Keywords: medical imaging, deep learning, spinopelvic parameters, object detection
TL;DR: Automatic extraction of spinopelvic parameters, provides consistency in annotations and parameter mesurements besides saving time from surgeons.
Abstract: Surgeons measure spinopelvic parameters from X-ray images to evaluate spinopelvic alignment preoperatively for surgical planning. Automatic extraction of these parameters not only saves time but also provides consistent measurements, avoiding human error. In this paper, we introduce a new approach to automatic spinopelvic parameter extraction, which considers landmarks as objects. The landmarks are extracted using a deep learning object detection algorithm that can address the drawbacks of heatmap-based regression. The model is evaluated using two datasets totalling 1000 lateral spinal and pelvic X-ray images. Acceptable accuracy is achieved when comparing the reference manual parameter measurements with those obtained automatically by our prediction model.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Detection and Diagnosis
Secondary Subject Area: Application: Radiology
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