Lumbar Spine

Research on the lumbar spine focuses on developing automated methods for accurate segmentation and analysis of anatomical structures from various imaging modalities (MRI, X-ray, ultrasound). Current efforts leverage deep learning architectures, including U-Net variations, Transformers, and object detection models like YOLO and Faster R-CNN, to improve the speed and accuracy of tasks such as vertebrae identification, intervertebral disc assessment, and spinal curvature measurement. These advancements aim to streamline clinical workflows, improve diagnostic accuracy for conditions like spinal stenosis and ankylosing spondylitis, and ultimately enhance patient care by providing more efficient and reliable image analysis. The creation and sharing of large, annotated datasets are also crucial for advancing this field.