ArticleDiagnostics (Basel, Switzerland)2024
Automated Supraclavicular Brown Adipose Tissue Segmentation in Computed Tomography Using nnU-Net: Integration with TotalSegmentator.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Normal twin PET: personalized generative modeling for confounder correction and anomaly detection in whole-body PET/CT.Scientific reports · 2025Article
- Brown adipose tissue machine learning nnU-Net V2 network using TriDFusion (3DF).EJNMMI research · 2025Article
- Prediction of TP53 mutations across female reproductive system pan-cancers using deep multimodal PET/CT radiogenomics.Frontiers in medicine · 2025Article
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5 authors.
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Abstract
BACKGROUND/
objectivesBrown adipose tissue (BAT) plays a crucial role in energy expenditure and thermoregulation and has thus garnered interest in the context of metabolic diseases. Segmentation in medical imaging is time-consuming and prone to inter- and intra-operator variability. This study aims to develop an automated BAT segmentation method using the nnU-Net deep learning framework, integrated into the TotalSegmentator software, and to evaluate its performance in a large cohort of patients with lymphoma.
methodsA 3D nnU-Net model was trained on the manually annotated BAT regions from 159 lymphoma patients' CT scans, employing a 5-fold cross-validation approach. An ensemble model was created using these folds to enhance segmentation performance. The model was tested on an independent cohort of 30 patients. The evaluation metrics included the DICE score and Hausdorff Distance (HD). Additionally, the mean standardized uptake value (SUV) in the BAT regions was analyzed in 7107 FDG PET/CT lymphoma studies to identify patterns in the BAT SUVs.
resultsThe ensemble model achieved a state-of-the-art average DICE score of 0.780 ± 0.077 and an HD of 29.0 ± 14.6 mm in the test set, outperforming the individual fold models. Automated BAT segmentation revealed significant differences in the BAT SUVs between the sexes, with higher values in women. The morning scans showed a higher BAT SUV compared to the afternoon scans, and seasonal variations were observed, with an increased uptake during the winter. The BAT SUVs decreased with age.
conclusionsThe proposed automated BAT segmentation tool demonstrates robust performance, reducing the need for manual annotation. The analysis of a large patient cohort confirms the known patterns of BAT SUVs, highlighting the method's potential for broader clinical and research applications.
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