Colorectal polyp segmentation method based on fusion of transformer and cross-level phase awareness / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 234-243, 2023.
Artigo
em Chinês
| WPRIM
| ID: wpr-981534
ABSTRACT
In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network's ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Fontes de Energia Elétrica
/
Semântica
/
Simulação por Computador
/
Processamento de Imagem Assistida por Computador
/
Pólipos do Colo
Limite:
Humanos
Idioma:
Chinês
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2023
Tipo de documento:
Artigo
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