Estimation of attachment regions of hip muscles in CT image using muscle attachment probabilistic atlas constructed from measurements in eight cadavers.
Estimation of attachment regions of hip muscles in CT image using muscle attachment probabilistic atlas constructed from measurements in eight cadavers.
复制标题
使用根据八具尸体的测量结果构建的肌肉附着概率图集来估计 CT 图像中臀部肌肉的附着区域。
DOI：
10.1007/s11548-016-1519-8
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发表时间：
2017
影响因子：
3
通讯作者：
Sato,Yoshinobu
中科院分区：
文献类型：
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作者：
Fukuda,Norio;Otake,Yoshito;Takao,Masaki;Yokota,Futoshi;Ogawa,Takeshi;Uemura,Keisuke;Nakaya,Ryota;Tamura,Kazunori;Grupp,RobertB;Farvardin,Amirhossein;Armand,Mehran;Sugano,Nobuhiko;Sato,Yoshinobu
目的针对患者的肌肉骨骼生物力学仿真在骨科手术和康复医学中的术前规划和术后评估中具有重要意义。患者特定的肌肉骨骼建模的应用中的困难来自于肌肉附着区域通常在CT和MRI中不可见的事实。我们的目的是开发一种方法，用于估计患者特定的肌肉附着区域的三维医学图像和尸体experiments.MethodsEight新鲜尸体标本的下肢被用于实验中的验证。解剖前采集所有标本的CT图像，采用自动分割方法提取CT图像中的骨区域，重建骨形状模型。在解剖过程中，用光学运动跟踪器记录了10个不同的肌肉附着区域。然后，从8具尸体上获得的这些区域通过非刚性配准集成在平均骨表面上，并构建肌肉附着概率图谱（PA）。结果用该方法计算出的肌肉附着面积与真实附着面积的平均Dice相似系数大于10%在大多数情况下，所提出的方法的平均边界距离误差比以前的方法小1.1 mm，结论我们进行了尸体实验，以测量髋关节肌肉附着区，并构建了肌肉附着区的PA。肌肉附着PA阐明了肌肉附着位置的变化，并允许我们基于从CT导出的患者骨形状更准确地估计患者特定附着区域。
PurposePatient-specific musculoskeletal biomechanical simulation is useful in preoperative surgical planning and postoperative assessment in orthopedic surgery and rehabilitation medicine. A difficulty in application of the patient-specific musculoskeletal modeling comes from the fact that the muscle attachment regions are typically invisible in CT and MRI. Our purpose is to develop a method for estimating patient-specific muscle attachment regions from 3D medical images and to validate with cadaver experiments.MethodsEight fresh cadaver specimens of the lower extremity were used in the experiments. Before dissection, CT images of all the specimens were acquired and the bone regions in CT images were extracted using an automated segmentation method to reconstruct the bone shape models. During dissection, ten different muscle attachment regions were recorded with an optical motion tracker. Then, these regions obtained from eight cadavers were integrated on an average bone surface via non-rigid registration, and muscle attachment probabilistic atlases (PAs) were constructed. An average muscle attachment region derived from the PA was non-rigidly mapped to the patients bone surface to estimate the patient-specific muscle attachment region.ResultsAverage Dice similarity coefficient between the true and estimated attachment areas computed by the proposed method was more than 10% higher than the one computed by a previous method in most cases and the average boundary distance error of the proposed method was 1.1 mm smaller than the previous method on average.ConclusionWe conducted cadaver experiments to measure the attachment regions of the hip muscles and constructed PAs of the muscle attachment regions. The muscle attachment PA clarified the variations of the location of the muscle attachments and allowed us to estimate the patient-specific attachment area more accurately based on the patient bone shape derived from CT.