Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model.
J Med Syst
; 48(1): 55, 2024 May 23.
Article
en En
| MEDLINE
| ID: mdl-38780820
ABSTRACT
Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https//github.com/Jianningli/ssm .
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Cráneo
/
Redes Neurales de la Computación
Límite:
Humans
Idioma:
En
Revista:
J Med Syst
Año:
2024
Tipo del documento:
Article
País de afiliación:
Alemania