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.
■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。