and activation function in the diagram. (c) After correcting outliers, the intra-class feature variance is reduced, allowing for a more robust feature embedding.[2]
Fig. 3. Empirical results of our BoostNet algorithm. (a) The landmarks detected by our BoostNet conforms to the spinal shape more closely compared to the ConvNet detections. (b) The BoostNet converges to a much lower error rate compared to the ConvNet.[2]
S2VR was validated on a dataset composed of 439 samples from various subjects. Seventeen vertebrae from the thoracic and lumbar spine were selected for spinal shape characterization. Each vertebra is located by four landmarks with respect to four corners thus resulting in 68 points per spinal image. The leave-one-out cross-validation scheme was employed to evaluate the model's performance.[1]
BoostNet validation was conducted on a dataset consisting of 481 spinal anterior-posterior x-ray images. All images used for training and testing showed signs of scoliosis to varying extents. Seventeen vertebrae from the thoracic and lumbar spine were selected for spinal shape characterization. Each vertebra is located by four landmarks with respect to four corners thus resulting in 68 points per spinal image. The mean squared error and pearson correlation coefficients were employed to evaluate the accuracy of the model's estimations.[2]
1. Haoliang Sun, Xiantong Zhen, Chris Bailey, Parham Rasoulinejad, Yilong Yin, and Shuo Li. Direct Estimation of Spinal Cobb Angles by Structured Multi-Output Regression. The 25th international conference on Information Processing in Medical Imaging (IPMI), Boone, USA, 2017.
2. Hongbo Wu, Chris Bailey, Parham Rasoulinejad, and Shuo Li. Automatic Landmark Estimation for Adolescent Idiopathic Scoliosis Assessment using BoostNet. 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Quebec City, Canada. 2017.