Linder A, Eggebrecht T, Linder N, Stange R, Schaudinn A, Blüher M, Denecke T, Busse Harald
Department of Diagnostic and Interventional Radiology, Leipzig University Hospital, Liebigstrasse 20 - Haus 4, 04103, Leipzig, Germany.
Integrated Research and Treatment Center (IFB) Adiposity Diseases, Leipzig University Medical Center, Leipzig, Germany.
Sci Rep. 2025 Mar 18;15(1):9354. doi: 10.1038/s41598-025-87578-4.
Abdominal adipose tissue (AT) amounts are increasingly considered as potential biomarkers for a variety of diseases and clinical questions, for instance, in diabetology, oncology or cardiovascular medicine. Despite the emergence of automated deep-learning methods for tissue quantification, interactive (supervised) segmentation tools will typically be used for model training. In comparison with CT-based approaches, MRI segmentation tools are more complex and less common. This work aims to validate a novel MRI-based tissue volumetry against a reference method in patients with (pre-) obesity. The new tool (segfatMR) was developed under a Matlab-based, open-source software framework and combines fast automatic pre-segmentation followed by manual (expert) corrections where needed. Analyses were performed retrospectively on a subset of clinical research MRI datasets (1.5 T Achieva XR, Philips Healthcare) and involved the segmentation of datasets from 20 patients (10 women/men) aged 25.1-63.1 (mean 48.5) years with BMIs between 28.3 and 58.8 (mean 36.8) kg/m. Two independent expert readers analyzed the abdominopelvic data (30-40 slices, mean 35.8) with segfatMR and a widely used commercial tool (sliceOmatic). Coefficients of determination (R), bias and limits of agreement (Bland Altman) were determined. Segmentation performance (R between methods) was excellent for both readers for SAT (> 0.99) and very high for VAT (around 0.90). The novel method was almost twice as fast as the reference standard - 25 and 19 s/slice (R1 and R2) vs. 40 and 34 s/slice. The presented semiautomatic segmentation tool enables a fast and accurate quantification of whole abdominopelvic adipose tissue volume in obesity studies. Use, adjustments and extensions of the MRI volumetry tool are facilitated by the open-source design on a standard PC.
腹部脂肪组织（AT）的含量越来越被视为多种疾病和临床问题的潜在生物标志物，例如在糖尿病学、肿瘤学或心血管医学领域。尽管出现了用于组织定量的自动化深度学习方法，但交互式（监督）分割工具通常仍将用于模型训练。与基于CT的方法相比，MRI分割工具更为复杂且不太常见。这项工作旨在针对肥胖（前期）患者，对照参考方法验证一种基于MRI的新型组织容积测量法。新工具（segfatMR）是在基于Matlab的开源软件框架下开发的，它结合了快速自动预分割，必要时再进行手动（专家）校正。对临床研究MRI数据集（1.5T Achieva XR，飞利浦医疗保健公司）的一个子集进行了回顾性分析，涉及对20名年龄在25.1 - 63.1岁（平均48.5岁）、BMI在28.3至58.8（平均36.8）kg/m²之间的患者（10名女性/男性）的数据集进行分割。两名独立的专家读者使用segfatMR和一种广泛使用的商业工具（sliceOmatic）分析了腹部盆腔数据（30 - 40层，平均35.8层）。确定了决定系数（R）、偏差和一致性界限（布兰德 - 奥特曼法）。两种方法之间的分割性能（R）对于两位读者来说，皮下脂肪组织（SAT）均极佳（> 0.99），对于内脏脂肪组织（VAT）则非常高（约为0.90）。新方法的速度几乎是参考标准的两倍——分别为25秒/层和19秒/层（R1和R2），而参考标准为40秒/层和34秒/层。所展示的半自动分割工具能够在肥胖研究中快速准确地定量整个腹部盆腔脂肪组织的体积。标准PC上的开源设计便于MRI容积测量工具的使用、调整和扩展。
