Age-dependent generalizability of lumbar spine detection and segmentation models: a comparative study in pediatric populations (Image Processing Poster Award)

Jemyoung Lee; Changmin Park; Minkyoung Cho; Young Hun Choi; Jong Hyo Kim

doi:10.1117/12.3006168

2 April 2024 Age-dependent generalizability of lumbar spine detection and segmentation models: a comparative study in pediatric populations (Image Processing Poster Award)

Jemyoung Lee, Changmin Park, Minkyoung Cho, Young Hun Choi, Jong Hyo Kim

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292626 (2024) https://doi.org/10.1117/12.3006168
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

This study assessed automated bone density measurement technologies in pediatric groups, focusing on lumbar spine localization and spine segmentation models initially trained on adult data. The research involved three phases: training models using YOLOv5 and U-Net on adult images, adapting these models with pediatric data via transfer learning, and external validation categorized by age to account for anatomical variances. The adult-trained model showed decreased sensitivity in younger ages, with the lowest performance in the youngest group. Conversely, the pediatric-trained model achieved high sensitivity, over 90% in children under 10, and perfect scores in the 10-12 group, demonstrating improved accuracy. Qualitative analysis for segmentation indicated better performance in the pediatric model across all age groups, particularly in those under 13. The study concludes that transfer learning enhances the performance and generalizability of models for pediatric spine analysis, suggesting a potential for more accurate diagnostics.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jemyoung Lee, Changmin Park, Minkyoung Cho, Young Hun Choi, and Jong Hyo Kim "Age-dependent generalizability of lumbar spine detection and segmentation models: a comparative study in pediatric populations (Image Processing Poster Award)", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292626 (2 April 2024); https://doi.org/10.1117/12.3006168

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