Deep learning models have become instrumental in medical imaging, with a wide array of applications such as automated segmentation and diagnosis. However, the majority of these models are trained on adult imaging datasets, leading to underperformance when applied to the pediatric population. This discrepancy arises from anatomical differences between these populations, posing a significant challenge in fields requiring high precision like spine segmentation in Computed Tomography (CT) scans. Read More

Meeting name: SPR 2025 Annual Meeting , 2025

Authors: De Medeiros Bruno, Gatidis Sergios

Keywords: Computed Tomography, Segmentation, Image Database

Automated segmentation is an important step in automated processing of radiographs. However, manual generation of training data is tedious. To overcome this challenge, we leverage synthetic radiograph training data generated from CT volumes together with corresponding anatomy segmentation labels. We demonstrate the effectiveness of this approach on the use case of automated scoliosis Cobb angle quantification. Read More

Meeting name: SPR 2025 Annual Meeting , 2025

Authors: Vasanawala Sauram, Pauly John, Gatidis Sergios

Keywords: Segmentation, Scoliosis, Deep Learning