Grant Tamara M, Diamond Laura E, Pizzolato Claudio, Killen Bryce A, Devaprakash Daniel, Kelly Luke, Maharaj Jayishni N, Saxby David J
School of Allied Health Sciences, Griffith University, Gold Coast, QLD, Australia.
Griffith Centre for Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia.
PeerJ. 2020 Feb 4;8:e8397. doi: 10.7717/peerj.8397. eCollection 2020.
Musculoskeletal models are important tools for studying movement patterns, tissue loading, and neuromechanics. Personalising bone anatomy within models improves analysis accuracy. Few studies have focused on personalising foot bone anatomy, potentially incorrectly estimating the foot's contribution to locomotion. Statistical shape models have been created for a subset of foot-ankle bones, but have not been validated. This study aimed to develop and validate statistical shape models of the functional segments in the foot: first metatarsal, midfoot (second-to-fifth metatarsals, cuneiforms, cuboid, and navicular), calcaneus, and talus; then, to assess reconstruction accuracy of these shape models using sparse anatomical data.
Magnetic resonance images of 24 individuals feet (age = 28 ± 6 years, 52% female, height = 1.73 ± 0.8 m, mass = 66.6 ± 13.8 kg) were manually segmented to generate three-dimensional point clouds. Point clouds were registered and analysed using principal component analysis. For each bone segment, a statistical shape model and principal components were created, describing population shape variation. Statistical shape models were validated by assessing reconstruction accuracy in a leave-one-out cross validation. Statistical shape models were created by excluding a participant's bone segment and used to reconstruct that same excluded bone using full segmentations and sparse anatomical data (i.e. three discrete points on each segment), for all combinations in the dataset. Tali were not reconstructed using sparse anatomical data due to a lack of externally accessible landmarks. Reconstruction accuracy was assessed using Jaccard index, root mean square error (mm), and Hausdorff distance (mm).
Reconstructions generated using full segmentations had mean Jaccard indices between 0.77 ± 0.04 and 0.89 ± 0.02, mean root mean square errors between 0.88 ± 0.19 and 1.17 ± 0.18 mm, and mean Hausdorff distances between 2.99 ± 0.98 mm and 6.63 ± 3.68 mm. Reconstructions generated using sparse anatomical data had mean Jaccard indices between 0.67 ± 0.06 and 0.83 ± 0.05, mean root mean square error between 1.21 ± 0.54 mm and 1.66 ± 0.41 mm, and mean Hausdorff distances between 3.21 ± 0.94 mm and 7.19 ± 3.54 mm. Jaccard index was higher ( < 0.01) and root mean square error was lower ( < 0.01) in reconstructions from full segmentations compared to sparse anatomical data. Hausdorff distance was lower ( < 0.01) for midfoot and calcaneus reconstructions using full segmentations compared to sparse anatomical data.
For the first time, statistical shape models of the primary functional segments of the foot were developed and validated. Foot segments can be reconstructed with minimal error using full segmentations and sparse anatomical landmarks. In future, larger training datasets could increase statistical shape model robustness, extending use to paediatric or pathological populations.
肌肉骨骼模型是研究运动模式、组织负荷和神经力学的重要工具。在模型中对骨骼解剖结构进行个性化设置可提高分析准确性。很少有研究关注足部骨骼解剖结构的个性化，这可能会错误估计足部在运动中的作用。已经为部分足踝骨骼创建了统计形状模型，但尚未经过验证。本研究旨在开发并验证足部功能节段的统计形状模型：第一跖骨、中足（第二至第五跖骨、楔骨、骰骨和舟骨）、跟骨和距骨；然后，使用稀疏解剖数据评估这些形状模型的重建准确性。
对24名个体足部的磁共振图像（年龄 = 28 ± 6岁，52%为女性，身高 = 1.73 ± 0.8米，体重 = 66.6 ± 13.8千克）进行手动分割，以生成三维点云。使用主成分分析对点云进行配准和分析。对于每个骨节段，创建一个统计形状模型和主成分，描述群体形状变化。通过在留一法交叉验证中评估重建准确性来验证统计形状模型。通过排除一名参与者的骨节段来创建统计形状模型，并使用完整分割和稀疏解剖数据（即每个节段上的三个离散点）对该相同排除的骨进行重建，用于数据集中的所有组合。由于缺乏外部可触及的标志点，未使用稀疏解剖数据重建距骨。使用杰卡德指数、均方根误差（毫米）和豪斯多夫距离（毫米）评估重建准确性。
使用完整分割生成的重建的平均杰卡德指数在0.77 ± 0.04至0.89 ± 0.02之间，平均均方根误差在0.88 ± 0.19至1.17 ± 0.18毫米之间，平均豪斯多夫距离在2.99 ± 0.98毫米至6.63 ± 3.68毫米之间。使用稀疏解剖数据生成的重建的平均杰卡德指数在0.67 ± 0.06至0.83 ± 0.05之间，平均均方根误差在1.21 ± 0.54至1.66 ± 0.41毫米之间，平均豪斯多夫距离在3.21 ± 0.94毫米至7.19 ± 3.54毫米之间。与稀疏解剖数据相比，使用完整分割进行的重建中杰卡德指数更高（<0.01）且均方根误差更低（<0.01）。与稀疏解剖数据相比，使用完整分割对中足和跟骨进行重建时豪斯多夫距离更低（<0.01）。
首次开发并验证了足部主要功能节段的统计形状模型。使用完整分割和稀疏解剖标志点可以以最小误差重建足部节段。未来，更大的训练数据集可以提高统计形状模型的稳健性，将其应用扩展到儿科或病理人群。
