Lung segmentation in chest X-ray image using multi-interaction feature fusion network

Lung segmentation is an essential step in a computer-aided diagnosis system for chest radiographs. The lung parenchyma is first segmented in pulmonary computer-aided diagnosis systems to remove the interference of non-lung regions while increasing the effectiveness of the subsequent work. Nevertheless, most medical image segmentation methods nowadays use U-Net and its variants. These variant networks perform poorly in segmentation to detect smaller structures and cannot accurately segment boundary regions. A multi-interaction feature fusion network model based on Kiu-Net is presented in this paper to address this problem. Specifically, U-Net and Ki-Net are first utilized to extract high-level and detailed features of chest images, respectively. Then, cross-residual fusion modules are employed in the network encoding stage to obtain complementary features from these two networks. Second, the global information module is introduced to guarantee the segmented region's integrity. Finally, in the network decoding stage, the multi-interaction module is presented, which allows to interact with multiple kinds of information, such as global contextual information, branching features, and fused features, to obtain more practical information. The performance of the proposed model was assessed on both the Montgomery County (MC) and Shenzhen datasets, demonstrating its superiority over existing methods according to the experimental results. Most current medical image segmentation methods use U-Net and its variants. However, these networks are not good at detecting the segmentation of smaller structures and cannot accurately segment the boundary region. To solve this problem, this paper proposes a multi-interaction feature fusion network model based on Kiu-Net, which can be used to achieve high-precision segmentation of chest radiograph lung region.image

Automated summary

This paper presents a multi-interaction feature fusion network model based on Kiu-Net for lung segmentation in chest CT images, aiming to address the limitations of current methods using U-Net in detecting small structures and accurately segmenting boundaries. The experimental results demonstrate the superiority of this model over existing methods.