利用基于 nnU-Net 的自动分割技术评估磁共振成像扫描中腹部脂肪量和质子密度脂肪率的性别差异

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Arun Somasundaram, Mingming Wu, Anna Reik, Selina Rupp, Jessie Han, Stella Naebauer, Daniela Junker, Lisa Patzelt, Meike Wiechert, Yu Zhao, Daniel Rueckert, Hans Hauner, Christina Holzapfel, Dimitrios C Karampinos
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引用次数: 0

摘要

"刚刚接受 "的论文经过同行评审,已被接受在《放射学》上发表:人工智能》上发表。这篇文章在以最终版本发表之前,还将经过校对、排版和校样审核。请注意,在制作最终校对稿的过程中,可能会发现影响内容的错误。利用定量水-脂肪核磁共振成像的自动多器官分割技术评估了减肥干预期间肥胖症患者腹部器官体积和质子密度脂肪分数(PDFF)的性别特异性。根据定量化学位移编码核磁共振成像,并使用生活方式干预(LION)研究参与者生成的地面实况标签,采用 nnU-Net 架构自动分割腹部器官,包括内脏脂肪组织(VAT)和皮下脂肪组织(SAT)、肝脏、腰肌和竖脊肌。研究人员对 127 名参与者(73 名女性,54 名男性;体重指数为 30-39.9 kg/m2)以及其中 81 名参与者(54 名女性,32 名男性)进行了为期 8 周的配方低热量饮食后,对每个器官的体积和脂肪含量进行了检测。自动分段的骰子得分从 0.91 到 0.97 不等。研究发现,在男性和女性参与者中,VAT 的 PDFF 均低于 SAT。干预前,与男性相比,女性在 SAT(90.6% 对 89.7%,P < .001)和肝脏(8.6% 对 13.3%,P < .001)以及 VAT(76.4% 对 81.3%,P < .001)中表现出更高的 PDFF。这种关系在干预后仍然存在。作为对热量限制的反应,与女性参与者相比,男性参与者的增值脂肪体积明显减少(1.76 升比 0.91 升,P < .001),SAT PDFF 的下降幅度也更高(2.7% 比 1.5%,P < .001)。对定量水-脂肪核磁共振成像数据进行自动身体成分分析为了解肥胖症患者对热量限制和体重减轻的性别特异性代谢反应提供了新的视角。以 CC BY 4.0 许可发布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating Sex-specific Differences in Abdominal Fat Volume and Proton Density Fat Fraction at MRI Using Automated nnU-Net-based Segmentation.

Sex-specific abdominal organ volume and proton density fat fraction (PDFF) in people with obesity during a weight loss intervention was assessed with automated multiorgan segmentation of quantitative water-fat MRI. An nnU-Net architecture was employed for automatic segmentation of abdominal organs, including visceral and subcutaneous adipose tissue, liver, and psoas and erector spinae muscle, based on quantitative chemical shift-encoded MRI and using ground truth labels generated from participants of the Lifestyle Intervention (LION) study. Each organ's volume and fat content were examined in 127 participants (73 female and 54 male participants; body mass index, 30-39.9 kg/m2) and in 81 (54 female and 32 male participants) of these participants after an 8-week formula-based low-calorie diet. Dice scores ranging from 0.91 to 0.97 were achieved for the automatic segmentation. PDFF was found to be lower in visceral adipose tissue compared with subcutaneous adipose tissue in both male and female participants. Before intervention, female participants exhibited higher PDFF in subcutaneous adipose tissue (90.6% vs 89.7%; P < .001) and lower PDFF in liver (8.6% vs 13.3%; P < .001) and visceral adipose tissue (76.4% vs 81.3%; P < .001) compared with male participants. This relation persisted after intervention. As a response to caloric restriction, male participants lost significantly more visceral adipose tissue volume (1.76 L vs 0.91 L; P < .001) and showed a higher decrease in subcutaneous adipose tissue PDFF (2.7% vs 1.5%; P < .001) than female participants. Automated body composition analysis on quantitative water-fat MRI data provides new insights for understanding sex-specific metabolic response to caloric restriction and weight loss in people with obesity. Keywords: Obesity, Chemical Shift-encoded MRI, Abdominal Fat Volume, Proton Density Fat Fraction, nnU-Net ClinicalTrials.gov registration no. NCT04023942 Supplemental material is available for this article. Published under a CC BY 4.0 license.

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来源期刊
CiteScore
16.20
自引率
1.00%
发文量
0
期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
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