OBJECTIVE To provide objective visualization and pattern analysis of neck muscle boundaries to inform and monitor treatment of cervical dystonia. METHODS We recorded transverse cervical ultrasound (US) images and whole-body motion analysis of sixty-one standing participants (35 cervical dystonia, 26 age matched controls). We manually annotated 3,272 US images sampling posture and the functional range of pitch, yaw, and roll head movements. Using previously validated methods, we used 60-fold cross validation to train, validate and test a deep neural network (U-net) to classify pixels to 13 categories (five paired neck muscles, skin, ligamentum nuchae, vertebra). For all participants for their normal standing posture, we segmented US images and classified condition (Dystonia/Control), sex and age (higher/lower) from segment boundaries. We performed an explanatory, visualization analysis of dystonia muscle-boundaries. RESULTS For all segments, agreement with manual labels was Dice Coefficient (64±21%) and Hausdorff Distance (5.7±4 mm). For deep muscle layers, boundaries predicted central injection sites with average precision 94±3%. Using leave-one-out cross-validation, a support-vector-machine classified condition, sex, and age from predicted muscle boundaries at accuracy 70.5%, 67.2%, 52.4% respectively, exceeding classification by manual labels. From muscle boundaries, Dystonia clustered optimally into three sub-groups. These sub-groups are visualized and explained by three eigen-patterns which correlate significantly with truncal and head posture. CONCLUSION Using US, neck muscle shape alone discriminates dystonia from healthy controls. SIGNIFICANCE Using deep learning, US imaging allows online, automated visualization, and diagnostic analysis of cervical dystonia and segmentation of individual muscles for targeted injection. The dataset is available (DOI: 10.23634/MMUDR.00624643).

中文翻译：

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使用超声成像和深度学习对颈肌张力障碍的颈部肌肉边界进行客观分析。

目的提供颈部肌肉边界的客观可视化和模式分析，以告知和监测宫颈肌张力障碍的治疗。方法我们记录了61例站立参与者（35例颈肌张力障碍，26例年龄相匹配的对照组）的横向颈椎超声（US）图像和全身运动分析。我们手动注释了3,272张美国图像的采样姿势以及俯仰，偏航和摇头运动的功能范围。使用先前验证的方法，我们使用60倍交叉验证来训练，验证和测试深层神经网络（U-net），以将像素分为13类（五对成对的颈部肌肉，皮肤，韧带韧带，椎骨）。对于所有正常站立姿势的参与者，我们对美国图像进行了分割，并根据分割边界对状况（Dystonia /对照），性别和年龄（较高/较低）进行了分类。我们对肌张力障碍肌边界进行了解释性的可视化分析。结果对于所有细分市场，与手动标签一致的是骰子系数（64±21％）和Hausdorff距离（5.7±4 mm）。对于深层肌肉，边界可预测中心注射部位，平均精确度为94±3％。使用留一法交叉验证，支持向量机从预测的肌肉边界对状况，性别和年龄进行了分类，准确度分别为70.5％，67.2％，52.4％，超过了人工标签的分类。从肌肉边界来看，肌张力障碍最佳地分为三个亚组。这些子组可以通过三个本征模式进行可视化和解释，这些本征模式与截骨和头部姿势显着相关。结论仅使用US，颈肌的形状就能将肌张力障碍与健康对照区分开。意义通过深度学习，美国成像技术可对宫颈肌张力障碍进行在线，自动可视化和诊断分析，并针对目标注射分割单个肌肉。该数据集可用（DOI：10.23634 / MMUDR.00624643）。