Automatic detection of the lumbar spine using transfer learning for Quantitative Fluoroscopy

Quantitative assessment of spinal motion plays a pivotal role in diagnosing and understanding lower back pain. This paper utilises a Convolutional Neural Network for precise landmark localisation of bounding boxes encompassing the lumbar spine in sagittal plane lumbar fuoroscopy image sequences. The proposed methodology aims to automate spinal movement tracking and provide a benchmark for future research, thereby enhancing the efficiency and accuracy of low back pain diagnosis.