LLNS-Net: Red Neuronal Ligera para Segmentación de Nódulos Pulmonares con Fusión y Complementariedad de Información...

Área de la Ciencia:

• Medical Imaging; Computer Vision; Artificial Intelligence

Sus antecedentes:

• Accurate lung nodule segmentation is crucial for early lung cancer detection.; Existing algorithms struggle to balance high accuracy with lightweight design for clinical application.

Objetivo del estudio:

• To propose LLNS-Net, a compact and effective network for lung nodule segmentation.; To improve the accuracy and efficiency of lung nodule segmentation in CT images.

Principales métodos:

• Developed LLNS-Net featuring a feature-mining encoder with multiscale attention and a feature enhancement module.; Incorporated an enhanced mixed local channel attention (E-MLCA) mechanism and a reinforced multiscale feature module.; Utilized a decoder with subchannel enhancement for refining segmentation maps and improving boundary smoothness.

Principales resultados:

• LLNS-Net achieved improved intersection over union (IoU) compared to HmsUnet (1.86%), MSA-Unet (0.27%), and H-vmunet (7.62%).; The network generated feature maps with smoother boundaries and superior visual quality.; Demonstrated a balance between segmentation accuracy and network compactness.

Conclusiones:

• LLNS-Net presents a promising solution for efficient and accurate lung nodule segmentation.; The proposed architecture enhances feature learning and segmentation refinement for better morphological preservation.; LLNS-Net offers a competitive alternative to existing methods in medical image segmentation.