DEEP LEARNING-DRIVEN AUTOMATION OF ORTHOPAEDIC LOWER LIMB MEASUREMENTS

Patient-specific angle and distance measurements are essential for evaluating joint alignment in orthopaedic diagnosis, surgical planning, and postoperative follow-up. Traditional methods often rely on 2D standing X-rays, which fail to capture the full 3D dynamics of the lower limb 1 . This limitation has driven interest in 3D measurement techniques on computed tomography (CT). Furthermore, weight-bearing CT (WBCT) enables 3D measurements under physiological loading, a critical factor for alignment measurements 2 . Manual anatomical landmarking to obtain, especially in 3D image annotation, is labour-intensive and prone to error due to the added level of complexity. Automating this process can significantly enhance speed, accuracy, and consistency of 3D measurements. Current state-of-the-art methods combine image segmentation, geometric operations, and statistical shape models but are time-consuming, computationally expensive, and depend on accurate bone segmentations 3 . To address these challenges, we propose a deep learning-based approach to automate anatomical landmarking directly at the image level. Our method employs a cascaded U-Net framework for various lower-limb measurements. In the first experiment, we utilized a dataset of 96 CT scans and 31 WBCT images to automate landmarking for 28 key knee and ankle measurements. In the second experiment, a separate dataset of 77 WBCT foot images, including 22 flatfoot cases, was used to extract 8 critical foot and ankle measurements. Landmark localization accuracy was assessed using Euclidean distance to ground truth landmarks. Average localization errors ranged from 1 mm to 3 mm, aligning with manual error ranges and image resolution. This approach demonstrates fast, reliable, and clinically applicable automation of anatomical landmarking, significantly reducing annotation time. While further fine-tuning is required, deep learning offers enhanced standardization and efficiency, improving decision-making and patient outcomes in orthopaedics.