CampChair

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

Address

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


Selected Video

EVI - Easy Visualization In-Situ
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Hot Stuff

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!
7 May 2020, Online
PhD Defense by Wadim Kehl
The task of detecting objects in images is essential for autonomous systems to categorize, comprehend and eventually navigate or manipulate its environment. Since many applications demand not only detection of objects but also the estimation of their exact poses, 3D CAD models can prove helpful since they provide means for feature extraction and hypothesis refinement. This work, therefore, explores two paths: Firstly, we will look into methods to create richlytextured and geometrically accurate models of real-life objects. Using these reconstructions as a basis, we will investigate on how to improve in the domain of 3D object detection and pose estimation, focusing especially on scalability, i.e. the problem of dealing with multiple objects simultaneously.
19 November 2019, TranslaTUM auditorium
Invited Talk by Dr. Bernhard Kainz
Artificial Narrow Intelligence (ANI) including Machine Learning (ML) has the controversial potential to replace healthcare professionals in their jobs. ANI/ML technology has already the capacity to augment, educate and democratize expertise for medical front-line personnel, freeing up their time for difficult cases while making them better at their tasks in modern precision medicine. ANI/ML can make the quality of an individual's work better, resulting in better decision making in healthcare, faster diagnosis, improved patient communication, more efficient training and less mistakes. In this talk I will shed some light on the looming AI bubble in Healthcare, discuss the potential of ML to complement human decision making in clinical applications and will provide an outlook for selected ML applications that have the potential to survive the coming AI winter. We will explore ML methods for population screening, where lack of training and limited expertise lead to missed early treatment opportunities and a high burden on patients and the healthcare system through the 'refer if unsure' approach. Our ML methods also show promising results for complementing sparse data in a meaningful way. I will briefly discuss the impact of ML on image reconstruction tasks that go beyond human capabilities and will conclude with an outlook into the future of autonomous agents in medical imaging, which have the potential to revolutionise patient monitoring and population screening through guided image acquisition.
5 July 2019, MI 03.13.010
The Coronary Atlas
The coronary atlas is the largest collection of processed coronary angiogram images. Together with computational modelling, shape analysis and benchtop experiments these can reveal unique insights into our individual differences and why generic coronary artery disease treatment methods won’t always work. This thus not only forms a platform for optimising existing strategies but opens entire new pathways for preventive strategies through early prediction with medical biomarkers.
24 May 2019, MI 03.13.010
Invited Talk by Danielle Pace
This work addresses the need for whole heart segmentation to individually label the cardiac chambers and great vessels for patients with congenital heart disease. Our main challenges are extreme anatomical variability and very limited training data. We demonstrate an iterative segmentation method, implemented as a recurrent neural network (RNN), in which a user provides a single landmark per structure, and a segmentation is evolved over multiple steps until reaching a stopping point that can be user-defined or automatically determined. The model grows segmentations in a predictable way that is defined during training. We show that a loss function that evaluates the entire sequence of output segmentations can be optimized using training images alongside input-output pairs of partial segmentations. Our experiments demonstrate that, compared to conventional models that segment an image in one step, the iterative segmentation offers better generalization to patients with the most severe heart malformations.

News

5 December 2019,
CAMP offers up to 5 International Undergraduate Excellence Awards in 2020 to the best international undergraduate applicants
The Chair for Computer Aided Medical Procedures (CAMP) is offering up to 5 research awards to strongly motivated international undergraduate students interested in improving their research skills in machine learning, medical imaging, computer vision, and augmented reality&. The students are required to pursue their undergraduate studies outside Germany. The first two awards amount to 4500 Euros and the second three awards to 4000 Euros. These awards will provide financial support for the awardees during their research stay at CAMP for a period of up to three months between June 2020 and October 2020.
4 December 2019, FMI-Building, Room 01.06.020
PhD Defense by Aslı Okur Kuru
Radioguided surgery is a well-established clinical discipline. However, the devices or systems are not necessarily optimized for the particular needs of the target clinical application. This thesis addresses several challenges associated with the deployment of new technologies or concepts for radioguided surgery in different clinical scenarios on the concrete example of Freehand SPECT, a novel 3D nuclear imaging and navigation system. This includes the analysis of the clinical workflow, definition of the supporting functions of the technology and its modification towards integrated applications.
13 November 2019, FMI-Building, Room 00.12.019
PhD Defense by Marco Esposito
In this dissertation, we explore innovative techniques for intraoperative medical imaging. First, we augment ultrasound-guided needle biopsy with functional information provided by a collaborative robotic arm holding a gamma camera. We evaluate its effectivness for sentinel lymph node biopsy in the axilla. Second, we propose a novel Riemannian regularization method for decreasing the jitter present in the output of electromagnetic tracking systems, which are standard for freehand 3D ultrasound.

Recent Publications

10 March 2020, Seattle, United States
3 papers accepted at CVPR 2020
We are happy to announce that three papers from our Chair will be presented at the International Conference on Computer Vision and Pattern Recognition, CVPR 2020,
which will take place in Seattle, WA, USA on June 16-18, 2020.
22 January 2020, Paris, France
2 papers accepted at RAL and ICRA 2020
We are happy to announce that 2 papers from our Chair accepted by IEEE Robotics and Automation Letters and will be presented at the 2020 IEEE International Conference on Robotics and Automation (ICRA 2020) in Paris, France, May 31 - June 4, 2020.

Alumni News



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