Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation.

ABSTRACT: Manual image segmentation consumes time. An automatic and accurate method to segment multimodal brain tumors using context information rich three-dimensional medical images that can be used for clinical treatment decisions and surgical planning is required. However, it is a challenge to use deep learning to achieve accurate segmentation of medical images due to the diversity of tumors and the complex boundary interactions between sub-regions while limited computing resources hinder the construction of efficient neural networks. We propose a feature fusion module based on a hierarchical decoupling convolution network and an attention mechanism to improve the performance of network segmentation. We replaced the skip connections of U-shaped networks with a feature fusion module to solve the c

SUBMITTER: Shen L 

PROVIDER: S-EPMC10343050 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature