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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

Teaching

Senior Research Scientists

Stefanie Demirci

Dr. Stefanie Demirci

  • Interventional image registration
  • Disocclusion in medical images
  • Shape-based detection in medical images
  • Segmentation in medical treatment planning

Pascal Fallavollita

Dr. Pascal Fallavollita

  • Computer-assisted surgery
  • Medical imaging and segmentation
  • Multimodal image registration
  • 3D reconstruction

Slobodan Ilic

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

Amin Katouzian

Dr. Amin Katouzian

  • Computer Aided Surgery and Diagnosis.
  • Cardiac, Intracardiac, and Intravascular Imaging.
  • Signal Processing for Medical Applications.
  • Medical Image Analysis and Perception.

Joint CAMP / Helmholtz Senior Research Group

Diana Mateus

Dr. Diana Mateus

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

Tobias Lasser

Dr. Tobias Lasser

  • Medical Imaging Modalities
  • Computer-Assisted Interventions
  • Tomographic Reconstruction Methods

Abouzar Eslami

Dr. Abouzar Eslami

  • Cardiac imaging and image analysis
  • Medical image registration and segmentation
  • Computer aided diagnosis
  • Computer assisted cardiac surgery

Olivier Pauly

Dipl.-Ing. Univ. Olivier Pauly

Senior Affiliate Lecturers / Research Scientists

Michael Friebe

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

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

Arash Taki

Dr. Arash Taki

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

Selected Video

Scene Understanding From a Moving Camera (Video3)
Video Length: 00:01:33
3D reconstruction

fullsize version

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.

News

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).
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.

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. Alexander Neumann
BMW Group

Dr. Mehmet Yigitsoy
Computer Vision Engineer at Carl Zeiss Meditec AG
Local and Global Consistency Measures in Intensity-based Medical Image Registration
24 September 2015
Dr. Moritz Blume

Joint Image and Motion Reconstruction for Positron Emission Tomography
12 September 2011
Dr. Marina Plavsic
BMW Group
Analyse und Modellierung des Fahrerverhaltens für Assistenzsysteme an Kreuzungen
4 October 2010
Dr. Dzhoshkun Shakir
Research Associate at University College London
Intra-operative Nuclear Imaging Based on Positron-emitting Radiotracers



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