Objective

To propose a fully automated algorithm, which is implemented to segment subcutaneous adipose tissue (SAT) and internal adipose tissue (IAT) from the total adipose tissue for whole-body fat distribution analysis using proton density fat fraction (PDFF) magnetic resonance images.

Materials and methods

Adipose tissue segmentation was implemented using the U-Net deep neural network model. All datasets were collected using a 3.0 T magnetic resonance imaging (MRI) scanner for whole-body scan of 20 volunteers covering from neck to knee with about 160 images for each volunteer. PDFF images were reconstructed based on chemical-shift-encoded fat–water imaging. After selecting the representative PDFF images (total 906 images), the manual labeling of the SAT area was used for model training (504 images), validation (168 images), and testing (234 images).

Results

The automatic segmentation model was validated through three indices using the validation and test sets. The dice similarity coefficient, precision rate, and recall rate were 0.976 ± 0.048, 0.978 ± 0.048, and 0.978 ± 0.050, respectively, in both validation and test sets.

Conclusion

The proposed algorithm can reliably and automatically segment SAT and IAT from whole-body MRI PDFF images. The proposed method provides a simple and automatic tool for whole-body fat distribution analysis to explore the relationship between fat deposition and metabolic-related chronic diseases.

中文翻译：

---

基于机器学习的磁共振脂肪分数图像全身脂肪组织自动分割

客观的

提出一种全自动算法，该算法用于从总脂肪组织中分割皮下脂肪组织 (SAT) 和内部脂肪组织 (IAT)，以使用质子密度脂肪分数 (PDFF) 磁共振图像进行全身脂肪分布分析。

材料和方法

脂肪组织分割是使用 U-Net 深度神经网络模型实现的。使用 3.0 T 磁共振成像 (MRI) 扫描仪收集所有数据集，对 20 名志愿者从颈部到膝盖进行全身扫描，每个志愿者大约有 160 张图像。基于化学位移编码的脂肪-水成像重建 PDFF 图像。选取具有代表性的 PDFF 图像（共 906 幅图像）后，对 SAT 区域进行人工标注，用于模型训练（504 幅图像）、验证（168 幅图像）和测试（234 幅图像）。

结果

使用验证集和测试集通过三个指标验证了自动分割模型。在验证集和测试集中，骰子相似系数、准确率和召回率分别为 0.976 ± 0.048、0.978 ± 0.048 和 0.978 ± 0.050。

结论

所提出的算法可以从全身 MRI PDFF 图像中可靠地自动分割 SAT 和 IAT。所提出的方法为全身脂肪分布分析提供了一种简单、自动化的工具，以探索脂肪沉积与代谢相关慢性疾病之间的关系。