Kagiyama Yoshiyuki, Otomaru Itaru, Takao Masaki, Sugano Nobuhiko, Nakamoto Masahiko, Yokota Futoshi, Tomiyama Noriyuki, Tada Yukio, Sato Yoshinobu
Graduate Faculty of Interdisciplinary Research, University of Yamanashi, 4-3-11, Takeda, Kofu, Yamanashi, Japan.
Graduate School of Engineering, Kobe University, 1-1, Rokkodai, Nada, Kobe, Japan.
Int J Comput Assist Radiol Surg. 2016 Dec;11(12):2253-2271. doi: 10.1007/s11548-016-1428-x. Epub 2016 Jun 25.
This study describes the use of CT images in atlas-based automated planning methods for acetabular cup implants in total hip arthroplasty (THA). The objective of this study is to develop an automated cup planning method considering the statistical distribution of the residual thickness.
From a number of past THA planning datasets, we construct two statistical atlases that represent the surgeon's expertise. The first atlas is a pelvis-cup merged statistical shape model (PC-SSM), which encodes global spatial relationships between the patient anatomy and implant. The other is a statistical residual thickness map (SRTM) of the implant surface, which encodes local spatial constraints of the anatomy and implant. In addition to PC-SSM and SRTM, we utilized the minimum thickness as a threshold constraint to prevent penetration.
The proposed method was applied to the pelvis shapes segmented from CT images of 37 datasets of osteoarthritis patients. Automated planning results with manual segmentation were compared to the plans prepared by an experienced surgeon. There was no significant difference in the average cup size error between the two methods (1.1 and 1.2 mm, respectively). The average positional error obtained by the proposed method, which integrates the two atlases, was significantly smaller (3.2 mm) than the previous method, which uses single atlas (3.9 mm). In the proposed method with automated segmentation, the size error of the proposed method for automated segmentation was comparable (1.1 mm) to that for manual segmentation (1.1 mm). The average positional error was significantly worse (4.2 mm) than that using manual segmentation (3.2 mm). If we only consider mildly diseased cases, however, there was no significance between them (3.2 mm in automated and 2.6 mm in manual segmentation).
We infer that integrating PC-SSM and SRTM is a useful approach for modeling experienced surgeon's preference during cup planning.
本研究描述了在全髋关节置换术（THA）髋臼杯植入的基于图谱的自动规划方法中CT图像的应用。本研究的目的是开发一种考虑残余厚度统计分布的自动杯规划方法。
从多个过去的THA规划数据集中，我们构建了两个代表外科医生专业知识的统计图谱。第一个图谱是骨盆-杯合并统计形状模型（PC-SSM），它编码了患者解剖结构与植入物之间的全局空间关系。另一个是植入物表面的统计残余厚度图（SRTM），它编码了解剖结构和植入物的局部空间约束。除了PC-SSM和SRTM，我们还利用最小厚度作为阈值约束来防止穿透。
所提出的方法应用于从37例骨关节炎患者的CT图像中分割出的骨盆形状。将自动规划结果与手动分割结果与由经验丰富的外科医生制定的计划进行比较。两种方法之间的平均杯尺寸误差没有显著差异（分别为1.1和1.2毫米）。通过整合两个图谱的所提出的方法获得的平均位置误差（3.2毫米）明显小于使用单个图谱的先前方法（3.9毫米）。在所提出的自动分割方法中，自动分割的所提出方法的尺寸误差（1.1毫米）与手动分割的尺寸误差（1.1毫米）相当。平均位置误差明显比使用手动分割的情况差（4.2毫米）（3.2毫米）。然而，如果我们只考虑轻度患病病例，则它们之间没有显著性差异（自动分割为3.2毫米，手动分割为2.6毫米）。
我们推断，整合PC-SSM和SRTM是在杯规划过程中模拟经验丰富的外科医生偏好的有用方法。
