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KMID : 1148920230570050216
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Nuclear Medicine and Molecular Imaging
2023 Volume.57 No. 5 p.216 ~ p.222
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Deep Learning-Based Feature Extraction from Whole-Body PET/CT Employing Maximum Intensity Projection Images: Preliminary Results of Lung Cancer Data
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Gil Joon-Hyung
Choi Hong-Yoon
Paeng Jin-Chul
Cheon Gi-Jeong
Kang Keon-Wook
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Abstract
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Purpose : Deep learning (DL) has been widely used in various medical imaging analyses. Because of the difficulty in processing volume data, it is difficult to train a DL model as an end-to-end approach using PET volume as an input for various purposes including diagnostic classification. We suggest an approach employing two maximum intensity projection (MIP) images generated by whole-body FDG PET volume to employ pre-trained models based on 2-D images.
Methods : As a retrospective, proof-of-concept study, 562 [18F]FDG PET/CT images and clinicopathological factors of lung cancer patients were collected. MIP images of anterior and lateral views were used as inputs, and image features were extracted by a pre-trained convolutional neural network (CNN) model, ResNet-50. The relationship between the images was depicted on a parametric 2-D axes map using t-distributed stochastic neighborhood embedding (t-SNE), with clinicopathological factors.
Results : A DL-based feature map extracted by two MIP images was embedded by t-SNE. According to the visualization of the t-SNE map, PET images were clustered by clinicopathological features. The representative difference between the clusters of PET patterns according to the posture of a patient was visually identified. This map showed a pattern of clustering according to various clinicopathological factors including sex as well as tumor staging.
Conclusion : A 2-D image-based pre-trained model could extract image patterns of whole-body FDG PET volume by using anterior and lateral views of MIP images bypassing the direct use of 3-D PET volume that requires large datasets and resources. We suggest that this approach could be implemented as a backbone model for various applications for whole-body PET image analyses.
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KEYWORD
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Deep learning, PET/CT, Maximum intensity projection, Convolutional neural network, FDG
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