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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

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

Benjamin Frisch

Dr. Benjamin Frisch

  • Nuclear Imaging Detectors
  • Interventional 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

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

Real-Time 3D Reconstruction (Video1)
Video Length: 00:01:00
Reconstruction results obtained with our system.

fullsize version

Hot Stuff

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.
12 October 2017, FMI 00.12.019
Invited Talk by Dr. Del Bue: Tomasi and Kanade revisited: from 3D point reconstruction to object based structure from motion.
This talk will present how semantic information as extracted by object and region detectors can be used to perform classical 3D Computer Vision tasks in a robust way. In particular, object positions can be used to simply calibrate cameras and to identify the position and orientation of objects in 3D. Strikingly, this solution has a closed form that can be solved very efficiently, similarly to the standard Tomasi and Kanade factorization problem. Moreover, the information of the position of objects can be used to initialise standard methods for 3D reconstruction using feature point matches. This new approach provides a novel pipeline to efficiently inject semantic information in Structure from Motion problems.
12 October 2017, FMI 00.12.019
Invited Talk from Prof. Cucchiara: Beyond People Detection: GANs and iterative networks working on human data
Computer Vision with CNNs performs well for people detection. This is not enough. A step forward can be taken to understand the aspect of people detected in low resolution, or corrupted by occlusions in the crowd; to track them in the wild; to detect saliency and pay attention to details only; to forecast motion and human actions. [..] The talk will present projects and results carried out at Imagelab University of Modena and Reggio Emilia on people aspect classification, tracking and saliency analysis based on what people see.
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.

News

6 October 2017,
PhD Defense by Oliver Zettinig
Ultrasound imaging is commonplace in clinical routine and has become the standard of care for a plethora of diagnostic scenarios. Due to issues such as limited image quality or obstructed visibility of anatomy, the exclusive use of this modality for interventional guidance purposes has, however, not yet reached a comparable level of maturity. This thesis addresses several challenges associated with ultrasound imaging by proposing advanced techniques for interventional use. Although applied to a broad spectrum of clinical fields and anatomies, their underlying methodology is generic and can be transferred to other medical scenarios. First, a framework for multi-modal prostate biopsy guidance is introduced, allowing urologists to accurately target suspicious lesions by combining trans-rectal ultrasound information with complementary functional tomographic data. The crucial part of this fusion consisting of deformable image registration is solved by two novel algorithms based on automatically segmented prostate surfaces or a preconditioned intensity similarity metric and a statistical deformation model. To overcome the challenges of manual acquisitions, in particular navigation to and maintenance of appropriate location and suitable acoustic window, robotic solutions are studied. Based on multi-modal image registration, a visual servoing control scheme for neurosurgical navigation is introduced. While compensating for target anatomy movements in real-time, it allows for automatic needle guide alignment for accurate manual insertions. The suitability of such systems for reliable robotic acquisitions even in absence of planning data is demonstrated by applying the developed methods, including image quality optimizations using confidence maps, for automated abdominal aortic aneurysm screenings. Through Doppler modes, ultrasound physics uniquely allows fast analysis of blood flow dynamics, albeit limited to 2D projections. This thesis introduces a novel technique to recover 3D velocity information in combination with a temporal flow profile using measurements from multiple directions. Due to the importance of accurate and linearly independent sampling, the advantages of robotic acquisition schemes can be hereby fully exploited. Results of phantom experiments, volunteer studies and clinical patient evaluations, all in close collaboration with medical partners, demonstrate the great potential benefit of advanced ultrasound imaging techniques in interventional settings in terms of both efficacy and efficiency.
8 September 2017,
PhD Defense by Loïc Peter
The task of semantic image segmentation, i.e. the simultaneous partitioning of an image into regions and the labeling of their content, is a fundamental objective in computer vision and medical image analysis. The need for segmentation algorithms can be encountered in diverse real-world scenarios. In some cases, the segmentation of a scene must be entirely automated, for example to act as a scene understanding tool to help the decision-making of an autonomous system. In other situations, a segmentation algorithm can assist a human user in annotating an image. In this case, the fully-automated aspect is no longer crucial and can be relaxed in favor of an improved flexibility via interactions with the user. In this thesis, we demonstrate how the statistical learning framework of decision forests can be exploited for these two scenarios and propose three contributions. First, we introduce an efficient and generic scale-adaptive forest training scheme for fully automated image understanding. In the context of large digital slides in histopathology, we propose an interactive and adaptive solution entangling segmentation with image exploration to facilitate the examination of these large images. Finally, we show how the segmentation task can be modeled as a Twenty Questions game towards hands-free human-computer interactions. Overall, we hope to demonstrate the effectiveness of decision forests for automated segmentation in diverse application domains. Moreover, we wish to emphasize the relevance of interactive settings and to introduce practical learning-based solutions in cases where standard interactive techniques are not directly applicable.

Recent Publications

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
24 July 2017, Nantes, France
2 papers accepted at ISMAR 2017
We are happy to announce that 2 papers of our chair have been accepted at ISMAR

Alumni News

Address

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

Proud of our Alumni

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
Dr. Ben Glocker
Lecturer in Computing at Imperial College London (UK)
Random Fields for Image Registration
Dr. Amin Katouzian


Master Nicolas Brieu
Application engineer at Definiens AG
Image-based Characterization of Thrombus Formation in Time-lapse DIC Microscopy
11 December 2012
Dr. Loren Schwarz
BMW Group
Machine Learning for Human Motion Analysis and Gesture Recognition
22 June 2012



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