Your browser doesn't support javascript.
loading
DGCBG-Net: A dual-branch network with global cross-modal interaction and boundary guidance for tumor segmentation in PET/CT images.
Zou, Ziwei; Zou, Beiji; Kui, Xiaoyan; Chen, Zhi; Li, Yang.
Afiliación
  • Zou Z; School of Computer Science and Engineering, Central South University, No. 932, Lushan South Road, ChangSha, 410083, China.
  • Zou B; School of Computer Science and Engineering, Central South University, No. 932, Lushan South Road, ChangSha, 410083, China.
  • Kui X; School of Computer Science and Engineering, Central South University, No. 932, Lushan South Road, ChangSha, 410083, China. Electronic address: xykui@csu.edu.cn.
  • Chen Z; School of Computer Science and Engineering, Central South University, No. 932, Lushan South Road, ChangSha, 410083, China.
  • Li Y; School of Informatics, Hunan University of Chinese Medicine, No. 300, Xueshi Road, ChangSha, 410208, China.
Comput Methods Programs Biomed ; 250: 108125, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38631130
ABSTRACT
BACKGROUND AND

OBJECTIVES:

Automatic tumor segmentation plays a crucial role in cancer diagnosis and treatment planning. Computed tomography (CT) and positron emission tomography (PET) are extensively employed for their complementary medical information. However, existing methods ignore bilateral cross-modal interaction of global features during feature extraction, and they underutilize multi-stage tumor boundary features.

METHODS:

To address these limitations, we propose a dual-branch tumor segmentation network based on global cross-modal interaction and boundary guidance in PET/CT images (DGCBG-Net). DGCBG-Net consists of 1) a global cross-modal interaction module that extracts global contextual information from PET/CT images and promotes bilateral cross-modal interaction of global feature; 2) a shared multi-path downsampling module that learns complementary features from PET/CT modalities to mitigate the impact of misleading features and decrease the loss of discriminative features during downsampling; 3) a boundary prior-guided branch that extracts potential boundary features from CT images at multiple stages, assisting the semantic segmentation branch in improving the accuracy of tumor boundary segmentation.

RESULTS:

Extensive experiments are conducted on STS and Hecktor 2022 datasets to evaluate the proposed method. The average Dice scores of our DGCB-Net on the two datasets are 80.33% and 79.29%, with average IOU scores of 67.64% and 70.18%. DGCB-Net outperformed the current state-of-the-art methods with a 1.77% higher Dice score and a 2.12% higher IOU score.

CONCLUSIONS:

Extensive experimental results demonstrate that DGCBG-Net outperforms existing segmentation methods, and is competitive to state-of-arts.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada por Tomografía de Emisión de Positrones / Neoplasias Límite: Humans Idioma: En Revista: Comput Methods Programs Biomed Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Irlanda
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada por Tomografía de Emisión de Positrones / Neoplasias Límite: Humans Idioma: En Revista: Comput Methods Programs Biomed Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Irlanda