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Dataset
Robotic vs. handheld ultrasound for lumbar spine: a comparative dataset with ground truth CT benchmarking
Musculoskeletal disorders present significant health and economic challenges on a global scale. Current intraoperative imaging techniques, including computed tomography (CT) and radiography, involve high radiation exposure and limited soft tissue visualization. Ultrasound (US) offers a non-invasive, real-time alternative but is highly observer-dependent and underutilized intraoperatively. US enhanced by artificial intelligence shows high potential for observer-independent pattern recognition and robot-assisted applications in orthopedics. Given the limited availability of in-vivo imaging data, we introduce a comprehensive dataset from a comparative collection of handheld US (HUS) and robot-assisted ultrasound (RUS) lumbar spine imaging in 63 healthy volunteers. This dataset includes demographic data, paired CT, HUS, RUS imaging, synchronized tracking data for HUS and RUS, and 3D-CT-segmentations. It establishes a robust baseline for machine learning algorithms by focusing on healthy individuals, circumventing the limitations of simulations and pathological anatomy. To our knowledge, this extensive collection is the first healthy anatomy dataset for the lumbar spine that includes paired CT, HUS, and RUS imaging, supporting advancements in computer- and robotic-assisted diagnostic and intraoperative techniques for musculoskeletal disorders.
Jaar van publicatie:2024
Toegankelijkheid:open
Uitgever:KU Leuven RDR
Licentie:CC-BY-4.0
Formaat:png, txt, csv, stl
Trefwoorden: Ultrasound, computed tomography, musculoskeletal disorders