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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

CAMP at JHU

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

Teaching

Senior Research Scientists

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 and Brachytherapy
  • Machine Learning
  • 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

Shadi Albarqouni

Dr. Shadi Albarqouni

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

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

Mosaicing (Video1)
Video Length: 00:01:58
Demonstration of simultaneous registration with a newly developed similarity measure on baby phantom

fullsize version

Hot Stuff

15 January 2021, Zoom
CAMP New Year's Party 2021
Happy 2021! To celebrate the arrival of the new year and connect with each other during these challenging times, the CAMP chair is hosting a virtual New Year's Party on Friday, January 15th. The event will feature educational talks about Transformers, career advice by selected Alumni and will conclude with a fun game night!
9 December 2020, Zoom
CAMP Pizza! December 2020 Edition
The last but not least CAMPPizza in 2020 will take place on December 9th from 10:00-15:00. It will be held purely virtual over Zoom. We are pleased to announce our main speakers, Dr. Federico Tombari (Google, Zürich), Dr. Pierre Jannin (Medical School of the University of Rennes, France) and Dr. Steven McDonagh (Huawei Technologies R&D, London) and welcome our new members and returners Tianyu Song, Heiko Maier, Yousef Yeganeh and Mahdi Hamad.
9 December 2020, YouTube
EIT Health FastRAi Symposium
We would like to invite you to the virtual FastRAi symposium, where CAMP, Deepc and M3i will present the methods and findings developed for the project 'Flexible and Adaptive Software-Framework for X-Ray-enabled AI for COVID-19 detection'.
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.

News

28 December 2020, IFL
SAT.1 Bayern feature on CAMP’s projects against COVID
SAT.1 Bayern features a reportage on the efforts of the CAMP Chair in collaboration with the Radiology of Klinikum Rechts der Isar to fight COVID-19 and equip physicians with solutions that could further be useful for future pandemics.

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

Address

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

Proud of our Alumni

-none- Philipp Stefan


Dr. Tayfur Coskun
Trumpf Medical

Dr. Florent Brunet
Computer vision manager at Ubleam
Contributions to Parametric Image Registration and 3D Surface Reconstruction
30 November 2010
-none- David Tan

Learn to Track: From Images to 3D Data
8 June 2017
Dr. Olivier Pauly
Application Developer @ Definiens AG
Random Forests for Medical Applications
30 August 2012



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