Jørgensen Kasper, Høi-Hansen Frederikke Engel, Loos Ruth J F, Hinge Christian, Andersen Flemming Littrup
Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen, Denmark.
Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.
Diagnostics (Basel). 2024 Dec 11;14(24):2786. doi: 10.3390/diagnostics14242786.
BACKGROUND/OBJECTIVES: Brown 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.
A 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.
The 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.
The 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.
背景/目的：棕色脂肪组织（BAT）在能量消耗和体温调节中起着关键作用，因此在代谢性疾病背景下引起了人们的关注。医学影像中的分割既耗时，又容易受到操作者之间和操作者内部差异的影响。本研究旨在开发一种使用nnU-Net深度学习框架的自动BAT分割方法，该方法集成到TotalSegmentator软件中，并在一大群淋巴瘤患者中评估其性能。
使用5折交叉验证方法，在159例淋巴瘤患者CT扫描的手动标注的BAT区域上训练3D nnU-Net模型。利用这些折创建一个集成模型以提高分割性能。该模型在30例患者的独立队列上进行测试。评估指标包括DICE分数和豪斯多夫距离（HD）。此外，在7107例FDG PET/CT淋巴瘤研究中分析了BAT区域的平均标准化摄取值（SUV），以确定BAT SUV中的模式。
在测试集中，集成模型实现了0.780±0.077的平均DICE分数和29.0±14.6 mm的HD，达到了先进水平，优于单个折模型。自动BAT分割显示，两性之间的BAT SUV存在显著差异，女性的值更高。上午扫描显示的BAT SUV高于下午扫描，并且观察到季节变化，冬季摄取增加。BAT SUV随年龄增长而降低。
所提出的自动BAT分割工具表现出强大的性能，减少了对手动标注的需求。对大量患者队列的分析证实了BAT SUV的已知模式，突出了该方法在更广泛的临床和研究应用中的潜力。
