Automatic MRI segmentation for upper limb muscles for clinical applications

摘要 肌肉解剖和结构的磁共振(MR)成像的临床价值受到 肌肉分割所产生的瓶颈。因此，肩袖损伤的肌肉临床评价 在主要为一致性而选择的位置使用单个2D图像来确定损伤和修复潜力 解剖标志；这些方法从根本上是有缺陷的，不能准确地反映两者的总体积 或肩袖肌肉脂肪渗入。因此，有效、准确、快速的三维分割方法 对于将有价值的3D成像信息集成到临床决策中是必不可少的- 制作。我们的长期目标是开发一个可共享的框架，以实现准确、自动的分割 上肢肌肉的磁共振图像，在一个时间尺度上，使图像分析易于临床处理。这个 总体目标是在48个健康人和10个人中利用我们的完全注释的上肢MR图像 用肩袖撕裂为两项研究开发、评估和共享成功的机器学习方法 还有诊所。我们的中心假设是，在我们的数据集上训练的监督方法将会表现得更好 无监督的方法和由此产生的模型可以成功地转移到标准的临床扫描。 我们的目标是(1)确定具有最佳精度和性能的机器学习技术 从MR图像中自动分割上肢单个肌肉，以及(2)识别模型 分析副矢状面图像的泛化能力和性能。我们的方法是应用受监督的 使用我们独特的、现有的、手动注释的图像进行技术培训，其中包括横跨的每一块肌肉 来自三个不同年龄段(25-35、45-60和61-83)的48名健康人的肩部、肘部和手腕 10名肩袖撕裂的老年人的肩部肌肉。加强潜在的翻译 对于临床环境，我们必须考虑将这些方法应用于临床所处的矢状面旁。 评估肌肉萎缩和脂肪渗透；研究扫描通常在轴位获得。 预期的结果是共享的上肢肌肉分割模型和计算 评估一系列算法性能的框架。此外，我们希望确定如何有效地 分割模型，开发自我们现有的肩部肌肉的轴向断层图像，转移到 评估肩袖损伤后萎缩的临床影像分析。实现这些目标将 为该领域提供了第一套用于自动分割上肢肌肉和Will的开源工具 确定实现临床翻译的关键后续步骤。

SUMMARY The clinical value of magnetic resonance (MR) imaging of muscle anatomy and structure is limited by the bottleneck that arises from muscle segmentation. Thus, clinical evaluations of muscle in rotator cuff injuries to determine injury and repair potential use a single 2D image at a location selected primarily for consistency of anatomic landmarks; these methods are fundamentally flawed and do not accurately reflect either total volume or fatty infiltration for the rotator cuff muscles. Thus, effective, accurate, and fast methods for 3D segmentation for the upper limb are essential to enable the integration of valuable 3D imaging information into clinical decision- making. Our long-term goal is to develop a shareable framework that enables accurate, automated segmentation of MR images of upper limb muscles, on a timescale that makes image analysis tractable for the clinic. The overall objective is to leverage our fully annotated upper limb MR images in 48 healthy individuals and 10 persons with rotator cuff tears to develop, assess, and share successful machine learning approaches for both research and the clinic. Our central hypothesis is that supervised methods trained on our datasets will outperform unsupervised approaches and the resulting models can be successfully transferred to standard clinical scans. Our aims are to (1) identify the machine learning techniques with the best accuracy and performance for automatic segmentation of individual muscles in the upper limb from MR images, and (2) identify model generalizability and performance for analysis of parasagittal plane images. Our approach is to apply supervised techniques trained using our unique, existing, manually annotated images that include every muscle that crosses the shoulder, elbow, and wrist of 48 healthy individuals from three distinct age groups (25-35, 45-60, and 61-83 years) and the shoulder muscles of 10 elderly persons with rotator cuff tears. To strengthen potential translation to the clinical setting, we must consider application of these methods to the parasagittal plane in which clinical evaluation of muscle atrophy and fatty infiltration occurs; research scans are typically obtained in the axial plane. The expected outcomes are shareable models for segmentation of upper limb muscles and a computational framework to assess performance of a range of algorithms. Further, we expect to determine how effectively segmentation models, developed from our existing axial tomographic images of shoulder muscles, transfer for analysis of clinical images acquired to assess atrophy in rotator cuff injury. Accomplishing these objectives will provide the field the first set of open-source tools for automatic segmentation of upper limb muscles and will identify the critical next steps for enabling clinical translation.