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Automated 3D Cobb Angle Measurement Using U-Net in CT Images of Preoperative Scoliosis Patients.
Li, Lening; Zhang, Teng; Lin, Fan; Li, Yuting; Wong, Man-Sang.
Afiliación
  • Li L; Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China. lynn2018.li@connect.polyu.hk.
  • Zhang T; School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China.
  • Lin F; Institute for Artificial Intelligence in Medicine, Nanjing University of Information Science and Technology, Nanjing, China.
  • Li Y; Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen, China. foxetfoxet@gmail.com.
  • Wong MS; Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China. foxetfoxet@gmail.com.
J Imaging Inform Med ; 2024 Aug 08.
Article en En | MEDLINE | ID: mdl-39117939
ABSTRACT
To propose a deep learning framework "SpineCurve-net" for automated measuring the 3D Cobb angles from computed tomography (CT) images of presurgical scoliosis patients. A total of 116 scoliosis patients were analyzed, divided into a training set of 89 patients (average age 32.4 ± 24.5 years) and a validation set of 27 patients (average age 17.3 ± 5.8 years). Vertebral identification and curve fitting were achieved through U-net and NURBS-net and resulted in a Non-Uniform Rational B-Spline (NURBS) curve of the spine. The 3D Cobb angles were measured in two ways the predicted 3D Cobb angle (PRED-3D-CA), which is the maximum value in the smoothed angle map derived from the NURBS curve, and the 2D mapping Cobb angle (MAP-2D-CA), which is the maximal angle formed by the tangent vectors along the projected 2D spinal curve. The model segmented spinal masks effectively, capturing easily missed vertebral bodies. Spoke kernel filtering distinguished vertebral regions, centralizing spinal curves. The SpineCurve Network method's Cobb angle (PRED-3D-CA and MAP-2D-CA) measurements correlated strongly with the surgeons' annotated Cobb angle (ground truth, GT) based on 2D radiographs, revealing high Pearson correlation coefficients of 0.983 and 0.934, respectively. This paper proposed an automated technique for calculating the 3D Cobb angle in preoperative scoliosis patients, yielding results that are highly correlated with traditional 2D Cobb angle measurements. Given its capacity to accurately represent the three-dimensional nature of spinal deformities, this method shows potential in aiding physicians to develop more precise surgical strategies in upcoming cases.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Imaging Inform Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Imaging Inform Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza