Predicting Lung Nodule Growth with Shape Transformation and Texture Learning
MA Li
HUANG Dehuang
WANG Yanfang
Abstract：While artificial intelligence has achieved considerable maturity in lung nodule detection,research on growth prediction remains limited.Accurate growth prediction aids clinical decision-making,informing patient follow-up strategies.This paper proposes a novel nodule growth prediction network model that generates high-quality lung nodule images at specific time intervals.The model employs a two-branch structure for feature extraction.One branch,leveraging a displacement field prediction mechanism,models the shape transformation of pulmonary nodules through voxel-level future displacement estimation.The other branch,empowered by a three-dimensional U-Net,focused on learning texture changes within the nodules.A coordinate attention mechanism that emphasizes informative features within the extracted high-dimensional feature map.Subsequently,the outputs of both branches are fused and fed into the feature reconstruction module to generate the final lung nodule growth prediction image.Furthermore,a time interval coding module is introduced to incorporate the desired time interval into the network,enabling the generation of prediction images for different future time points.
Keywords：lung nodulesgrowth predictiondisplacement fieldtime interval coding
Publication Date：2024-05-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 317-324 )