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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

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

Improving Depth Perception and Perception of Layout for In-Situ Visualization in Medical Augmented Reality (Video1)
Video Length: 00:02:58
Methods for focus and context visualization in HMD-based Medical Augmented Reality for improved depth perception.

fullsize version

Whats hot

14 December 2017,
Our Deep Learning papers were featured in Biomedical Computation Review
Our Deep Learning papers were featured in the %_Q_%Deep Learning and the Future of Biomedical Image Analysis%_Q_% article published in the Biomedical Computation Review (BCR).

Hot Stuff

24 November 2017, MI 03.13.010
Invited Talk by Prof. Michel Defrise
This talk introduces the concept and applications of consistency conditions in inverse problems with redundant data. We consider inverse problems modelled by some linear operator or matrix A, such as the Radon transform in tomography. The inverse problem consists in solving an equation Ax=y for x given measured data y. In many applications this equation admits a solution only if the data satisfy a set of equations denoted C(y)=0, referred to as the consistency conditions. If the data are noise free and the operator A accurately models the imaging system, the data are by definition consistent because the “exact” object x satisfies Ax=y, and in that case C(y)=0. In practice however the consistency conditions are not satisfied; they can then be used to estimate some vector of parameters p of the imaging system (typically calibration parameters) by solving C(p, y)=0 for p, where C(y, p) denotes the consistency condition corresponding to the parameter p. After a general introduction to the concept, we will review a variety of examples pertaining to 2D and 3D tomography. Applications will be briefly described.
24 November 2017, MI 01.07.014
PhD defense by Matthias Wieczorek
Modern X-ray based imaging enables recording of phase-contrast (refraction) and dark-field (scattering) information. Tomographic Reconstruction of the dark-field signal poses an especially challenging problem, as the scattering within an object depends on its orientation. Within this thesis an abstract software framework for Tomographic Reconstruction as well as a novel method for Anisotropic X-ray Dark-field Tomography will be presented. A first biomedical experiment on a sample of a human cerebellum indicates that this method could provide a complementary imaging modality for imaging nerve fibers.
22 November 2017, MI 01.09.014
PhD defense by Kanishka Sharma
In Autosomal Dominant Polycystic Kidney Disease (ADPKD), automated segmentation of kidneys for total kidney volume (TKV) measurement is very challenging due to severe, disease-related alterations in kidney morphology. This PhD? thesis analyzes the applicability and performance of machine learning techniques (Random Forests and Deep Learning) for kidney segmentation in ADPKD. The developed segmentation method based on Deep Learning allows fast and reproducible TKV measurements, demonstrating that machine learning can be successfully used for complex medical image segmentation tasks.
22 November 2017, MI 00.05.035
PhD defense by Shadi Albarqouni
Aimed at improving machine learning algorithms by incorporating domain-specific knowledge, we develop a set of mathematical and technical methods that cope with different conditions of data abundance, reliable labels, and class balance. Proposed methods are evaluated for various biomedical applications, in particular, Tomographic Reconstruction and Noise Reduction in Cryo-Electron Tomography, Mitotic figure Detection in Breast Cancer Histology Images, and Depth Perception in Interventional Imaging.

News

19 October 2017, FMI 00.12.019
Invited Talk by Events.PerceptiveIO: The Need for Speed in Computer Vision
Depth cameras are becoming key tools for computer vision tasks ranging from hand, body or object tracking, 3D reconstruction and simultaneous localization and mapping. Almost all these tasks need to solve a tracking problem i.e. each new frame of depth and image data is correlated to the previous, and this temporal information allows for faithful pose and/or geometry reconstruction over time. However, this reliance on temporal information, makes tracking problems hard to solve when using sensors running at 30fps, due to susceptibility to high frame-to-frame scene motions and artifacts such as motion blur. [..] In this talk we are going to demonstrate a 3D capture system for high speed and high quality depth estimation, and show its advantages in a variety of computer vision tasks. Our hardware and software depth pipeline can run at 1.1msec with modern GPUs and readily procurable camera and illumination components.
17 October 2017, FMI 00.12.019
Invited Talk by Dr. Hamid Rezatofighi from University of Adelaide, Australia
Recently, deep structured networks such as deep convolutional (CNN) and recurrent (RNN) neural networks have enjoyed great success. However, similar to most machine learning techniques, current deep learning approaches rely on conventional statistics and restricted to a specific problem formulation. In particular, they are designed to learn a model for a distribution (or a function) that maps a structured input, typically a vector, matrix, or tensor, to a structured output. Due to this limitation, deep learning has not had any success or significant impact in many other applications. In fact, many problems such as object detection, graph matching, and multi-target tracking, are naturally expressed with sets of elements rather than vectors. As opposed to a vector, the size of a set is not fixed in advance, and it is invariant to the ordering of its elements. Therefore, learning approaches built based on conventional statistics cannot be used directly for these problems. In the talk, I will discuss deep learning approaches that use finite set statistics and point processes instead of conventional statistics, as a step towards the set learning problem. I will also highlight further recent work.

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

Dr. Pierre Chatelain
Postdoc at the University of Oxford
Quality-Driven Control of a Robotized Ultrasound Probe
12 December 2016
Dr. Diana Mateus
Ecole Centrale Nantes

Dr. Amin Katouzian


M.Sc. Matthias Wieczorek


Dr. Andreas Keil
Managing Director / CFO at microDimensions GmbH
Dynamic Variational Level Sets for Cardiac 4D Reconstruction
16 September 2010



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