PelviNet: A Collaborative Multi-agent Convolutional Network for Enhanced Pelvic Image Registration.
J Imaging Inform Med
; 2024 Sep 09.
Article
en En
| MEDLINE
| ID: mdl-39249582
ABSTRACT
PelviNet introduces a groundbreaking multi-agent convolutional network architecture tailored for enhancing pelvic image registration. This innovative framework leverages shared convolutional layers, enabling synchronized learning among agents and ensuring an exhaustive analysis of intricate 3D pelvic structures. The architecture combines max pooling, parametric ReLU activations, and agent-specific layers to optimize both individual and collective decision-making processes. A communication mechanism efficiently aggregates outputs from these shared layers, enabling agents to make well-informed decisions by harnessing combined intelligence. PelviNet's evaluation centers on both quantitative accuracy metrics and visual representations to elucidate agents' performance in pinpointing optimal landmarks. Empirical results demonstrate PelviNet's superiority over traditional methods, achieving an average image-wise error of 2.8 mm, a subject-wise error of 3.2 mm, and a mean Euclidean distance error of 3.0 mm. These quantitative results highlight the model's efficiency and precision in landmark identification, crucial for medical contexts such as radiation therapy, where exact landmark identification significantly influences treatment outcomes. By reliably identifying critical structures, PelviNet advances pelvic image analysis and offers potential enhancements for broader medical imaging applications, marking a significant step forward in computational healthcare.
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:
Marruecos
Pais de publicación:
Suiza