MCAFNet: multiscale cross-layer attention fusion network for honeycomb lung lesion segmentation

• Published in: Medical & biological engineering & computing Vol. 62; no. 4; pp. 1121 - 1137
• Main Authors: Li, Gang, Xie, Jinjie, Zhang, Ling, Sun, Mengxia, Li, Zhichao, Sun, Yuanjin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024; Springer Nature B.V
• Subjects: Algorithms; Availability; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Computed tomography; Computer Applications; Datasets; Diagnosis; Feature maps; Health services; Human Physiology; Humans; Image processing; Image Processing, Computer-Assisted; Image segmentation; Imaging; Lesions; Lung - diagnostic imaging; Lung diseases; Lung Diseases - diagnostic imaging; Medical diagnosis; Medical imaging; Medical treatment; Modules; Original Article; Radiology; Spatial data; Target recognition; Tomography, X-Ray Computed; Bi-directional attention gate; Cross-layer feature fusion; Multi-scale input; Honeycomb lung segmentation
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-023-02995-9

Accurate segmentation of honeycomb lung lesions from lung CT images plays a crucial role in the diagnosis and treatment of various lung diseases. However, the availability of algorithms for automatic segmentation of honeycomb lung lesions remains limited. In this study, we propose a novel multi-scale cross-layer attention fusion network (MCAFNet) specifically designed for the segmentation of honeycomb lung lesions, taking into account their shape specificity and similarity to surrounding vascular shadows. The MCAFNet incorporates several key modules to enhance the segmentation performance. Firstly, a multiscale aggregation (MIA) module is introduced in the input part to preserve spatial information during downsampling. Secondly, a cross-layer attention fusion (CAF) module is proposed to capture multiscale features by integrating channel information and spatial information from different layers of the feature maps. Lastly, a bidirectional attention gate (BAG) module is constructed within the skip connection to enhance the model’s ability to filter out background information and focus on the segmentation target. Experimental results demonstrate the effectiveness of the proposed MCAFNet. On the honeycomb lung segmentation dataset, the network achieves an Intersection over Union (IoU) of 0.895, mean IoU (mIoU) of 0.921, and mean Dice coefficient (mDice) of 0.949, outperforming existing medical image segmentation algorithms. Furthermore, experiments conducted on additional datasets confirm the generalizability and robustness of the proposed model. The contribution of this study lies in the development of the MCAFNet, which addresses the lack of automated segmentation algorithms for honeycomb lung lesions. The proposed network demonstrates superior performance in accurately segmenting honeycomb lung lesions, thereby facilitating the diagnosis and treatment of lung diseases. This work contributes to the existing literature by presenting a novel approach that effectively combines multi-scale features and attention mechanisms for lung lesion segmentation. The code is available at https://github.com/Oran9er/MCAFNet . Graphical abstract