Cargando…

RDAU-Net: Based on a Residual Convolutional Neural Network With DFP and CBAM for Brain Tumor Segmentation

Due to the high heterogeneity of brain tumors, automatic segmentation of brain tumors remains a challenging task. In this paper, we propose RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks and inserting 3D CBAM blocks after skip-connection layers. Moreover, a CBAM with channel a...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Jingjing, Yu, Zishu, Luan, Zhenye, Ren, Jinwen, Zhao, Yanhua, Yu, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924611/
https://www.ncbi.nlm.nih.gov/pubmed/35311076
http://dx.doi.org/10.3389/fonc.2022.805263
Descripción
Sumario:Due to the high heterogeneity of brain tumors, automatic segmentation of brain tumors remains a challenging task. In this paper, we propose RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks and inserting 3D CBAM blocks after skip-connection layers. Moreover, a CBAM with channel attention and spatial attention facilitates the combination of more expressive feature information, thereby leading to more efficient extraction of contextual information from images of various scales. The performance was evaluated on the Multimodal Brain Tumor Segmentation (BraTS) challenge data. Experimental results show that RDAU-Net achieves state-of-the-art performance. The Dice coefficient for WT on the BraTS 2019 dataset exceeded the baseline value by 9.2%.