ArticleEJNMMI research2025
Brown adipose tissue machine learning nnU-Net V2 network using TriDFusion (3DF).
Article in EJNMMI research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundRecent 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.
resultsWe 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.
conclusionAlthough 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.