Automatic MRI segmentation for upper limb muscles for clinical applications

总结 磁共振（MR）成像对肌肉解剖和结构的临床价值受到限制， 肌肉分割产生的瓶颈。因此，临床评估肌肉在肩袖损伤， 使用单一2D图像确定损伤和修复可能性，所选位置主要是为了 解剖标志;这些方法存在根本性缺陷，不能准确反映总体积 或者是脂肪浸润因此，有效、准确和快速的3D分割方法 对于将有价值的3D成像信息整合到临床决策中至关重要- 制作。我们的长期目标是开发一个可共享的框架，实现准确的自动化细分 上肢肌肉的MR图像，在一个时间尺度上，使图像分析易于处理的临床。的 总体目标是在48名健康个体和10名患者中利用我们完全注释的上肢MR图像 肩袖撕裂，以开发，评估和分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优于其他方法。 无监督的方法和得到的模型可以成功地转移到标准临床扫描。 我们的目标是（1）识别具有最佳准确性和性能的机器学习技术， 从MR图像中自动分割上肢中的个体肌肉，以及（2）识别模型 用于分析旁视平面图像的通用性和性能。我们的方法是应用监督 使用我们独特的、现有的、手动注释的图像训练的技术，这些图像包括交叉的每一块肌肉。 来自三个不同年龄组（25-35、45-60和61-83）的48名健康个体的肩、肘和腕 10例老年人肩袖撕裂患者的肩肌。加强潜在翻译 对于临床环境，我们必须考虑将这些方法应用于临床上 进行肌肉萎缩和脂肪浸润的评估;研究扫描通常在轴向平面中获得。 预期的结果是共享模型的上肢肌肉分割和计算 框架来评估一系列算法的性能。此外，我们希望确定如何有效地 分割模型，从我们现有的轴向断层图像的肩部肌肉，转移， 分析获得的临床图像以评估肩袖损伤中的萎缩。实现这些目标将 为该领域提供了第一套用于上肢肌肉自动分割的开源工具，并将 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.