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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
  • Medical device detection/navigation
  • Segmentation for medical treatment planning

Pascal Fallavollita

Dr. Pascal Fallavollita

  • Patient & process specific navigation
  • Intra-op multimodal imaging & visualization
  • Cardiac, Orthopedic, Trauma, & Radiation therapy

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

Dr. Olivier Pauly

Senior Affiliate Lecturers / Research Scientists

Michael Friebe

affiliate 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

Dr. Markus Kowarschik

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

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

RGBD Xray imaging system
Video Length:
First design and experimental validation of a mirror-based RGBD Xray imaging system.

fullsize version

Hot Stuff

6 July 2018, MI 03.13.010
Invited Talk by Prof. William (Sandy) Wells
Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image-noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment. Estimating the uncertainty in label assignment is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning. One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications. On the other hand, most computationally efficient methods fail to estimate label uncertainty. [...]

News

10 September 2018, IFL
Invited Talk by Dr. Hayato Itoh
In colon cancer screening, polyp size estimation using only colonoscopy images or videos is difficult even for expert physicians although the size information of polyps is important for diagnosis. To construct a fully automated computer-aided diagnosis (CAD) pipeline, a robust and precise polyp size estimation method is highly desired. However, the size estimation problem of a three-dimensional object from a single two-dimensional image is ill-posed due to the lack of three-dimensional spatial information. To circumvent this challenge, we formulate a relaxed form of size estimation as a binary-size classification problem and solve it by a new deep neural network architecture. This relaxed form of size estimation is defined as a two-category classification: under and over a certain polyp dimension criterion that would provoke different clinical treatments (resecting the polyp or not). Our proposed deep learning architecture estimates the depth map from an input colonoscopic RGB image using unsupervised deep learning, and integrates RGB with the computed depth information to produce a four-channel RGB-D imagery data, that is subsequently encoded as a pipeline to extract deep RGB-D image features and facilitate the size classification into two categories: under and over 10mm polyps. We collect a large dataset of colonoscopic videos of totally over 16 hours is constructed for the evaluation of our proposed method. Using this dataset, we evaluate the accuracies of both polyp detection and binary polyp-size estimation since detection is a prerequisite step of a fully automated CAD system. The experimental results show that our proposed method achieves 79.2% accuracy for binary polyp-size classification. We also combine the extraction features by our deep learning architecture and classification of short video clips using a long short-term memory (LSTM) network. Polyp detection (if the video clip contains a polyp or not) shows 88.8% sensitivity when employing the spatio-temporal image feature extraction and classification.
7 September 2018, 01.07.014
Learning Context For Semantic Segmentation And Applications
Nowadays, cameras are an integral part of many devices and systems - from mobile phones to autonomous vehicles and from medical robots to surveillance cameras. While an active field of research, the task of understanding a camera image still poses significant challenges and is not solved in general. A key in interpreting camera images correctly is not to focus on individual image areas or objects, but to use context from the whole image in order to resolve ambiguities - something that we humans are very good at...
5 July 2018, MI 01.11.018
PhD Defense by Sailesh Conjeti
The task of similarity search refers to fetching an item that is closest to the query item from a reference database under the notion of some distance measure. In critical applications of large-scale search and pattern matching, exhaustive comparison is often not possible due to prohibitive computational complexity and memory overheads. Towards mitigating this, hashing has been adopted as a popular approach for performing computationally efficient approximate nearest neighbor search. [...]
27 April 2018,
CAMP@TUM offers 4 International Undergraduate Excellence Awards in 2018: to the best international undergraduate applicants
The Chair for Computer Aided Medical Procedures (CAMP) would like to offer 4 research awards to strongly motivated international undergraduate students interested in improving their research skills in machine learning, medical imaging, computer vision and/or augmented reality. The students are required to pursue their undergraduate studies outside Germany. The first two awards amount to 4500 Euros and the second two awards to 4000 Euros. These awards will provide financial support for the awardees during their research stay at CAMP for a period of up to three months between June 2018 and October 2019.
19 March 2018,
Medvis / Karl-Heinz-Höhne Award 2018 to Markus Rempfler
The Medvis / Karl-Heinz-Höhne Award 2018 has been awarded to Markus Rempfler. The Medvis Karl-Heinz Höhne Award has been established in 2004 by the 'Gesellschaft für Informatik' (GI) working group on Visual Computing in Medicine and Biology. It is awarded bi-annually to a young scientist for outstanding work on image analysis, visualization and interaction in the field of image-based diagnostics and therapy in biology and medicine.

Recent Publications

3 December 2018, Montréal Canada
1 paper accepted at NIPS 2018
We are happy to announce that 1 paper has been accepted at NIPS 2018.
1 October 2018, Madrid
4 papers accepted at IROS 2018
We are happy to announce that 4 papers have been accepted at IROS 2018.
16 September 2018, Granada, Spain
19 papers accepted at MICCAI 2018
We are happy to announce that 19 papers of our chair will be presented at MICCAI
8 September 2018, Munich
5 papers accepted at ECCV 2018
We are happy to announce that 5 papers have been accepted at ECCV 2018.
6 September 2018, Newcastle, UK
One accepted paper at BMVC
We are proud to announce that our recent works was selected for publication at BMVC

Alumni News

Address

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

Proud of our Alumni

Dr. Olivier Pauly
Application Developer @ Definiens AG
Random Forests for Medical Applications
30 August 2012
Master Nicolas Brieu
Application engineer at Definiens AG
Image-based Characterization of Thrombus Formation in Time-lapse DIC Microscopy
11 December 2012
Dr. Martin Groher
CEO of microDimensions
2D-3D Registration of Angiographic Images
8 April 2008
Dr. Loren Schwarz
BMW Group
Machine Learning for Human Motion Analysis and Gesture Recognition
22 June 2012
Dipl.-Ing. Univ. Silvan Kraft




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