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Updated: Jun 5, 2026

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

DecX-Net: a dual-path feature decoupling network with layer-wise feature alignment for subsolid pulmonary nodule

Shaohua Zheng¹, Yongli Liu¹, Xiao Yang¹

¹College of Physics and Information Engineering, Fuzhou University, Fuzhou, People's Republic of China.

Physics in Medicine and Biology

|June 3, 2026

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This summary is machine-generated.

A new DecX-Net model accurately segments subsolid pulmonary nodules, improving early lung cancer detection. This method enhances component analysis for better clinical risk assessment and decision-making.

Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Subsolid pulmonary nodules are key indicators of early lung cancer detected via low-dose CT screening.
Accurate segmentation of solid and ground-glass components is crucial for malignancy risk assessment.
Existing methods struggle with precise solid component segmentation due to blurred boundaries and class imbalance.

Purpose of the Study:

To develop an advanced network for precise component-level segmentation of subsolid pulmonary nodules.
To address challenges in segmenting solid components within part-solid nodules.
To improve malignancy risk assessment and clinical decision-making in early lung cancer detection.

Main Methods:

Proposed DecX-Net, a dual-path feature decoupling network with layer-wise feature alignment.

Keywords:

dual-path network feature alignment feature decoupling pulmonary nodule segmentation subsolid pulmonary nodules

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A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

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Utilized parallel dual-branch encoders for local texture and global structure capture.

Incorporated cross-branch layer-wise alignment and mutual guidance (CLAMG) and enhanced position attention (E-PAM) modules.

Main Results:

DecX-Net demonstrated superior performance in Recall, Precision, MIoU, and DSC compared to mainstream methods.
The network showed exceptional capability in detecting small solid components.
Evaluation was performed on a multi-center clinical dataset of 463 annotated cases.

Conclusions:

The proposed DecX-Net enables accurate component-level analysis of subsolid pulmonary nodules.
This method has significant potential to enhance clinical risk assessment for early lung cancer.
Improved segmentation accuracy can aid in better treatment planning and prognosis evaluation.