The inside of the human body is fascinating. To capture detailed 3-D images of the inside of the body Magnetic Resonance Imaging (MRI), Computed tomography (CT), and ultrasonic imaging methods are used. Examples are internal structures of the human body (e.g. the skeleton). In many cases these images are used for diagnostic purposes. In other cases, these images are used as input to build models, like digital twins. In yet other cases, like in archaeology or forensic sciences, many images from different subjects (mostly humans or animals) are collected and statistical models are created to describe the variation in populations (to be able to conclude what is considered “normal”), and to be able to estimate stature, sex, and age from a single bone.

These population models are often referred to as morphometric models or statistical shape models (SSMs). At the division of Vehicle Safety, we are creating finite element models of the human body (HBMs), to be used as human surrogates in simulations of vehicle crashes, with the end goal to improve safety and save lives. We have developed SSMs of many body parts, and these are used to scale the HBMs to represent different parts of the population (including both sexes). We do however not have a SSM of the uppermost rib (rib one). Accurate modelling of this rib is deemed important for occupant protection in future vehicles.

This project is suitable for two students and does not require either FE background or more than an introductory statistical course. Interest in biomechanics is meritorious.

Objective and Method

The aim of this project is to develop a statistical shape model of the uppermost human rib:

• Review published data on morphometric models (or statistical shape models) describing human ribs
• Segment, extract the outer surface, of a sample (n=50-100) of human ribs from medical images
• Estimate the cortical thickness of each rib using the open-source software STRADVIEW
• Analyze and describe the variability in the population (e.g. differences between the sexes)

Learning outcomes:

Students will learn and develop skills useful when extracting geometry from medical images, statistical techniques useful for dimensional reduction (not as hard as it might sound) and description of variance in high dimensional data (like an image with many pixels).

Supervisors/Examiners

Johan Iraeus (johan.iraeus@chalmers.se) Division of Vehicle Safety at M2

Bengt Pipkorn (bengt.pipkorn@autoliv.com), Autoliv Research (financial compensation from Autoliv will be awarded the students)

Application

If you find this opportunity interesting and in line with your profile, do not wait with your application! We will start the recruitment process immediately and the positions could be filled before the final application date, 2024-12-31.

If you have any questions, you are welcome to contact the supervisor:

Bengt Pipkorn, bengt.pipkorn@autoliv.com