Brown adipose tissue machine learning nnU-Net V2 network using TriDFusion (3DF) Journal Article
| Authors: | Lafontaine, D.; Chahwan, S.; Barraza, G.; Agridag Ucpinar, B.; Kayal, G.; Gómez-Banoy, N.; Cohen, P.; Humm, J. L.; Schöder, H. |
| Article Title: | Brown adipose tissue machine learning nnU-Net V2 network using TriDFusion (3DF) |
| Abstract: | Background: Recent advances in machine learning have revolutionized medical imaging. Currently, identifying brown adipose tissue (BAT) relies on manual identification and segmentation on Fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scans. However, the process is time-consuming, especially for studies involving a large number of cases, and is subject to bias due to observer dependency. The introduction of machine learning algorithms, such as the PET/CT algorithm implemented in the TriDFusion (3DF) Image Viewer, represents a significant advancement in BAT detection. In the context of cancer care, artificial intelligence (AI)-driven BAT detection holds immense promise for rapid and automatic differentiation between malignant lesions and non-malignant BAT confounds. By leveraging machine learning to discern intricate patterns in imaging data, this study aims to advance the automation of BAT recognition and provide precise quantitative assessment of radiographic features. Results: We used a semi-automatic, threshold-based 3DF workflow to segment 317 PET/CT scans containing BAT. To minimize manual edits, we defined exclusion zones via machine-learning-based CT organ segmentation and used those organ masks to assign each volume of interest (VOI) to its anatomical site. Three physicians then reviewed and corrected all segmentations using the 3DF contour panel. The final, edited masks were used to train an nnU-Net V2 model, which we subsequently applied to 118 independent PET/CT scans. Across all anatomical sites, physicians reviewed the network’s automated segmentations to be approximately 90% accurate. Conclusion: Although nnU-Net V2 effectively identified BAT from PET/CT scans, training an AI model capable of perfect BAT segmentation remains a challenge due to factors such as PET/CT misregistration and the absence of visible BAT activity across contiguous slices. © 2025 Elsevier B.V., All rights reserved. |
| Keywords: | major clinical study; computer assisted tomography; algorithm; quantitative analysis; artificial intelligence; esophagus; trachea; adrenal gland; brown adipose tissue; 18f-fdg pet/ct; orientation; machine learning; bat; human; female; article; network analysis; positron emission tomography-computed tomography; tridfusion (3df); machine learning algorithm; nnu-net; nnu net v2 network |
| Journal Title: | EJNMMI Research |
| Volume: | 15 |
| ISSN: | 2191-219X |
| Publisher: | Springer |
| Date Published: | 2025-08-13 |
| Start Page: | 108 |
| Language: | English |
| DOI: | 10.1186/s13550-025-01303-w |
| PROVIDER: | scopus |
| PMCID: | PMC12350914 |
| PUBMED: | 40804586 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PubMed record and PDF. Corresponding MSK author is Daniel Lafontaine -- Source: Scopus |
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