Automatic Extraction of Muscle Parameters with Attention UNet in Ultrasonography.
Automatic Extraction of Muscle Parameters with Attention UNet in Ultrasonography.
复制标题
DOI：
10.3390/s22145230
复制
发表时间：
2022-07-13
期刊：
影响因子：
3.9
通讯作者：
Panayiotakis, George
中科院分区：
文献类型：
--
作者：
Katakis, Sofoklis;Barotsis, Nikolaos;Kakotaritis, Alexandros;Economou, George;Panagiotopoulos, Elias;Panayiotakis, George
从超声图像中自动描绘骨骼肌的深、浅腱膜在临床常规的许多方面都很重要。特别是，发现肌肉参数，如厚度，束长度或pennation角，是一个耗时的临床任务，需要人力和专业知识。在本研究中，提出了一种用于自动化这些任务的多步骤解决方案。作为第一步，引入了一种毫不费力地提取腱膜以自动测量肌肉厚度的方法。这个过程主要包括三个部分。在第一部分中，Attention UNet已被纳入自动划定所研究的肌肉的边界。之后，使用专门的后处理算法来改善（和校正）分割结果。最后，进行肌肉厚度的计算。所提出的方法已经取得了类似于人类水平的性能。特别是，自动和手动肌肉厚度测量之间的总体差异等于0.4 mm，这是一个重要的结果，证明了自动化这项任务的可行性。在第二步中，提出的方法，束的长度和pennation角度提取通过一个无人监督的管道。首先，对超声图像应用滤波以进一步区分组织与其他肌肉结构。随后，使用著名的K-Means算法成功地将它们分离出来。作为最后一步，报告分割的肌肉组织的主导角，并与手动测量进行比较。拟议的管道在评估数据集中显示出非常有希望的结果。具体而言，在计算旗形角时，自动和手动测量之间的总体差异小于2.22 °（度），再次与人类水平的性能相当。最后，关于肌束长度测量，基于肌肉特性划分结果。在大部分（或全部）肌束位于上下腱膜之间的肌肉中，所提出的管道表现出极好的性能;否则，由于长度计算所需的三角近似引起的误差，整体精度恶化。
Automatically delineating the deep and superficial aponeurosis of the skeletal muscles from ultrasound images is important in many aspects of the clinical routine. In particular, finding muscle parameters, such as thickness, fascicle length or pennation angle, is a time-consuming clinical task requiring both human labour and specialised knowledge. In this study, a multi-step solution for automating these tasks is presented. A process to effortlessly extract the aponeurosis for automatically measuring the muscle thickness has been introduced as a first step. This process consists mainly of three parts. In the first part, the Attention UNet has been incorporated to automatically delineate the boundaries of the studied muscles. Afterwards, a specialised post-processing algorithm was utilised to improve (and correct) the segmentation results. Lastly, the calculation of the muscle thickness was performed. The proposed method has achieved similar to a human-level performance. In particular, the overall discrepancy between the automatic and the manual muscle thickness measurements was equal to 0.4 mm, a significant result that demonstrates the feasibility of automating this task. In the second step of the proposed methodology, the fascicle’s length and pennation angle are extracted through an unsupervised pipeline. Initially, filtering is applied to the ultrasound images to further distinguish the tissues from the other muscle structures. Later, the well-known K-Means algorithm is used to isolate them successfully. As the last step, the dominant angle of the segmented muscle tissues is reported and compared with manual measurements. The proposed pipeline is showing very promising results in the evaluated dataset. Specifically, in the calculation of the pennation angle, the overall discrepancy between the automatic and the manual measurements was less than 2.22° (degrees), once more comparable with the human-level performance. Finally, regarding the fascicle length measurements, the results were divided based on the muscle properties. In the muscles where a large portion (or all) of the fascicles are located between the upper and lower aponeuroses, the proposed pipeline exhibits superb performance; otherwise, overall accuracy deteriorates due to errors caused by the trigonometric approximations needed for the length calculation.
登录
查看更多内容
影响因子：
2.9
作者：
Katakis, Sofoklis;Barotsis, Nikolaos;Panayiotakis, George
通讯作者：
Panayiotakis, George
DOI：
10.2307/2987937
发表时间：
1983-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN
影响因子：
--
作者：
ALTMAN, DG;BLAND, JM
通讯作者：
BLAND, JM
影响因子：
2.9
作者：
Caresio, Cristina;Salvi, Massimo;Minetto, Marco Alessandro
通讯作者：
Minetto, Marco Alessandro
影响因子：
1.8
作者：
Abe, Takashi;Loenneke, Jeremy P.;Thiebaud, Robert S.
通讯作者：
Thiebaud, Robert S.
影响因子：
3
作者：
Akagi, Ryota;Takai, Yohei;Kawakami, Yasuo
通讯作者：
Kawakami, Yasuo