MEVIS - Fraunhofer Institute for Medical Image Computing

Embedded in a worldwide network of clinical and academic partners, Fraunhofer MEVIS develops real-world software solutions for image-supported early detection, diagnosis, and therapy. Strong focus is placed on cancer as well as diseases of the circulatory system, brain, breast, liver, and lung. The goal is to detect diseases earlier and more reliably, tailor treatments to each individual, and make therapeutic success more measurable. In addition, the institute develops software systems for industrial partners to undertake image-based studies to determine the effectiveness of medicine and contrast agents. To reach its goals, Fraunhofer MEVIS works closely with medical technology and pharmaceutical companies, providing solutions for the entire chain of development from applied research to certified medical products.

Research Portfolio

Application Areas

  • Breast Care
  • Cardio-Vascular Solutions
  • Neuroimaging
  • Clinical Trials
  • Computer-Assisted Histology
  • Lung Care
  • Liver Diagnosis and Therapy
  • Minimally Invasive Interventions


  • Image Registration
  • Image Acquisition
  • Modeling and Simulation
  • Software Technologies
  • Usability and User Experience

Key personel

Selected publications

  1. Homeyer A, Schenk A, Arlt J, Dahmen U, Dirsch O, Hahn HK (2015) Fast and accurate identification of fat droplets in histological images. Computer Methods and Programs in Biomedicine 121(2):59–65
  2. Schwenke M, Strehlow J, Haase S, Jenne J, Tanner C, Langø T, Loeve AJ, Karakitsios I, Xiao X, Levy Y, Sat G, Bezzi M, Braunewell S, Guenther M, Melzer A, Preusser T (2015) An integrated model-based software for FUS in moving abdominal organs. Int J Hyperthermia 31(3):240–250
  3. Heckel F, Meine H, Moltz JH, Kuhnigk J-M, Heverhagen JT, Kießling A, Buerke B, Hahn HK (2014) Segmentation-Based Partial Volume Correction for Volume Estimation of Solid Lesions in CT. IEEE Trans Med Imaging 33(2):462–480
  4. Burger M, Modersitzki J, Ruthotto L (2013) A hyperelastic regularization energy for image registration. SIAM Journal on Scientific Computing. SIAM J Sci Comput 35(1):B132–B148
  5. Rieder C, Kröger T, Schumann C, Hahn HK (2011) GPU-Based Real-Time Approximation of the Ablation Zone for Radiofrequency Ablation. IEEE Transactions on Visualization and Computer Graphics 17(12):1812–1821


Fraunhofer MEVIS
Universitätsallee 29
28359 Bremen