IPE 09-20 Internship or Masterthesis: Development and evaluation of automated segmentation methods in multi-modal 3D breast image registration

Karlsruhe Institute of Technology (KIT)

Karlsruhe, Germany

Area of research:

Diploma & Master Thesis



Part-Time Suitability:

The position is suitable for part-time employment.



Starting date:

1589493600



Job description:

Breast cancer is the most common cancer in women worldwide: more than 1.6 Mio. cancers are diagnosed per year according to WHO cancer statistics. At Karlsruhe Institute of Technology an automated method for multi-modal image registration is developed. The registration allows localizing suspicious tissue structures in all modalities at a glance. Using this approach the advantages of modalities may be combined. Our method therefore contributes to multimodal diagnosis of breast cancer. The challenges for the image registration are the severe differences in image acquisition of different modalities such as patient positioning and compression state of the breast during examination. In order to overcome these challenges we apply sophisticated patient-specific biomechanical models of the breast to simulate the tissue deformation when subjected to compression.


The aim of this work is to contribute to the development of an automated workflow for the proposed patient-specific image registration method by means of developing fundamental algorithms for image processing and segmentation of the breast. The current image segmentation methods should be analyzed in terms of robustness and extended if necessary. Furthermore, the influence of material parameters and different model complexities should be evaluated by selected clinical datasets.


Task description (topics will be adapted to desired time frame)


Starting point of the work will be the analysis of the current state of the registration method and development of a work-plan.


Implementation of image segmentation methods for breast MRI into the existing image registration workflow and their evaluation by selected clinical datasets.



Please apply via recruiter’s website.

Quote Reference: 15146116