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

CAMP at JHU

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

Medical AR and Visalization (Video2)
Video Length: 00:01:53
Multi-modal visualization

fullsize version

Hot Stuff

17 January 2019, MI HS 2
Invited Talk by Prof. Oliver Bimber
Digital images play an essential role in our life. Advanced imaging systems, image processing methods, and visualization techniques are today fundamental to many professions. Medical imaging is certainly a good example. However, when mapping complex (possibly multidimensional) data to 2D, information is lost. What if the notion of digital images would change once and forever? What if instead of capturing, storing, processing and displaying only a single color per pixel, each pixel would consist of individual colors for each emitting direction? Digital images would no longer be two-dimensional matrices but four-dimensional ones (storing spatial information in two dimensions, and directional information in the other two dimensions). This is called a light field. I will introduce the fundamentals of light fields, explain how light fields are captured, processed, and displayed, and present several applications of light-field technology in various application domains, such as microscopy, sensors, and aerial scanning.
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

19 December 2018, 00.12.019
Geometric Methods for 3D Reconstruction from Large Point Clouds
3D reconstruction involves the task of capturing the shape and appearance of objects or scenes in the formof 3D computer aided design (CAD) models, often throughmultiple measurements: individual 2D images or multiview 3D data. In this thesis, we explore both sparse and dense 3D reconstruction methods in scenarios where 3D cues are present and rough, prior CAD models are at hand before the operation. Thanks to the proliferation of 3D sensors and increased accessibility of 3D data, we can now remain true to the 3D nature of our physical world in such digitization processes and utilize direct 3D input, point clouds, which can also alleviate problems of capture modality and illumination conditions. Both sparse and dense reconstruction problems arise frequently either in industrial machine vision where the production processes of parts and goods are to be inspected, or in restoration applications where crude digital models are desired to be improved. We start by explaining our contributions to sparse yet accurate reconstruction from non-overlapping multiview images. The experience developed here have also been used to acquire accurate ground truth aiding the assessment of the next stage, dense reconstruction.We tackle the latter by proposing a novel, multiview point cloud based 3D reconstruction pipeline in which it is possible to incorporate CAD proxies. This is accomplished by first aligning all the scans not by an O(N2) inter-scan matching but by a linear scan-to-model registration. Such alignment ismade possible by novel object detection and pose estimation algorithms.Next, respecting the deviations of the real data from the CAD model, we performa CAD-free multi-scan refinement further increasing the accuracy both qualitatively and quantitatively.We also propose novel methods to initialize such large scale optimization problems and to infer information about the reliability of solutions, known as the uncertainty. As seen, the necessity to incorporate the CAD models as proxies to reconstruction comes along withmany challenges to be addressed. In this thesis, the prominent subtasks include preparing CAD models towards the reconstruction task, object detection, estimation of full six degree of freedom (DoF?) rigid pose and pose graph optimization, in which the roughly aligned scans are brought to the final alignment.We address all of those problems with rigor.Moreover, thanks to our photogrammetric ground truth acquisition strategies, we present thorough evaluation of all tasks, in real datasets besides synthetic ones.
6 December 2018, 00.12.019
PhD Defense by Christian Rupprecht
Nearly all real-world image understanding problems in computer vision are inherently ambiguous. Often, predictive systems do not model this ambiguity and do not consider the possibility that there can be more than just a single outcome for a given problem. This leads to sub-par performance on ambiguous tasks as the model has to account for all possibilities with one answer. We define three typical sources of confusion that render tasks not optimally solvable with a single unique prediction. In this dissertation we describe two principled and general approaches of dealing with ambiguity. First, we elaborate on a method that allows the algorithm to predict multiple instead of one single answer. This is a pragmatic way of dealing with ambiguity: instead of deciding for an exclusive outcome for a given problem, we enable the system to list several possibilities. The second part describes an alternative way to deal with uncertain predictions. Often human perception can provide additional information about a task or application that an intelligent system might have not recognized. Building on the paradigm of human-machine interaction, we show how enabling interaction between the system and a user can improve predictions on the example of semantic segmentation. We describe a novel guiding mechanism that can be seamlessly integrated into the system and shows great potential beyond the demonstrated tasks for several further applications.
19 November 2018, 01.09.014
PhD Defense by Nicola Rieke
Visual tracking of surgical instruments is a key component of various computer-assisted interventions, yet a very challenging problem in the field of Computer Vision. This dissertation presents novel approaches which leverage machine learning techniques for precise real-time tracking and 2D pose estimation of instruments. The achieved results demonstrate that the proposed methods based on Random Forests and Deep Learning provide remarkable advantages with respect to the state of the art in terms of accuracy, robustness and generalization.
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

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. Ben Glocker
Lecturer in Computing at Imperial College London (UK)
Random Fields for Image Registration
M.Sc. Wadim Kehl


Dr. Marina Plavsic
BMW Group
Analyse und Modellierung des Fahrerverhaltens für Assistenzsysteme an Kreuzungen
4 October 2010
Dr. Anabel MartinGonzalez
Universidad Autonoma de Yucatan
Advanced Imaging in Head-Mounted Displays for Patients with Age-Related Macular Degeneration
28 September 2011
Dr. Dzhoshkun Shakir
Research Associate at University College London
Intra-operative Nuclear Imaging Based on Positron-emitting Radiotracers



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

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