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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

Teaching

Senior Research Scientists

Stefanie Demirci

Dr. Stefanie Demirci

  • Interventional image registration
  • Disocclusion in medical images
  • Shape-based detection in medical images
  • Segmentation in medical treatment planning

Pascal Fallavollita

Dr. Pascal Fallavollita

  • Computer-assisted surgery
  • Medical imaging and segmentation
  • Multimodal image registration
  • 3D reconstruction

Slobodan Ilic

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

Amin Katouzian

Dr. Amin Katouzian

  • Computer Aided Surgery and Diagnosis.
  • Cardiac, Intracardiac, and Intravascular Imaging.
  • Signal Processing for Medical Applications.
  • Medical Image Analysis and Perception.

Joint CAMP / Helmholtz Senior Research Group

Diana Mateus

Dr. Diana Mateus

  • Machine Learning for Medical Applications
  • 3D shape acquisition, modeling and registration
  • Computer Vision

Tobias Lasser

Dr. Tobias Lasser

  • Medical Imaging Modalities
  • Computer-Assisted Interventions
  • Tomographic Reconstruction Methods

Abouzar Eslami

Dr. Abouzar Eslami

  • Cardiac imaging and image analysis
  • Medical image registration and segmentation
  • Computer aided diagnosis
  • Computer assisted cardiac surgery

Olivier Pauly

Dipl.-Ing. Univ. Olivier Pauly

Senior Affiliate Lecturers / Research Scientists

Michael Friebe

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

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

Arash Taki

Dr. Arash Taki

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

Selected Video

Augmented Reality to Optical Coherence Tomography
Video Length:
The video demonstrates our proposed medical Augmented Reality (AR) solution to the long-known problem of determining the distance between the surgical instrument tip and the underlying tissue in ophthalmic surgery to further pave the way of AR into the surgical theater.

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

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


Dr. Alexander Neumann
BMW Group

Dr. Christian Rupprecht
PostDoc at University of Oxford

M.Sc. Stefan Holzer
CTO & Co-founder at Fyusion Inc.
Learning-based Approaches for Template Tracking and Interest Point Detection
8 January 2015



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