Student Projects
Time-series Pose Prediction of Knee Joint for Next Frames Estimation using deep learning
Accurately estimating knee joint kinematics is crucial for understanding joint motion and diagnosing conditions like osteoarthritis. Traditional methods for pose estimation often rely on manual intervention, which is time-consuming and prone to human error. At the Laboratory of Movement Biomechanics, we have developed a pose refiner based on an iterative optimization approach, commonly employed in 2D-3D image registration (2D-3D pose estimation). However, this method requires an initial pose estimate for the refiner to function effectively. This project proposes the use of deep learning techniques to predict the next-frame pose of the knee joint based on previous sequences of kinematic data. By leveraging transformer-based models and advanced time series analysis, the goal is to provide robust, automatic prediction of future poses, streamlining knee kinematics analysis with increased accuracy and efficiency.
Keywords
TSA (Time Series Analysis); Joint Kinematics; Deep Learning; Pose Estimation; Transformer.
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Semester Project , Master Thesis
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Published since: 2025-05-20 , Earliest start: 2025-06-16
Organization Taylor Group / Dual-Plane Fluoroscope
Hosts Wang Jinhao
Topics Medical and Health Sciences , Information, Computing and Communication Sciences
Automated kinematic estimation of the knee joint using deep learning
Knee kinematics is critical for diagnosing pathologies such as osteoarthritis and providing guidance for implant design. Estimating knee kinematics requires aligning a model with a target X-ray image. This estimation process, often implemented by human labor, can be very time-consuming. This research aims to use a deep learning network to estimate the pose (kinematics) from X-ray images, partially replacing manual labor. Such a network should predict a pose from a current fluoroscopic image. By the end of this project, a robust pipeline should be completed, achieving baseline performance to provide convincing pose estimation for images from different modalities (single-plane system & dual-plane system; natural bone model & implant model).
Keywords
Deep learning;X-ray; Computational method; Medical image; Image registration; Rendering.
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Semester Project , Internship , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-05-20 , Earliest start: 2025-06-17
Applications limited to ETH Zurich
Organization Taylor Group / Dual-Plane Fluoroscope
Hosts Wang Jinhao
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Automated 2D3D image registration of dual-plane fluoroscopic image using deep learning enhancement
Understanding knee kinematics is a key requirement for understanding the processes occurring during injury or pathology as well as their remedies. Compared to optical systems, x-ray fluoroscopy directly measures the joint kinematics without soft-tissue artifacts and is thus the method of choice whenever such high performance is required. To extract the 3D knee kinematics the rendering of each bone (3D geometry acquired independently by e.g. CT) is matched to the x-ray image in a process called 2D-3D pose estimation or 'image registration'. Current manual registration methods are time-consuming, expensive, and prone to operator bias. For example, a 10-second trial measurement acquired at 30 Hz consists of about 300 images and takes an experienced operator about 1500 minutes to match manually. Since most studies often consist of thousands of images, an automated way of performing image registration to assist or replace manual alignment becomes crucial. With the help of modern deep networks, we wish to extend the current bolder of traditional 2D3D image registration to reach higher accuracy and robustness.
Keywords
Deep learning, Fluoroscopy, X-ray, CT, Image registration, Optimization, Rendering.
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-05-20 , Earliest start: 2024-06-17
Organization Taylor Group / Dual-Plane Fluoroscope
Hosts Wang Jinhao
Topics Medical and Health Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Design and development of a novel printing approach
3D printing has revolutionized the way objects are designed and fabricated across a wide range of industries—from aerospace and automotive to healthcare and consumer products. It enables rapid prototyping, complex geometries, customized solutions, and recently bioprinting of living tissues that are difficult or impossible to achieve with traditional manufacturing methods. Every 3D printing method has certain drawbacks, often related to resolution, material compatibility, speed, or scalability. The ongoing search for new approaches aims to overcome these challenges and expand the potential of the technology. We have developed and demonstrated a proof of concept for a novel printing approach, and are now seeking to advance it into a fully functional prototype.
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Semester Project , Master Thesis
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Published since: 2025-05-15 , Earliest start: 2025-06-01 , Latest end: 2025-12-31
Organization Zenobi-Wong Group / Tissue Engineering and Biofabrication
Hosts Janiak Jakub
Topics Engineering and Technology
Tissue Engineering Approaches to Study Tendon Injury, Disease, and Therapy
Join a dynamic research team at the intersection of biomechanics, tissue engineering, and cell biology. This project offers hands-on training in state-of-the-art methods to investigate how tendon tissue responds to injury, disease processes, and mechanical stimulation during exercise-based therapy.
Keywords
Tendon biology, tissue engineering, mechanobiology, cell culture, microscopy, regenerative medicine, exercise therapy, inflammation, ECM remodeling
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Master Thesis
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Published since: 2025-04-15 , Earliest start: 2025-05-01 , Latest end: 2026-12-31
Organization Snedeker Group / Laboratory for Orthopaedic Biomechanics
Hosts Snedeker Jess, Prof.
Topics Engineering and Technology
Experimental and Numerical Investigation of Direction-Dependent Flow Resistance in Engineered Geometries
Controlling fluid flow is essential in various natural and engineering systems, with geometry playing a fundamental role in shaping fluid behavior. However, the interaction between geometry and flow behavior remains a complex phenomenon, primarily governed by the flow regime and fluid material properties. Certain geometries, whether naturally occurring or engineered, induce direction-dependent flow resistance, causing variations in velocity and flow rate in opposite directions. One well-known example of such engineered geometries is the Tesla valve—a passive device without moving parts, designed to create asymmetric flow resistance, particularly at high Reynolds numbers. This structure acts like a fluidic diode, offering greater resistance to flow in one direction by generating turbulent vortices and flow separations while allowing smoother movement in the opposite direction. This effect is quantified by diodicity, which represents the ratio of pressure drop in the reverse direction to that in the forward direction, providing a measure of the valve's asymmetric resistance. However, this direction dependence is limited at lower velocities. We have designed two sets of geometries that effectively induce directional flow resistance within high and low fluid flow velocities. This Master’s thesis project aims to experimentally investigate the impact of different flow obstruction designs on direction-dependent resistance in rectangular channels and semicircular arc segments. The student will, together with their direct supervisor, design and construct an experimental setup for the reliable measurement of flow and diodicity. This project offers an excellent opportunity to gain expertise in fluid dynamics, experimental testing, numerical modeling, and additive manufacturing, with applications in biomedical systems. Students with a background in mechanical engineering, fluid dynamics, or related fields are encouraged to apply. Prior experience with COMSOL Multiphysics is beneficial but not mandatory.
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Master Thesis
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Published since: 2025-04-15 , Earliest start: 2025-06-01 , Latest end: 2025-12-01
Organization Musculoskeletal Biomechanics
Hosts Mosayebi Mahdieh
Topics Engineering and Technology
Development of a Heterocellular Human Bone Organoid for Precision Medicine and Treatment
Our goal is to establish a heterocellular 3D printed bone organoid model comprising all major bone cell types (osteoblasts, osteocytes, osteoclasts) to recapitulate bone remodeling units in an in vitro system. The organoids will be produced with the human cells, as they could represent human pathophysiology better than animal models, and eventually could replace them. These in vitro models could be used in the advancement of next-generation personalised treatment strategies. Our tools are different kinds of 3D bioprinting platforms, bio-ink formulations, hydrogels, mol-bioassays, and time-lapsed image processing of micro-CT scans.
Keywords
3D printing, bone organoids, co-culture, bioreactor, hydrogels, drug testing
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Semester Project , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-03-24 , Earliest start: 2022-08-01 , Latest end: 2025-11-30
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Steffi Chris
Topics Engineering and Technology , Biology
Exploring the Mechanoregulation of Bone Regeneration
In over 100 years, the remarkable ability of bone to adapt to its mechanical environment has been a source of scientific fascination. Bone regeneration has been shown to be highly dependent on the mechanical environment at the fracture site. It has been demonstrated that mechanical stimuli can either accelerate or impede regeneration. Despite the fundamental importance of the mechanical environment in influencing bone regeneration, the molecular mechanisms underlying this phenomenon are complex and poorly understood.
Keywords
Bone, Mechanobiology, Spatial transcriptomics, Gene expression, Finite element modelling, Image processing
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Semester Project , Internship , Bachelor Thesis , Master Thesis
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Published since: 2025-03-23 , Earliest start: 2024-11-01 , Latest end: 2025-08-31
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Mathavan Neashan
Topics Medical and Health Sciences , Engineering and Technology
Exploring the 3D Mineralization Behavior in Material-Induced Osteoinduction Through a Multiscale Micro-CT Imaging Approach
The project aims at investigating material-induced osteoinduction using the available mouse model of orthotopic or ectopic bone graft substitute (BGS) application. Through the 3D-3D registration of ex vivo and in vivo multiscale micro-CT images, crucial 3D mineralization behavior of the BGS can be investigated.
Keywords
Femur, Bone Graft Substitute, Critical Size Defect, Osteoinduction, in vivo, micro-CT, 3D-3D Image Registration, Image Analysis, Image Processing, Python, Computational
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-03-11 , Earliest start: 2025-04-01 , Latest end: 2026-01-31
Organization Müller Group / Laboratory for Bone Biomechanics
Hosts Lindenmann Sara
Topics Medical and Health Sciences , Engineering and Technology