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Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Prof. Dr. Nassir Navab

Nassir Navab CAMP - Computer Aided Medical Procedures & Augmented Reality

Research Interests

CAMP Publications

Prof. Gudrun Klinker, Ph.D.

Gudrun Klinker FAR - Fachgebiet Augmented Reality

Research Interests

FAR Publications

Prof. Dr. Bjoern Menze

Bjoern Menze IBBM - Image-Based Biomedical Modelling

Research Interests

  • Computational Physiology
  • Image-based Modeling
  • Medical Computer Vision

Prof. Dr. Peter Struss

Peter Struss MQM - Model-Based Systems & Qualitative Reasoning

Research Interests

  • Qualitative REasoning
  • Model-based Diagnosis
  • Knowledge-based Configuration
  • Model-based Functional Safety
  • Model-based Support Systems

Teaching

Senior Research Scientists

Stefanie Demirci

Dr. Stefanie Demirci

  • Interventional image registration
  • Disocclusion in medical images
  • Medical device detection/navigation
  • Segmentation for medical treatment planning

Ulrich Eck

Dr. Ulrich Eck

  • Medical Augmented Reality for Surgery and Training
  • Intra-operative Navigation and Guidance
  • System Architectures for Realtime Interactive Systems
  • Haptic-Enabled Simulators

Christoph Hennersperger

Dr. Christoph Hennersperger

  • Ultrasound Imaging
  • Computer Assisted Neurosurgery
  • Cardiac Surgery and Imaging
  • Robotic Imaging

Tobias Lasser

PD Dr. Tobias Lasser

  • Mathematical Methods in Medical Imaging
  • Tomographic Reconstruction and Ill-posed Problems
  • Computer Assisted Surgery

Marie Piraud

Dr. Marie Piraud

  • Medical data analytics and modeling
  • Stochastic modeling and Bayesian inference
  • Machine learning and quantum artificial intelligence

Federico Tombari

Dr. Federico Tombari

  • Invariant representations for 2D, 3D and RGB-D data
  • 3D object recognition and pose estimation
  • Stereo vision and 3D reconstruction
  • Pattern matching and robust visual correspondence

Shadi Albarqouni

Dr. Shadi Albarqouni

  • Deep Learning for Medical Applications
  • Microscopic Image Analysis
  • Computer Aided Diagnosis

Joint CAMP / Helmholtz Senior Research Group

Tingying Peng

Dr. Tingying Peng

  • Molecular imaging
  • Microscopic image processing
  • Machine learning for medical applications
  • Mathematical modeling for physiological understanding

Senior Affiliate Lecturers / Research Scientists

Ahmad Ahmadi

Dr. Ahmad Ahmadi

  • Neuroimaging
  • Multi-modal Imaging (US,MRI,etc.)
  • Clinical Applications

Maximilian Baust

Dr. Maximilian Baust

  • Variational methods
  • Medical image analysis
  • Machine learning

Vasileios Belagiannis

Dr. Vasileios Belagiannis

  • Computer Vision
  • Machine Learning
  • Deep Learning

Slobodan Ilic

PD Dr. Slobodan Ilic

  • Deformable surface modeling and tracking
  • 3D reconstruction (multi-camera, stereo)
  • Real-time object detection and tracking
  • Object detection and localization in 3D data

Michael Friebe

Prof. Dr. Michael Friebe

  • Lecturer: "Medical Technology Entrepreneurship" and "Image Guided Surgery: From Bench to Bed and Back (IGSB3)"
  • Research: Translational research in medical imaging & image guided minimal invasive procedures

Markus Kowarschik

PD Dr. Markus Kowarschik

  • Lecturer: Interventional Imaging
  • Research: Interventional Imaging, Tomographic Image Reconstruction

Diana Mateus

Dr. Diana Mateus

  • Machine Learning for Medical Applications
  • 3D shape acquisition, modeling and registration
  • Computer Vision

Wolfgang Wein

Dr. Wolfgang Wein

  • Lecturer: conebeam CT and US Imaging
  • Research: Advanced Ultrasound Imaging

Joerg Traub

Dr. Joerg Traub

  • Lecturer: Image Guided Surgery: From Bench to Bed and Back (IGSB3)
  • Invited lecture in CAMP-I: Translation research in medical technology

Peter Noël

Dr. Peter Noël

  • Lecturer: Medical Imaging
  • Research: Tomographic reconstruction

Stephan Nekolla

PD Dr. Stephan Nekolla

  • Quantitative methods in medical imaging
  • Integration of multi modality data: PET, SPECT, CT, MRI
  • Going the full distance: from imaging physics to tracking therapeutical changes

Arash Taki

Dr. Arash Taki

  • Lecturer: Ultrasound Imaging
  • Scientific Advisor: BMC Master of TUM in Singapore

Selected Video

Stereo Time-of-Flight (Video1)
Video Length: 00:02:11
Video shows how the system works and include results examples

fullsize version

Hot Stuff

8 February 2018, MI 00.12.019
PhD Defense by Fausto Milletari
Object detection, segmentation and visual tracking are extremely important problems in both computer vision and medical image analysis. Most of the recent scientific efforts focused on proposing machine learning based approaches that can tackle and solve these problems appropriately. Methods that are based on handcrafted features, the so called shallow approaches, have been widely used and explored until very recently and were often employing machine learning algorithms such as boosting, support vector machines, random forests coupled with a careful choice of manually engineered features which were designed in a specific manner for each task. Most recently these approaches have been replaced by deep learning methods which are as well capable of learning features directly from raw data and can capture semantically meaningful information in a hierarchical and structured fashion. Such approaches, which are articularly suited for vision tasks, deliver in some cases superhuman performances when applied to challenging problems. Although machine learning approaches as such delivered outstanding performances on a number of challenging tasks, many methods -- especially in the field of medical image analysis -- cannot still be applied in a straightforward manner. The lack of large amounts of annotated training data, the presence of noise and artifacts, the low inter-class versus the high intra-class variability of the samples, and other domain-specific factors, often limit the performances of the models. In my talk I will show how voting strategies can be used to tackle detection, segmentation and pose estimation problems relying on voting strategies which look only at image parts and assemble the resulting knowledge into a global decision. This approach overcomes the limitation of current machine learning methods in all those cases where, due to the nature of the data and despite appropriate training, the uncertainty of the decision over previously unseen data remains high.

News

Recent Publications

2 October 2017,
NeuBtracker: publication in Nature Methods
The work on the open-source microscope NeuBtracker by Prof. Dr. Gil Westmeyer and his group was carried out with participation from CAMP. The results have now been published in Nature Methods, see also the TUM press release.
8 August 2017,
One paper accepted at IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
5 August 2017, Vancouver, Canada
2 papers accepted at IROS 2017
25 July 2017, Venice, Italy
5 papers accepted at ICCV 2017
We are happy to announce that 5 papers of our chair have been accepted at ICCV
25 July 2017, Quebec City, Quebec, Canada
11 papers accepted at MICCAI 2017
We are happy to announce that 11 papers of our chair have been accepted at MICCAI

Alumni News

Address

Location Technische Universität München
Fakultät für Informatik / I16
Boltzmannstraße 3
85748 Garching bei München
Germany

Proud of our Alumni

-none- Pierre Georgel
Principal Engineer at dekko Inc
Augmented Reality Tools for Digital Plant Engineering
Master Nicolas Brieu
Application engineer at Definiens AG
Image-based Characterization of Thrombus Formation in Time-lapse DIC Microscopy
11 December 2012
Dr. Alexander Ladikos
CTO at ImFusion GmbH
Real-Time Multi-View 3D Reconstruction for Interventional Environments
5 May 2011
Dr. Christoph Bichlmeier

Immersive, Interactive and Contextual In-Situ Visualization for Medical Applications
1 December 2010
Dr. Oliver Kutter
Senior Domain Engineer at Stryker IMT, Freiburg
Visual Computing for Computer Assisted Interventions
28 June 2010



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