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EndoMapper SIGNED

EndoMapper: Real-time mapping from endoscopic video

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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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.

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

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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