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EndoMapper SIGNED

EndoMapper: Real-time mapping from endoscopic video

Total Cost €

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EC-Contrib. €

0

Partnership

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 EndoMapper project word cloud

Explore the words cloud of the EndoMapper project. It provides you a very rough idea of what is the project "EndoMapper" about.

algorithm    millimetre    colon    training    stream    intracorporeal    models    deformable    detected    interaction    mathematical    invasive    handcrafted    tomography    autonomously    autonomous    lack    tubular    topology    navigating    automated    perform    tissue    video    time    endomapper    live    combine    pipelines    endoscopies    autonomy    monocular    cameras    attempt    living    exact    surgery    traversing    machine    localization    geometry    human    tumour    gi    augmented    hard    navigation    deep    body    map    drug    mapping    plan    rigidity    radical    robotized    basis    matches    explore    secondly    overcoming    firstly    endoscopes    biopsy    algorithms    perspective    incorporates    vslam    sequences    accuracy    surgeon    minimally    cartography    compute    minimize    risk    endoscope    feed    routine    first    data    medical    longer    cavities    fundamentals    supplied    standard    location    regions    coded    inside    rigid    learning    endoscopy    instructions   

Project "EndoMapper" data sheet

The following table provides information about the project.

Coordinator
UNIVERSIDAD DE ZARAGOZA 

Organization address
address: CALLE PEDRO CERBUNA 12
city: ZARAGOZA
postcode: 50009
website: www.unizar.es

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Spain [ES]
 Total cost 3˙697˙227 €
 EC max contribution 3˙697˙227 € (100%)
 Programme 1. H2020-EU.1.2.1. (FET Open)
 Code Call H2020-FETOPEN-2018-2019-2020-01
 Funding Scheme RIA
 Starting year 2019
 Duration (year-month-day) from 2019-12-01   to  2023-11-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSIDAD DE ZARAGOZA ES (ZARAGOZA) coordinator 1˙439˙125.00
2    UNIVERSITY COLLEGE LONDON UK (LONDON) participant 1˙208˙750.00
3    UNIVERSITE CLERMONT AUVERGNE FR (CLERMONT-FERRAND) participant 728˙700.00
4    ODIN MEDICAL LIMITED UK (LONDON) participant 320˙652.00

Map

 Project objective

Endoscopes traversing body cavities such as the colon are routine in medical practice. However, they lack any autonomy. An endoscope operating autonomously inside a living body would require, in real-time, the cartography of the regions where it is navigating, and its localization within the map. The goal of EndoMapper is to develop the fundamentals for real-time localization and mapping inside the human body, using only the video stream supplied by a standard monocular endoscope.

In the short term, will bring to endoscopy live augmented reality, for example, to show to the surgeon the exact location of a tumour that was detected in a tomography, or to provide navigation instructions to reach the exact location where to perform a biopsy. In the longer term, deformable intracorporeal mapping and localization will become the basis for novel medical procedures that could include robotized autonomous interaction with the live tissue in minimally invasive surgery or automated drug delivery with millimetre accuracy. Our objective is to research the fundamentals of non-rigid geometry methods to achieve, for the first time, mapping from GI endoscopies. We will combine three approaches to minimize the risk. Firstly, we will build a fully handcrafted EndoMapper approach based on existing state-of-the-art rigid pipelines. Overcoming the non-rigidity challenge will be achieved by the new non-rigid mathematical models for perspective cameras and tubular topology. Secondly, we will explore how to improve using machine learning. We propose to work on new deep learning models to compute matches along endoscopy sequences to feed them to a VSLAM algorithm where the non-rigid geometry is still hard-coded. We finally plan to attempt a more radical end-to-end deep learning approach, that incorporates the mathematical models for non-rigid geometry as part of the training of data-driven learning algorithms.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "ENDOMAPPER" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "ENDOMAPPER" are provided by the European Opendata Portal: CORDIS opendata.

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