PDF(Pubmed)
Abstract：
UNASSIGNED: Bone 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.
UNASSIGNED: To establish deep learning for high-throughput multi-site BMFF analysis from UKBB MRI data.
UNASSIGNED: We 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.
UNASSIGNED: Model 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.
UNASSIGNED: We 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.
UNASSIGNED: To establish deep learning for high-throughput multi-site BMFF analysis from UKBB MRI data.
UNASSIGNED: We 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.
UNASSIGNED: Model 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.
UNASSIGNED: We 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.
摘要：
骨髓脂肪组织(BMAT)在健康人中代表>10%的脂肪量,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研究已经确定了几种与BMFF相关的疾病，但规模相对较小。因此，人口规模研究具有揭示BMAT真正临床相关性的巨大潜力。英国生物银行（UKBB）正在对100,000名参与者进行MRI检查，为这种进步提供了理想的机会。
■从UKBBMRI数据中建立用于高通量多站点BMFF分析的深度学习。
■我们研究了60-69岁的男性和女性。使用新的轻量级基于注意力的3DU-Net卷积神经网络自动进行骨髓（BM）分割，该网络改进了从大体积数据中分割小结构的方法。使用61-64名受试者的手动分割，训练模型以分割四个感兴趣的BM区域：脊柱（胸椎和腰椎），股骨头,全髋关节和股骨干。使用每个地区另外10-12个数据集对模型进行了测试，并使用729名UKBB参与者的数据集进行了验证。然后对BMFF进行量化，并评估病理生理特征，包括取决于部位和性别的差异以及与年龄的关系，BMI,骨矿物质密度，外周肥胖,和骨质疏松症。
■模型精度匹配或超过常规U网，产生91.2%(脊柱)的骰子得分，94.5%（股骨头），91.2%(全髋关节)和86.6%(股骨干)。一例严重的脊柱侧凸阻止了脊柱的分割，而1例非霍奇金淋巴瘤阻止了脊柱的分割，由于T2信号耗尽，股骨头和全髋关节；然而，成功的分割没有被任何其他病理生理变量破坏.由此产生的BMFF测量结果证实了BMFF和年龄之间的预期关系，性别和骨密度，并确定了新的特定地点和性别特征。
■我们建立了一种新的深度学习方法，用于从大量体积数据中精确分割小结构，允许UKBB中的高通量多站点BMFF测量。我们的发现揭示了新的病理生理学见解，强调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。
■从UKBBMRI数据中建立用于高通量多站点BMFF分析的深度学习。
■我们研究了60-69岁的男性和女性。使用新的轻量级基于注意力的3DU-Net卷积神经网络自动进行骨髓（BM）分割，该网络改进了从大体积数据中分割小结构的方法。使用61-64名受试者的手动分割，训练模型以分割四个感兴趣的BM区域：脊柱（胸椎和腰椎），股骨头,全髋关节和股骨干。使用每个地区另外10-12个数据集对模型进行了测试，并使用729名UKBB参与者的数据集进行了验证。然后对BMFF进行量化，并评估病理生理特征，包括取决于部位和性别的差异以及与年龄的关系，BMI,骨矿物质密度，外周肥胖,和骨质疏松症。
■模型精度匹配或超过常规U网，产生91.2%(脊柱)的骰子得分，94.5%（股骨头），91.2%(全髋关节)和86.6%(股骨干)。一例严重的脊柱侧凸阻止了脊柱的分割，而1例非霍奇金淋巴瘤阻止了脊柱的分割，由于T2信号耗尽，股骨头和全髋关节；然而，成功的分割没有被任何其他病理生理变量破坏.由此产生的BMFF测量结果证实了BMFF和年龄之间的预期关系，性别和骨密度，并确定了新的特定地点和性别特征。
■我们建立了一种新的深度学习方法，用于从大量体积数据中精确分割小结构，允许UKBB中的高通量多站点BMFF测量。我们的发现揭示了新的病理生理学见解，强调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。
