ArticleNuclear medicine and molecular imaging2023
Deep Learning-Based Feature Extraction from Whole-Body PET/CT Employing Maximum Intensity Projection Images: Preliminary Results of Lung Cancer Data.
Article in Nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- FUO-PETMamba: a pet maximum-intensity-projection-based artificial intelligence framework for aetiological classification of fever of unknown origin with multicentre validation.European journal of nuclear medicine and molecular imaging · 2026Article
- Optimizing Layer Thickness in Multi-Planar Volume Reconstruction for Distinguishing Invasive Adenocarcinoma from Non-Invasive and Minimally Invasive Lesions in Pulmonary Nodules (≤15 mm): A Comparative Study with Conventional Lung Window Settings.Diagnostics (Basel, Switzerland) · 2026Article
- Performance of multi-planar volume rendering versus maximum intensity projection and minimum intensity projection on pulmonary nodule detection.Quantitative imaging in medicine and surgery · 2026Article
- Multimodal feature distinguishing and deep learning approach to detect lung disease from MRI images.Scientific reports · 2025Article
- Predicting postoperative prognosis in clear cell renal cell carcinoma using a multiphase CT-based deep learning model.Abdominal radiology (New York) · 2025Article
- Deep learning-based measurement of split glomerular filtration rate withEJNMMI physics · 2024Article
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5 authors.
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Abstract
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 [ 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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