Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Sevgi Gokce Kafali, Shu-Fu Shih, Xinzhou Li, Grace Hyun J Kim, Tristan Kelly, Shilpy Chowdhury, Spencer Loong, Jeremy Moretz, Samuel R Barnes, Zhaoping Li, Holden H Wu
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引用次数: 0

Abstract

Objective: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs.

Materials and methods: 920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC).

Results: ACD 3D U-Net achieved rapid (< 4.8 s/subject) segmentation with high DICE-SAT (median ≥ 0.994) and DICE-VAT (median ≥ 0.976), small FN (median ≤ 0.7%), and FP (median ≤ 1.1%). 3D nnU-Net yielded rapid (< 2.5 s/subject) segmentation with similar DICE-SAT (median ≥ 0.992), DICE-VAT (median ≥ 0.979), FN (median ≤ 1.1%) and FP (median ≤ 1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC > 0.997) in longitudinal analysis.

Discussion: ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.

Abstract Image

利用多对比度输入的三维卷积神经网络在纵向磁共振成像上自动进行腹部脂肪组织分割和体积量化。
目的:皮下和内脏脂肪组织(SAT/VAT)体积的增加与心血管代谢疾病的风险有关。这项工作旨在利用基于注意力的竞争性密集(ACD)三维 U-Net 和三维 nnU-Net 以及全视场容积多对比度输入,开发和评估超重/肥胖症成人纵向 MRI 上的腹部 SAT/VAT 自动分割。第一次扫描分为训练/验证/测试集(n = 646/92/182)。测试集受试者的第二次扫描用于评估纵向分析的通用性。通过测量 Dice 分数(DICE-SAT、DICE-VAT)、假阴性(FN)和假阳性(FP)来评估分割性能。采用类内相关系数(ICC)评估体量一致性:结果:ACD 3D U-Net 在纵向分析中达到了快速(0.997):讨论:ACD 3D U-Net 和 3D nnU-Net 可作为自动化工具,快速、准确、纵向地量化超重/肥胖成人的腹部 SAT/VAT 容积。
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来源期刊
CiteScore
4.60
自引率
0.00%
发文量
58
审稿时长
>12 weeks
期刊介绍: MAGMA is a multidisciplinary international journal devoted to the publication of articles on all aspects of magnetic resonance techniques and their applications in medicine and biology. MAGMA currently publishes research papers, reviews, letters to the editor, and commentaries, six times a year. The subject areas covered by MAGMA include: advances in materials, hardware and software in magnetic resonance technology, new developments and results in research and practical applications of magnetic resonance imaging and spectroscopy related to biology and medicine, study of animal models and intact cells using magnetic resonance, reports of clinical trials on humans and clinical validation of magnetic resonance protocols.
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