Pelvis and femur shape prediction using principal component analysis for body model on seat comfort assessment. Impact on the prediction of the used palpable anatomical landmarks as predictors

Savonnet, Léo; Duprey, Sonia; Jan, Serge Van Sint; Wang, Xingmei

doi:10.1371/journal.pone.0221201

Cited by 10 publications

(14 citation statements)

References 17 publications

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“…Comparing to similar adult studies, this value is around 24% for the pelvis [13], and 35-45% for the femur [14], [18], [19]. Other studies in adults found 95% of the variation was found in the rst 20 (for the pelvis), 4-10 (for the femur), and 8 (for the tibia) principal components [12], [20]. Another PCA created for the tibia captured 96% of variation in the rst principal component [27].…”

Section: Discussionmentioning

confidence: 64%

“…Comparing to a similar study on the adult femur, bone shape was predicted with an RMS error of 2.3mm using the same demographic and linear bone measurements, however, the best predictive factors were not determined in that study [14]. Other studies have predicted bone geometry using anatomical landmarks, nding errors in the pelvis of 4.23-5.4mm, the femur of 2.6-4.8mm, and the tibia/ bula of 2.88-3.63mm [12], [22], [30].…”

Section: Comparison To Linear Scalingmentioning

confidence: 83%

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Morphological Variation In Paediatric Lower Limb Bones

Carman

Besier

Choisne

2021

Preprint

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Available methods for generating paediatric musculoskeletal geometry are to scale generic adult geometry, which is widely accessible but can be inaccurate, or to obtain geometry from medical imaging, which is accurate but time-consuming and costly. A population-based shape model is required to generate accurate and accessible musculoskeletal geometry in a paediatric population. The pelvis, femur, and tibia/fibula were segmented from 333 CT scans of children aged 4-18 years. Bone morphology variation was captured using principal component analysis (PCA). Subsequently, a shape model was developed to predict bone geometry from demographic and linear bone measurements and validated using a leave one out analysis. The shape model was compared to linear scaling of adult and paediatric bone geometry. The PCA captured growth-related changes in bone geometry. The shape model predicted bone geometry with root mean squared error (RMSE) of 2.91±0.99mm in the pelvis, 2.01±0.62mm in the femur, and 1.85±0.54mm in the tibia/fibula. Linear scaling of an adult mesh produced RMSE of 4.79±1.39mm in the pelvis, 4.38±0.72mm in the femur, and 4.39±0.86mm in the tibia/fibula. We have developed a method for capturing and predicting lower limb bone shape variation in a paediatric population more accurately than linear scaling without using medical imaging.

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Section: Discussionmentioning

confidence: 64%

Section: Comparison To Linear Scalingmentioning

confidence: 83%

Morphological Variation In Paediatric Lower Limb Bones

Carman

Besier

Choisne

2021

Preprint

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“…This transformation makes it possible to interpret the difference in shapes in the deformation metric, which is considered as being intuitive and natural. This capability of the registration algorithm allows for shape analysis, which is usually performed by using the Principal Component Analysis (PCA) [42,43,44]. Unlike the PCA, the algorithm does not require a correlation matrix, which can be large and dense (in the case of CT data).…”

Section: Discussionmentioning

confidence: 99%

Shape morphing technique can accurately predict pelvic bone landmarks

et al. 2021

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Diffeomorphic shape registration allows for the seamless geometric alignment of shapes. In this study, we demonstrated the use of a registration algorithm to automatically seed anthropological landmarks on the CT images of the pelvis. We found a high correlation between manually and automatically seeded landmarks. The registration algorithm makes it possible to achieve a high degree of automation with the potential to reduce operator errors in the seeding of anthropological landmarks. The results of this study represent a promising step forward in effectively defining the anthropological measures of the human skeleton.

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“…Shape models typically use a principal component analysis (PCA) to characterise anatomical variation as a combination of weights and principal components, or modes. This approach has been used to capture the geometry of the adult pelvis 12 , 13 , femur 14 – 20 , the knee joint 21 , the tibia 20 , and the lower limbs collectively 22 . Using a shape model of the adult femur, for example, geometry can be predicted within 2.3 mm root mean square error using just six parameters: age, sex, height, body mass, femoral length, and epicondylar width 14 .…”

Section: Introductionmentioning

confidence: 99%

Morphological variation in paediatric lower limb bones

Carman

Besier

Choisne

2022

Sci Rep

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Available methods for generating paediatric musculoskeletal geometry are to scale generic adult geometry, which is widely accessible but can be inaccurate, or to obtain geometry from medical imaging, which is accurate but time-consuming and costly. A population-based shape model is required to generate accurate and accessible musculoskeletal geometry in a paediatric population. The pelvis, femur, and tibia/fibula were segmented from 333 CT scans of children aged 4–18 years. Bone morphology variation was captured using principal component analysis (PCA). Subsequently, a shape model was developed to predict bone geometry from demographic and linear bone measurements and validated using a leave one out analysis. The shape model was compared to linear scaling of adult and paediatric bone geometry. The PCA captured growth-related changes in bone geometry. The shape model predicted bone geometry with root mean squared error (RMSE) of 2.91 ± 0.99 mm in the pelvis, 2.01 ± 0.62 mm in the femur, and 1.85 ± 0.54 mm in the tibia/fibula. Linear scaling of an adult mesh produced RMSE of 4.79 ± 1.39 mm in the pelvis, 4.38 ± 0.72 mm in the femur, and 4.39 ± 0.86 mm in the tibia/fibula. We have developed a method for capturing and predicting lower limb bone shape variation in a paediatric population more accurately than linear scaling without using medical imaging.

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Pelvis and femur shape prediction using principal component analysis for body model on seat comfort assessment. Impact on the prediction of the used palpable anatomical landmarks as predictors

Cited by 10 publications

References 17 publications

Morphological Variation In Paediatric Lower Limb Bones

Morphological Variation In Paediatric Lower Limb Bones

Shape morphing technique can accurately predict pelvic bone landmarks

Morphological variation in paediatric lower limb bones

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