Jinhang Li
Editorial Notes
The authors have requested minor, non-substantive changes to the Version of Record and, in according with ACM policies, a Corrected Version of Record was published on April 1, 2022. For reference purposes, the VoR may still be accessed via the Supplemental Material section on this page.
ABSTRACT
Clinically, manual measurement of deformity diagnosis is the standard but costly in labor and time. Deep Learning makes lots of progressions in medical image processing. However, for spinal deformity diagnosis, they all directly predict the spinal classification or measure specific cobb angle without considering the clinical demand in surgery and treatment. This research proposed an automatic scoliosis system based on multiple U-Net networks, and it produced the segmentation of vertebral bodies. The in-rules algorithms automatically estimate the clinical measurements in coronal and sagittal spinal deformity from segmentation results. For the calculation of the parameters, the system presents the outcome absolute normalized errors with less than 0.35 in Std, and the Mean is just under 0.33. The main contribution is providing most of the primary clinical parameters, which is the essentials of spinal treatment and surgery customization. In addition, it is necessary to extend the dataset to improve the accuracy of deep learning models and furtherly verify the algorithms.
Supplemental Material
Available for Download
Version of Record for "An Automatic Scoliosis Diagnosis Platform Based on Deep Learning Approach" by Li et al., 2022 4th Asia Pacific Information Technology Conference (APIT '22).
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