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.

The efficient and automatic detection of chest abnormalities is vital for the auxiliary diagnosis of medical images. Many studies utilize computer vision and deep learning approaches involving symmetry and asymmetry concepts to detect chest abnormalities, and achieve promising findings. However, an accurate instance-level and multi-label detection of abnormalities in chest X-rays remains a significant challenge. Here, a novel anomaly detection method for symmetric chest X-rays using dual-attention and multi-scale feature fusion is proposed. Three aspects of our method should be noted in comparison with the previous approaches. We improved the deep neural network with channel-dimensional and spatial-dimensional attention to capture the abundant contextual features. We then used an optimized multi-scale learning framework for feature fusion to adapt to the scale variation in the abnormalities. Considering the influence of the data imbalance and other factors, we introduced a seesaw loss function to flexibly adjust the sample weights and enhance the model learning efficiency. The rigorous experimental evaluation of a public chest X-ray dataset with fourteen different types of abnormalities demonstrates that our model has a mean average precision of 0.362 and outperforms existing methods.