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

Associate Prof. Dr. Kuangyu Shi

Kuangyu Shi

Research Interests

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

Prof. Dr. Bjoern Menze

Bjoern Menze IBBM - Image-Based Biomedical Modelling

Research Interests

  • Image-based Modeling
  • Medical Computer Vision & Machine Learning
  • Computer-Aided Diagnostics & Radiomics

PD Dr. Tobias Lasser

Tobias Lasser CIIP - Computational Imaging and Inverse Problems

Research Interests

  • Computational Imaging
  • Inverse Problems in Tomography
  • X-ray Phase-contrast and Dark-field Imaging
  • Lightfield Microscopy

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


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

Daniel Roth

Dr. Daniel Roth

  • Virtual and Augmented Reality
  • Computer Aided Interventions
  • Collaborative Interaction

Dr. Seong Tae Kim

  • Deep Learning for Computer-aided Diagnosis
  • Spatio-temporal Learning
  • Interpretable/Explainable Deep Learning

Dr. Thomas Wendler

Dr. Thomas Wendler

  • Clinical Applications
  • Nuclear Medicine
  • Brachytherapy
  • Image-Guided Interventions

Dr. Shahrooz Faghihroohi

Dr. Shahrooz Faghihroohi

  • Deep Learning for Medical Image Analysis
  • Medical Image Reconstruction
  • Cardiovascular Image Analysis

Senior Affiliate Lecturers / Research Scientists

Ahmad Ahmadi

Dr. Ahmad Ahmadi

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

Vasileios Belagiannis

Dr. Vasileios Belagiannis

  • Computer Vision
  • Machine Learning
  • Deep Learning

Christoph Hennersperger

Dr. Christoph Hennersperger

  • Research & Development in Interventional Imaging
  • Lecturer MedInnovate Innovation Fellowship
  • Lecturer Ultrasound Imaging & Robotics

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

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

Marie Piraud

Dr. Marie Piraud

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

Arash Taki

Dr. Arash Taki

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

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

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

Augmented Presentation (Video1)
Video Length:
Display and observer moving independently. Rendering is using anaglyphic stereo separation.

fullsize version

Hot Stuff

22 October 2020,
Deep Learning Solutions for Cancer Drug Development in Digital Pathology
In the domain of oncology, pathology tasks such as patient diagnosis and cancer drug development, have been revolutionized since the growing availability and quality of high-resolution whole slide scanners. The transition from conventional glass slide microscopic assessment to digital pathology has been beneficial and promising for both pathologists and researchers due to the ability to collect, process and analyze much larger datasets with more stains (clinical markers) using more objective, accurate and consistent methods. Furthermore, with the recent advances in computational power, deep learning solutions have been developed allowing to explore previously unachievable predictive algorithms and to enhance the value and insights that can be generated from pathology tissue images. However, different challenges need to be taken into consideration in order to make the applications more efficient and to increase confidence and adoption in the very conservative field of human pathology. These challenges include, among others, application generalization over multiple stains, limitations in terms of tissue staining materials and procedures, size of histologic whole slide images (WSis) and intra and inter-pathologist variability. In this thesis, we explore new methodologies, techniques, and deep learning solutions to the aforementioned challenges in the context of two different applications: stain generalization and stain virtualization applied to digital images of Colorectal Carcinoma metastases in liver tissue from biopsy and surgical specimen. Slide annotation is a key step in pathology routines and in cancer biomarker research aiming to quantify pattern changes in microscopic WSis of tumor biopsies. The process of manual annotation can be tedious and subjective, especially in the context of drug development research where a correlative analysis of multiple biomarkers is required. In the first part of this dissertation, we elaborate on a supervised learning-based method that allows to segment different functional compartments in histology images of various stainings including Hematoxylin and Eosin (H&E) staining and multiple immunohistochemistry (IHC) stainings. We show the effect of the proposed solution on increasing the stain generalization performance and dealing with dataset variability by comparing to state-of-the-art methods and using different visualization techniques. In the second part, we propose stain virtualization solutions allowing to virtually generate an IHC staining from different input stainings using unsupervised learning methods. Stain virtualization presents many advantages to the pathology drug development workflow, such as reduction of lab workload, reduction in tissue and costs, and multiplexing of different biomarkers on the same tissue and with the same coordinate system. In this context, we additionally propose solutions to one of the main challenges of high-resolution style transfer in general and to WSI processing in particular, i.e. the tiling artifact caused by the necessity of tilewise processing. We describe different experiments and visualizations aiming to prove the hypothesis and we validate the application mathematically and histologically.
7 September 2020, IFL
Lab Research Engineer Position
The CAMP chair at the TUM is looking for a Lab Research Engineer to join the research team at Klinikum rechts der Isar in Munich Germany.
3 September 2020, Garching/ Transla TUM /Online
CAMP Pizza! September 2020 Edition
We are very pleased to announce great speakers to CAMP Pizza on Sept. 3 2020. This time in the event series Prof. Daniel Rückert&, Ph.D (Imperial College London), Cristina Piazza&, Ph.D (Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL (USA), and Dr Georgios Kaissis, MHBA (Institute of Diagnostic and Interventional Radiology and the Institute for Artificial Intelligence and Data Science in Healthcare and Medicine at the Technical University of Munich) will join our event in addition to talks by Prof. Navab, Benjamin Busam, Sasan Martinfar, Alexander Lehner, and Sen Wang. The event will be held in two physical locations (Garching and Transla TUM) as well as virtual.
2 September 2020, Online
Invited Talk by Dr. Tolga Birdal: A Geometric Take on Probabilistic Synchronization
Tolga Birdal will present an unconventional sequel of works bringing fresh perspectives to the 2D/3D multiview alignment, correspondence estimation and optimization problems. In this peculiar talk, Tolga interrogates the mono-optimality in vision problems and argues for an interpolatant between optimization and statistical inference that characterize the space and geometry of solutions for a specific downstream task. Within the scope, the problem of particular interest is synchronization, operation to unison. Tolga will start off with the nuts and bolts on probability and inference in geometric domains such as the Riemannian tempered stochastic gradient MCMC methods. He will then apply different variations of this algorithm to various group valued non-Euclidean parameters (rotations, poses and permutations) belonging to the highly non-convex problem of synchronization within an undirected graph. Subsequently, he will leap into non-parametric methods and show how in the most general case, cyclic-consistency of distributions on maps can be understood as a sum of optimal transportations in the Lie group of the parameters. In the remainder of the talk, he will discuss how some of these algorithms can be used in contemporary deep neural networks as well as alternative twists to synchronize: multi-scan registration without registration. Findings relevant to this project are published in NeurIPS? 2018, CVPR 2019 & CVPR 2020 as well as the CVPR 2020 tutorial on synchronization: http://synchinvision.github.io/. The methods are particularly suited to improve SFM (structure from motion) and SLAM (simultaneous localization and mapping) pipelines that are essential for computer vision and robotics.
8 May 2020, Munich
FastRAi selected for EIT Health 2020 COVID-19 Rapid Response
We are happy to announce that our proposal ‘FastRAI’ has been selected for the EIT Health 2020 portfolio as a COVID-19 Rapid Response project!


Recent Publications

30 November 2020, Kyoto, Japan
2 papers accepted at ACCV 2020
We are happy to announce that 2 papers from our group were accepted and will be presented at the 2020 Asian Conference on Computer Vision (ACCV 2020), November 30 - December 04, 2020.
25 November 2020, Online, Japan
2 papers accepted at 3DV 2020
We are happy to announce that 2 papers from our group were accepted and will be presented at the International Conference on 3D Vision (3DV 2020), November 25 - 28, 2020.
12 November 2020,
Paper accepted for IEEE Transactions on Industrial Electronics (TIE)
Our article on Automatic Force-Based Probe Positioning for Precise Robotic Ultrasound Acquisition has been accepted by IEEE Transactions on Industrial Electronics.
9 November 2020, Recife/Porto De Galinhas, Brasil
3 Papers accepted at ISMAR 2020
We are happy to announce that several papers of our chair will be presented at the IEEE International Symposium on Mixed and Augmented Reality that will happen between November 09th and 13th 2020.
31 August 2020, Lima, Peru
15 papers accepted at MICCAI 2020
We are happy to announce that 15 papers of our chair will be presented at the 23rd International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI 2020) in Lima, Peru, October 4-8, 2020.

Alumni News


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

Proud of our Alumni

Dr. Andreas Keil
Head of Business Development / Pathology & Pharma at Smart Reporting GmbH
Dynamic Variational Level Sets for Cardiac 4D Reconstruction
16 September 2010
Dr. Ben Glocker
Lecturer in Computing at Imperial College London (UK)
Random Fields for Image Registration
Dr. Bjoern Schwerdtfeger

Pick-by-Vision: Bringing HMD-based Augmented Reality into the Warehouse
9 July 2010
M.Sc. Stefan Holzer
CTO & Co-founder at Fyusion Inc.
Learning-based Approaches for Template Tracking and Interest Point Detection
8 January 2015
Dr. Stefan Hinterstoisser

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

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