CT理论与应用研究2024，Vol.33Issue(3)：317-324,8.DOI:10.15953/j.ctta.2023.167
融合形状变换及纹理学习的肺结节生长预测
Predicting Lung Nodule Growth with Shape Transformation and Texture Learning
摘要
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.关键词
肺结节/生长预测/位移场/时间间隔编码Key words
lung nodules/growth prediction/displacement field/time interval coding分类
数理科学引用本文复制引用
马力,黄德皇,王艳芳..融合形状变换及纹理学习的肺结节生长预测[J].CT理论与应用研究,2024,33(3):317-324,8.基金项目
中山市2019年高端科研机构创新专项(第一批)(基于人工智能CT时序列的肺癌早期预测及其应用). （第一批）
