ISSN 1004-4140
CN 11-3017/P
| Citation: |
MA L, HUANG D H, WANG Y F. Predicting Lung Nodule Growth with Shape Transformation and Texture Learning[J]. CT Theory and Applications, 2024, 33(3): 317-324. DOI: 10.15953/j.ctta.2023.167. (in Chinese).
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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.
| [1] |
OUDKERK M, LIU S, HEUVELMANS M A, et al. Lung cancer LDCT screening and mortality reduction: Evidence, pitfalls and future perspectives[J]. Nature Reviews (Clinical Oncology), 2021, 18(3): 135−151. DOI: 10.1038/s41571-020-00432-6.
|
| [2] |
SHEN W, ZHOU M, YANG F, et al. Multi-scale Convolutional Neural Networks for lung nodule Classication[C]//Information Processing in Medical Imaging: 24th International Conference, UK: Springer, 2015: 588-599.
|
| [3] |
SHEN W, ZHOU M, YANG F, et al. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification[J]. Pattern Recognition, 2017, 61: 663−673. DOI: 10.1016/j.patcog.2016.05.029.
|
| [4] |
ZHANG L, LU L, SUMMERS R M, et al. Convolutional invasion and expansion networks for tumor growth prediction[J]. IEEE Transactions on Medical Imaging, 2018, 37(2): 638-648.
|
| [5] |
RAFAEL-PALOU X, AUBANELL A, BONAVITA I, et al. Re-identification and growth detection of pulmonary nodules without image registration using 3D siamese neural networks[J]. Medical Image Analysis, 2021, 67: 101823. DOI: 10.1016/j.media.2020.101823.
|
| [6] |
SHENG J, LI Y, CAO G, et al. Modeling nodule growth via spatial transformation for follow-up prediction and diagnosis[C]//2021 International Joint Conference on Neural Networks (IJCNN). Shenzhen: IEEE, 2021: 1-7.
|
| [7] |
LI Y, YANG J, XU Y, et al. Learning tumor growth via follow-up volume prediction for lung nodules[C]//Proceedings of the 23th International Conference on Medical Image Computing and Computer-assisted Intervention. Peru: Springer, 2020: 508-517.
|
| [8] |
RONNEBERGER O, FISCHER P, BROX T. U-Net: Convolutional networks for biomedical image segmentation[C]//Proceedings of the 18th International Conference on Medical Image Computing and Computer-assisted Intervention. Munich: Springer, 2015: 234-241.
|
| [9] |
BALAKRISHNAN G, ZHAO A, SABUNCU M R, et al. VoxelMorph: A learning framework for deformable medical image registration[J]. IEEE Transactions on Medical Imaging, 2019: 1788-1800. DOI: 10.1109/TMI.2019.2897538.
|
| [10] |
HU J, SHEN L, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011−2023. DOI: 10.1109/TPAMI.2019.2913372.
|
| [11] |
唐秉航, 王艳芳, 马力, 等. 基于混合注意力机制的肺结节假阳性降低[J]. CT理论与应用研究, 2022, 31(1): 63−72. DOI: 10.15953/j.ctta.2021.002.
TANG B H, WANG Y F, MA L, et al. False positive reduction of pulmonary nodules based on mixed attentional mechanism[J]. CT Theory and Applications, 2022, 31(1): 63−72. DOI: 10.15953/j.ctta.2021.002. (in Chinese).
|
| [12] |
HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 770-778.
|
| [13] |
WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: From error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600−612. DOI: 10.1109/TIP.2003.819861.
|
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