ISSN 1004-4140
CN 11-3017/P
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(中英文), xxxx, x(x): 1-8. DOI: 10.15953/j.ctta.2023.167.
引用本文: 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(中英文), xxxx, x(x): 1-8. DOI: 10.15953/j.ctta.2023.167.
MA L, HUANG D H, WANG Y F. Predicting Lung Nodule Growth with Shape Transformation and Texture Learning[J]. Computerized Tomography Theory and Applications, xxxx, x(x): 1-8. DOI: 10.15953/j.ctta.2023.167. (in Chinese).
Citation: MA L, HUANG D H, WANG Y F. Predicting Lung Nodule Growth with Shape Transformation and Texture Learning[J]. Computerized Tomography Theory and Applications, xxxx, x(x): 1-8. DOI: 10.15953/j.ctta.2023.167. (in Chinese).

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: 虽然人工智能在肺结节检测方面已经相当成熟,但对其生长预测的研究仍然有限。准确的生长预测有助于临床决策,为患者随访策略提供信息。本文提出一种新的结节生长预测网络模型,该模型可以在特定时间间隔生成高质量的肺结节图像。模型使用双分支结构对肺结节图像进行特征提取。其中一个分支,利用位移场预测机制,通过体素水平的未来位移估计来学习肺结节的形状转换;另一分支,采用3D U-Net,学习肺结节的纹理变化。随后,对提取的高维特征图通过坐标注意力机制,突出有利的图像特征,再拼接两个分支的结果,输入至特征重建模块得到最终的肺结节生长预测图像。同时,本文引入时间间隔编码模块,将期望的时间间隔纳入网络,从而能够生成不同未来时间点的预测图像。

     

    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.

     

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