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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.

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

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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