A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon MRI data
A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon MRI data
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使用英国生物银行 Dixon MRI 数据大规模分析骨髓肥胖的新型深度学习方法
DOI：
10.1101/2022.12.06.22283151
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发表时间：
2022
期刊：
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通讯作者：
Morris D
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作者：
Morris D
骨髓脂肪组织（BMAT）代表健康人> 10%的脂肪质量，并且可以通过磁共振成像（MRI）作为骨髓脂肪分数（BMFF）来测量。人类MRI研究已经确定了与BMFF相关的几种疾病，但规模相对较小。因此，人群规模的研究有巨大的潜力来揭示BMAT的真正临床意义。英国生物银行（UKBB）正在进行100，000名参与者的MRI，为这种进步提供了理想的机会。Objective. Objective.Materials and MethodsWe研究了60-69岁的男性和女性。骨髓（BM）分割是使用新的轻量级基于注意力的3D U-Net卷积神经网络自动进行的，该网络改进了从大体积数据中分割小结构的能力。使用来自61-64名受试者的手动分割，训练模型分割四个BM感兴趣区域：脊柱（胸椎和腰椎）、股骨头、全髋关节和股骨干。每个地区使用另外10-12个数据集对模型进行了测试，并使用来自729名UKBB参与者的数据集进行了验证。然后对BMFF进行量化，并评估其病理生理学特征，包括部位和性别依赖性差异以及与年龄、BMI、骨矿物质密度、外周肥胖和骨质疏松症的关系。（股骨头）、91.2%（全髋）和86.6%（股骨干）。1例严重脊柱侧凸患者阻止了脊柱分割，而1例非霍奇金淋巴瘤患者由于T2信号耗尽而阻止了脊柱、股骨头和全髋关节分割;然而，成功分割未被任何其他病理生理学变量破坏。由此产生的BMFF测量证实了BMFF和年龄，性别和骨密度之间的预期关系，并确定了新的网站和性别特异性characteristics.ConclusionsWe已经建立了一个新的深度学习方法，从大体积数据的小结构的准确分割，允许高通量的多站点BMFF测量在UKBB。我们的研究结果揭示了新的病理生理学见解，突出了BMFF作为一种新型临床生物标志物的潜力。在整个UKBB队列中应用我们的方法将有助于揭示BMAT对人类健康和疾病的影响。
BackgroundBone marrow adipose tissue (BMAT) represents > 10% fat mass in healthy humans and can be measured by magnetic resonance imaging (MRI) as the bone marrow fat fraction (BMFF). Human MRI studies have identified several diseases associated with BMFF but have been relatively small scale. Population-scale studies therefore have huge potential to reveal BMAT’s true clinical relevance. The UK Biobank (UKBB) is undertaking MRI of 100,000 participants, providing the ideal opportunity for such advances.ObjectiveTo establish deep learning for high-throughput multi-site BMFF analysis from UKBB MRI data.Materials and methodsWe studied males and females aged 60–69. Bone marrow (BM) segmentation was automated using a new lightweight attention-based 3D U-Net convolutional neural network that improved segmentation of small structures from large volumetric data. Using manual segmentations from 61–64 subjects, the models were trained to segment four BM regions of interest: the spine (thoracic and lumbar vertebrae), femoral head, total hip and femoral diaphysis. Models were tested using a further 10–12 datasets per region and validated using datasets from 729 UKBB participants. BMFF was then quantified and pathophysiological characteristics assessed, including site- and sex-dependent differences and the relationships with age, BMI, bone mineral density, peripheral adiposity, and osteoporosis.ResultsModel accuracy matched or exceeded that for conventional U-Nets, yielding Dice scores of 91.2% (spine), 94.5% (femoral head), 91.2% (total hip) and 86.6% (femoral diaphysis). One case of severe scoliosis prevented segmentation of the spine, while one case of Non-Hodgkin Lymphoma prevented segmentation of the spine, femoral head and total hip because of T2 signal depletion; however, successful segmentation was not disrupted by any other pathophysiological variables. The resulting BMFF measurements confirmed expected relationships between BMFF and age, sex and bone density, and identified new site- and sex-specific characteristics.ConclusionsWe have established a new deep learning method for accurate segmentation of small structures from large volumetric data, allowing high-throughput multi-site BMFF measurement in the UKBB. Our findings reveal new pathophysiological insights, highlighting the potential of BMFF as a novel clinical biomarker. Applying our method across the full UKBB cohort will help to reveal the impact of BMAT on human health and disease.
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4.2
作者：
R. Nudo
通讯作者：
R. Nudo
影响因子：
29
作者：
Cawthorn WP;Scheller EL;Learman BS;Parlee SD;Simon BR;Mori H;Ning X;Bree AJ;Schell B;Broome DT;Soliman SS;DelProposto JL;Lumeng CN;Mitra A;Pandit SV;Gallagher KA;Miller JD;Krishnan V;Hui SK;Bredella MA;Fazeli PK;Klibanski A;Horowitz MC;Rosen CJ;MacDougald OA
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MacDougald OA
影响因子：
6.7
作者：
Kart, Turkay;Fischer, Marc;Gatidis, Sergios
通讯作者：
Gatidis, Sergios
影响因子：
5.8
作者：
Bredella, Miriam A.;Fazeli, Pouneh K.;Klibanski, Anne
通讯作者：
Klibanski, Anne
使用 3D U-Net 高精度提取气管和支气管
DOI：
--
发表时间：
2023
期刊：
影响因子：
--
作者：
小笠 竜哉;黒田 陸斗;河田 佳樹;鈴木 秀宣;松元 祐司;土田 敬明;楠本 昌彦;仁木 登
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仁木 登