WebHome

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

PD Dr. Tobias Lasser

Tobias Lasser IP - Inverse Problems in Tomography

Research Interests

  • Inverse Problems in Tomographic Reconstruction
  • X-ray Dark-field Imaging
  • Lightfield Microscopy

Teaching

Senior Research Scientists

Shadi Albarqouni

Dr. Shadi Albarqouni

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

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

Kuangyu Shi

Dr. Kuangyu Shi

  • Translational molecular imaging computing
  • Deep learning for computer-aided diagnosis
  • Computational modeling of molecular imaging and tumor microenvironment

Federico Tombari

PD 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

Guillaume Zahnd

Dr. Guillaume Zahnd

  • Ultrasound
  • Interventional Imaging
  • Robotic Imaging
  • Optical Coherence Tomography

Senior Affiliate Lecturers / Research Scientists

Ahmad Ahmadi

Dr. Ahmad Ahmadi

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

Marie Piraud

Dr. Marie Piraud

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

Maximilian Baust

Dr. Maximilian Baust

  • Variational methods
  • Medical image analysis
  • Machine learning

Vasileios Belagiannis

Dr. Vasileios Belagiannis

  • Computer Vision
  • Machine Learning
  • Deep Learning

Christoph Hennersperger

Dr. Christoph Hennersperger

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

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

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

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

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

Wolfgang Wein

Dr. Wolfgang Wein

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

Selected Video

Cardiac C-Arm CT (Video3)
Video Length: 00:00:06
A 3D comparison of ground truth (green) and reconstructed (red) deformable shapes for synthetic data at 50% noise.

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

8 October 2018, 01.06.020
Signed Distance Fields for Rigid and Deformable 3D Reconstruction
Capturing three-dimensional environments is a key task in the growing fields of virtual and augmented reality. This thesis addresses the task of 3D reconstruction of both static and dynamic objects and scenes scanned with a single hand-held RGB-D camera, without any markers or prior knowledge. Reconstructing rigid environments requires estimating the six degrees-of-freedom camera pose at every time instance, and subsequently fusing the acquired data into a geometrically consistent computer model. The task of reconstructing deformable objects is more challenging, as additionally the non-rigid motion that occurred in every frame has to be determined and factored out. We propose to tackle both the rigid and deformable reconstruction problems via implicit-to-implicit alignment of SDF pairs without correspondence search. In the static case, we obtain more accurate pose estimates with a framework that permits straightforward incorporation of various additional constraints, such as surface colour and orientation. We start with the reconstruction of small- to medium-scale household objects and demonstrate how to extend the approach to larger spaces such as rooms. To this end, we develop a limited-extent volume strategy that restricts registration to the most geometrically distinctive regions of a scene, leading to significantly improved rotational motion estimation. Finally, we adapt our approach to dynamic scenes by modifying our implicit-to-implicit approach so that new data is incremented appropriately. For this purpose we evolve an initial SDF to a target SDF by imposing rigidity constraints that require the underlying deformation field to be approximately Killing, i.e. volume-preserving and generating locally isometric motions. Alternatively, we employ gradient flow in the smooth Sobolev space, which favours global deformations over finer-scale details. These strategies also circumvent explicit correspondence search and thus avoid the repeated conversion between SDF and mesh representations that other techniques entail. Nevertheless, we ensure that correspondence information can be recovered by proposing two strategies based on Laplacian eigenfunctions, which are known to encode natural deformation patterns. Thanks to the used SDF representation, our non-rigid reconstruction approach is able to handle topological changes and fast motion, which are major obstacles for existing approaches.
16 September 2018, Granada, Spain
CAMP Awards at MICCAI 2018
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. [...]

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. Sailesh Conjeti


Dr. Nicolas Padoy
Assistant Professor at University of Strasbourg, France
Workflow and Activity Modeling for Monitoring Surgical Procedures
14 April 2010
Dipl.-Inf. Univ. Andreas Hofhauser
MVTec Software GmbH

Dr. Stefan Hinterstoisser


Dr. Ali Bigdelou
Development Specialist and Software Architect at BMW Group, Forschungs- und Innovationszentrum.
Operating Room Specific Domain Model for Usability Evaluations and HCI Design
20 November 2012



Edit | Attach | Refresh | Diffs | More | Revision r1.192 - 11 Mar 2010 - 15:24 - MartinHorn

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif