Statistical Shape Modeling of the First Human Rib: Modeling Using CT Data and Demographic Predictors
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The first human rib plays a critical role in thoracic biomechanics and is increasingly
relevant for improving simulations in human body models used in vehicle and traffic
safety research. This thesis aims to develop a statistical shape model (SSM) of the
first rib that captures morphological variability in terms of sex, age, height, and
body mass index (BMI), based on computed tomography (CT) data.
A total of 50 anonymized CT scans, sourced from a trauma hospital through
the University of Michigan, were used from an available dataset of 104 samples.
The workflow included segmentation in 3D-Slicer, landmarking in ANSA, and
cortical bone thickness mapping using Stradview. To enable statistical analysis, a
template rib surface was morphed to match each segmented rib using the Infepy
Python library, so that all surfaces shared the same number and arrangement of
nodes. Rib thickness values were also transferred onto these common nodes to
combine shape and cortical thickness data. Generalized Procrustes Analysis (GPA)
and Principal Component Analysis (PCA) were performed to align the ribs and
identify dominant modes of variation, respectively. Linear regression was employed
to examine relationships between principal component scores and demographic
variables, and to predict shape and thickness parameters, which were then used to
reconstruct corresponding 3D meshes.
Four principal components showed statistically significant correlations (p ≤ 0.05)
with demographic variables (age, sex, height, and BMI) in regression analysis.
These components primarily encoded variation in rib size, cortical thickness, and
subtle shape changes. However, age, sex, height, and BMI alone could capture only
15% of the total variability, indicating that these parameters were insufficient to
fully predict rib morphology.
Despite these limitations, the resulting model represents a valuable step toward
anatomically realistic rib shape modeling and offers potential for enhancing
personalized simulations in safety engineering. Additionally, PCA proved useful
beyond model building by enabling quantification and visualization of how rib
morphology varies across individuals.
Beskrivning
Ämne/nyckelord
First rib, statistical shape model (SSM), cortical bone mapping, Generalized Procrustes Analysis (GPA), Principal Component Analysis (PCA), regression