Maddalo Michele, Zorza Ivan, Zubani Stefano, Nocivelli Giorgio, Calandra Giulio, Soldini Pierantonio, Mascaro Lorella, Maroldi Roberto
Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy; Medical Physics Unit, Spedali Civili Hospital, Brescia, Italy.
Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.
Phys Med. 2017 May;37:24-31. doi: 10.1016/j.ejmp.2017.04.002. Epub 2017 Apr 10.
To demonstrate the accuracy of an unsupervised (fully automated) software for fat segmentation in magnetic resonance imaging. The proposed software is a freeware solution developed in ImageJ that enables the quantification of metabolically different adipose tissues in large cohort studies.
The lumbar part of the abdomen (19cm in craniocaudal direction, centered in L3) of eleven healthy volunteers (age range: 21-46years, BMI range: 21.7-31.6kg/m) was examined in a breath hold on expiration with a GE T1 Dixon sequence. Single-slice and volumetric data were considered for each subject. The results of the visceral and subcutaneous adipose tissue assessments obtained by the unsupervised software were compared to supervised segmentations of reference. The associated statistical analysis included Pearson correlations, Bland-Altman plots and volumetric differences (VD).
Values calculated by the unsupervised software significantly correlated with corresponding supervised segmentations of reference for both subcutaneous adipose tissue - SAT (R=0.9996, p<0.001) and visceral adipose tissue - VAT (R=0.995, p<0.001). Bland-Altman plots showed the absence of systematic errors and a limited spread of the differences. In the single-slice analysis, VD were (1.6±2.9)% for SAT and (4.9±6.9)% for VAT. In the volumetric analysis, VD were (1.3±0.9)% for SAT and (2.9±2.7)% for VAT.
The developed software is capable of segmenting the metabolically different adipose tissues with a high degree of accuracy. This free add-on software for ImageJ can easily have a widespread and enable large-scale population studies regarding the adipose tissue and its related diseases.
验证一款用于磁共振成像脂肪分割的无监督（全自动）软件的准确性。该软件是一款基于ImageJ开发的免费解决方案，可在大规模队列研究中对代谢不同的脂肪组织进行量化。
对11名健康志愿者（年龄范围：21 - 46岁，BMI范围：21.7 - 31.6kg/m²）腹部的腰椎部分（颅尾方向19cm，以L3为中心）进行屏气呼气时的GE T1 Dixon序列检查。每个受试者均考虑单层面和容积数据。将无监督软件获得的内脏和皮下脂肪组织评估结果与参考的有监督分割结果进行比较。相关统计分析包括Pearson相关性、Bland - Altman图和容积差异（VD）。
无监督软件计算的值与皮下脂肪组织（SAT）（R = 0.9996，p < 0.001）和内脏脂肪组织（VAT）（R = 0.995，p < 0.001）的相应参考有监督分割结果显著相关。Bland - Altman图显示无系统误差且差异分布有限。在单层面分析中，SAT的VD为（1.6±2.9）%，VAT的VD为（4.9±6.9）%。在容积分析中，SAT的VD为（1.3±0.9）%，VAT的VD为（2.9±2.7）%。
所开发的软件能够高精度地分割代谢不同的脂肪组织。这款用于ImageJ的免费附加软件能够轻松广泛应用，并有助于开展关于脂肪组织及其相关疾病的大规模人群研究。
