CFANet: Context fusing attentional network for preoperative CT image segmentation in robotic surgery.
Comput Biol Med
; 171: 108115, 2024 Mar.
Article
en En
| MEDLINE
| ID: mdl-38402837
ABSTRACT
Accurate segmentation of CT images is crucial for clinical diagnosis and preoperative evaluation of robotic surgery, but challenges arise from fuzzy boundaries and small-sized targets. In response, a novel 2D segmentation network named Context Fusing Attentional Network (CFANet) is proposed. CFANet incorporates three key modules to address these challenges, namely pyramid fusing module (PFM), parallel dilated convolution module (PDCM) and scale attention module (SAM). Integration of these modules into the encoder-decoder structure enables effective utilization of multi-level and multi-scale features. Compared with advanced segmentation method, the Dice score improved by 2.14% on the dataset of liver tumor. This improvement is expected to have a positive impact on the preoperative evaluation of robotic surgery and to support clinical diagnosis, especially in early tumor detection.
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Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Procedimientos Quirúrgicos Robotizados
/
Neoplasias Hepáticas
Límite:
Humans
Idioma:
En
Revista:
Comput Biol Med
Año:
2024
Tipo del documento:
Article
País de afiliación:
China