Theses @ MTEC

We are offering theses on a number of topics, including medical robotics, mobile robotics, navigation and image guidance, medical image analysis, machine learning, treatment planning and optimization.

You can find our current thesis topics in Opens external link in new windowStudIp

 

When contacting us, please attach your transcript of records, both from bachelor and master. Also, briefly explain your technical interests and skills (e.g. programming, machine learning, robotics, image processing)

Disclaimer

  • Typically, the topics are suitable for all kind of thesis, BSc, project work and MSc thesis. Clearly, the BSc thesis will not cover the whole list of tasks.
  • Most topics assume that you have sound programming skills and good general knowledge of math. All topics will require an evaluation of the developed methods, either by simulation or experiments.
  • Finishing the thesis in the minimum amount of time will require very focused and effective work. We recommend visiting us early on to discuss and plan your thesis work

If you are interested in related topics and there is no specific thesis available, do not hesitate to contact us. We have a number of further topics evolving with our daily research and we are open to interesting topics beyond this.

 

Debayan Bhattacharya: Deep Learning for Medical Analysis

debayan.bhattacharya(at)tuhh.de     +49 (0)40 42878  3547      E.3087

Deep Learning for Medical Analysis

For our research, we consider modalities such as optical coherence tomography, endoscopic images, MRI images etc. However, the large data streams and the often non-linear behavior of the observed systems demands advanced image processing algorithms. We therefore couple our approaches with state-of-the-art deep learning methods. Further, due to the scarcity of labelled data in medical domain, we try learning techniques such as self-supervision, contrastive learning etc on unlabelled data. Based on these, we can create custom deep learning pipelines to overcome the task specific challenges.

Please contact me directly if you are interested in any of the above topics.

Robin Mieling: Sensing Tissue Characteristics, Robotic Needle Insertions, Deep Learning

robin.mieling(at)tuhh.de     +49 (0)40 42878 3828      E.3088

Tissue-Needle Interactions

Feedback on local forces and tissue characteristics is limited in minimally invasive procedures. We therefore aim to develop novel methods for providing sensory feedback on tissue-instrument interactions to improve cancer detection. We currently employ optical coherence tomography systems for the image-based measurement of tissue elasticity and needle tip forces.

Robotic Needle Insertions

The acquisiton of tissue samples via percutaneous biopsy is a cornerstone of the diagnosis of cancer. Biopsy is conducted for sampling tissue in numerous target organs such as the prostate or the liver. We investigate how robot-assisted biopsy can further improve accuracy, reliability and safety of the sampling procedure.

Deep Learning for Medical Image Analysis

For our research, we consider high-resolution and high-speed imaging from modalities such as optical coherence tomography and ultrasound. However, the large data streams and the often non-linear behavior of the observed systems demands advanced image processing algorithms. We therefore couple our approaches with state-of-the-art deep learning methods. We utilize robotic manipulators together with our imaging systems to acquire labeled datasets. Based on these, we can create custom deep learning pipelines to overcome the task specific challenges.

Please contact me directly if you are interested in any of the above topics.

Maximilian Neidhardt: Medical Imaging, Tracking

maximilian.neidhardt(at)tuhh.de       +49 (0)40 42878 4639      E.1051

OCE-Imaging

Optical Coherence Tomography is an optical imaging modality based on interferometric measurement of reflections of near-infrared light from different tissue depths. It offers a field of a view few millimeters but micrometer-scale spatial resolution.

The very high temporal resolution of modern OCT systems make it ideal for imaging shear waves which travel at high speed through tissue. The spreading of the wave can be directly linked to the elastic properties of a potential malignant tissue.

Ultrasound Imaging

Medical ultrasound has been long established in the clinical routine and is still today advancing. Our lab offers two research ultrasound machines which can be used in a variety of scenarios such as:

  • Tracking approaches with a robot
  • Online image segmentation
  • Shear wave elastography imaging.
  • Image processing (beamforming)

Please contact me if you are interested in one of the above topics. In general you should bring enthusiasm for experimental lab work, good programming skills (C++/Python/Matlab) and the ability to work independently.

Lennart Bargsten: Deep Learning, 2D/3D Medical Image Analysis

lennart.bargsten(at)tuhh.de       +49 (0)40 42878 3547       E.3087

As I am not an active member of mtec staff I don't supervise theses anymore!

Deep Learning for Ultrasound Image Analysis

Intravascular ultrasound (IVUS) is a medical image acquisition modality, where a catheter with an attached ultrasound probe is used to generate images from the inside of blood vessels (particularly coronary arteries). It can be used to detect stenoses or other vascular diseases, as well as for inspecting stents.

Deep learning tasks include the segmentaion of certain layers of the artery wall, detection of stenoses or stents or generating new labeled images be means of generative adversarial networks (GANs). GANs can also be used to enhance the image quality, to reduce the intensity of artifacts or to determine distributions of echogenicity or tissue density.

Marcel Bengs: 4D Spatio-Temporal Deep Learning Architectures

marcel.bengs(at)tuhh.de      +49 (0)40 42878 3389      E.3083

Johanna Sprenger: Image Guidance and Navigation, Deep Learning in Medical Imaging

johanna.sprenger(at)tuhh.de     +49 (0)40 42878 4639     E.1051

Image Guidance and Navigation

Image guidance, motion compensation and navigation are important in medical scenarios to support the surgeons and keep the patient safe. Different imaging modalities can be considered for these tasks, e.g. optical coherence tomography, ultrasound or camera systems.

Deep Learning in Medical Imaging

Medical imaging technologies deliver valuable information about the patients health. We have different medical systems in our lab and apply conventional image processing and deep learning methods to the acquired data. Different research projects are for example:

  • Robotized surface scans and volume stitching with optical coherence tomography
  • Robotized scanning of phantoms with ultrasound
  • Needle placement and tracking with ultrasound guidance

Stefan Gerlach: Treatment Planning, Machine Learning, Augmented Reality

stefan.gerlach(at)tuhh.de      +49 (0)40 42878 3828       E.3088

Treatment Planning

In robotic radiation therapy, ionizing radiation is delivered by a linear accelerator that is mounted on a robotic arm. This allows to deliver dose from practically arbitrary many directions that overlap in the target. Treatment planning determines from which directions dose should be delivered.

Virtual Reality

For elderly patients in particular, falling can cause severe injuries. An improved body control can help mitigate the increased risk of falls that is associated with high age. However, individual training is expensive and often not feasible. Therefore, we are developing a virtual reality (VR) based training application to improve body control for elderly patients.

We offer a HiWi position for helping to extend and evaluate the VR application (Opens external link in current windowlink).

We also offer a topic for a thesis / project thesis (Opens external link in current windowlink).

Please contact me directly for available thesis topics.

Alexander Schlaefer: Treatment Planning, Machine Learning, Medical Robotics, Mobile Robotics

 schlaefertuhh.de       +49 (0)40 42878 3050      E.3085

Generally, I supervise all theses offered through our institute and I will be actively involved in theses projects. For more information, please see the topics offered through the research assistants.

However, there are number of toy projects I foster myself. Often these are longer shot problems that may have some impact in the future. For example, if you are interested in mobile robotics, please contact me. Also, I am happy to discuss your ideas if they are related to our research.

Sarah Grube: Medical Imagaing, Tracking, Deep Learning, Needle Insertion

sarah.grube(at)tuhh.de    +49 (0)40 42878 4639     E.1051

 

Ultrasound Imaging

Ultrasound is a common and important method in the clinics for different medical interventions. We have several ultrasound probes in our lab which we use for medical imaging and tracking. For this purpose, we use both conventional methods as well as deep learning. Possible topics for theses would be for example:

  • Speckle tracking
  • Ultrasound shear wave elastography
  • Ultrasound image reconstruction
  • Tracking biopsy needles during insertion

More specific topics regarding these issues can be found on our StudIp page. Please contact me directly if you are interested in one of these topics.

 schlaefer(at)tuhh.de      +49 (0)40 42878 3050     E.3085

Generally, I supervise all theses offered through our institute and I will be actively involved in theses projects. For more information, please see the topics offered through the research assistants.

However, there are number of toy projects I foster myself. Often these are longer shot problems that may have some impact in the future. For example, if you are interested in mobile robotics, please contact me. Also, I am happy to discuss your ideas if they are related to our research.