Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Beatrice Demiray geb. Lentes (M.Sc.)


University: Technische Universität München
Fakultät für Informatik, I-16

Boltzmannstr. 3
85748 Garching
Directions: Reaching the Garching campus
Room: 03.13.058
Phone: +49 89 289 19401
Hospital Lab: Interdisziplinäres Forschungslabor (IFL)
Nuklearmedizinische Klinik und Poliklinik

Klinikum rechts der Isar
Ismaninger Str. 22
81675 München
Room: 01.3 A-C
Phone: +49 (0) 89 - 4140 6457
Fax: +49 (0) 89 - 4140 6458

Research Topics and Interests

  • Multimodal Image Registration
  • Medical Image Segmentation
  • Deep Learning for Medical Applications
  • Computer Assisted Interventions / Surgery
  • Computer Aided Diagnosis
  • Medical Device Tracking and Detection
  • Intra-operative Navigation
  • Microscopy Image Analysis

Active Research

Prostate Fusion Biopsy

Prostate Fusion Biopsy

Transrectal ultrasound (TRUS) guided biopsy remains the gold standard for diagnosis. However, it suffers from low sensitivity, leading to an elevated rate of false negative results. On the other hand, the recent advent of PET imaging using a novel dedicated radiotracer, Ga-labelled PSMA (Prostate Specific Membrane Antigen), combined with MR provides improved preinterventional identification of suspicious areas. Thus, MRI/TRUS fusion image-guided biopsy has evolved to be the method of choice to circumvent the limitations of TRUS-only biopsy. We propose a multimodal fusion image-guided biopsy framework that combines PET-MRI images with TRUS. Based on open-source software libraries, it is low cost, simple to use and has minimal overhead in clinical workflow. It is ideal as a research platform for the implementation and rapid bench to bedside translation of new image registration and visualization approaches.
Kooperationsprojekt SFB 824 (3. Förderperiode) & BFS

Kooperationsprojekt SFB 824 (3. Förderperiode) & BFS

The SFB824 (Sonderforschungsbereich 824: Central project for histopathology, immunohistochemistry and analytical microscopy) represents an interdisciplinary consortium which aims at the development of novel imaging technologies for the selection and monitoring of cancer therapy as an important support for personalized medicine. Z2, the central unit for comparative morphomolecular pathology and computational validation, provides integration, registration and quantification of data obtained from both macroscopic and (sub-)cellular in-vivo as well as ex-vivo imaging modalities with tissue-based morphomolecular readouts as the basis for the development and establishment of personalized medicine. In order to develop novel imaging technologies, co-annotation and validation of image data acquired by preclinical or diagnostic imaging platforms via tissue based quantitative morphomolecular methods is crucial. Light sheet microscopy will continue to close the gap between 3D data acquired by in-vivo imaging and 2D histological slices especially focusing on tumor vascularization. The Multimodal ImagiNg Data Flow StUdy Lab (MINDFUL) is a central system for data management in preclinical studies developed within SFB824. Continuing the close collaboration of pathology, computer sciences and basic as well as translational researchers from SFB824 will allow the Z2 to develop and subsequently provide a broad variety of registration and analysis tools for joint imaging and tissue based image standardization and quantification.

The goal of the BFS Project: ImmunoProfiling using Neuronal Networks (IPN2) is to develop a method based on neuronal networks and recent advances in Deep Learning to allow characterization of a patient's tumor as ″hot″ or ″cold″ tumor depending on the identified ImmunoProfile. Recent research has shown that many tumors are infiltrated by immuno-competent cells, as well as that the amount, type and location of the infiltrated lymph nodes in primary tumors provide valuable prognostic information. In contrast to a ″cold tumor″, a ″hot tumor″ is characterized by an active immune system which the tumor has identified as threat. This identification provides the basis for selecting the therapy best suitable for the individual patient.
MedInnovate: From unmet clinical needs to solution concepts

MedInnovate: From unmet clinical needs to solution concepts

Learn how to successfully identify unmet clinical needs within the clinical routine and work towards possible and realistic solutions to solve those needs. Students will get to know tools helping them to be successful innovators in medical technology. This will include all steps from needs finding and selection to defining appropriate solution concepts, including the development of first prototypes. Get introduced to necessary steps for successful idea and concept creation and realize your project in an interdisciplinary teams comprising of physicists, informations scientists and business majors. During the project phase, you are supported by coaches from both industry and medicine, in order to allow for direct and continuous exchange.


Student Projects

Please feel free to contact me via e-mail or drop by and ask!

  • Stevica Bozhinoski: Deep Learning Methods for Soft Tissue Segmentation in Ultrasound Brain Imaging (Master Thesis Ss2018) - running
  • Liesa Weigert: Deep Neural Networks for Pelvic Organ Segmentation in Multiparametic MRI (Master Thesis Ss2018) - running
  • Arsalan Muhammad: HistoTool: Advanced Annotations and Coregistration of Histological and Pre-Clinical Images (IDP / Software Development Project Ws2017/18) - finished
  • Sergio Quijano Rojas: Development of a Medical Intervention Preparation Interface (IDP / Software Development Project Ss2017) - finished


Google ScholarScholar
O. Zettinig, J. Rackerseder, B. Lentes, T. Maurer, K. Westenfelder, M. Eiber, B. Frisch, N. Navab
Preconditioned Intensity-Based Prostate Registration using Statistical Deformation Models
IEEE International Symposium on Biomedical Imaging (ISBI), Melbourne, April 2017. (bib)

Edit | Attach | Refresh | Diffs | More | Revision r1.1 - 08 Aug 2018 - 09:36 - BeatriceLentes

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif