Unsupervised Non-correspondence Detection in Medical Images Using an Image Registration Convolutional Neural Network.

Julia Andresen; Timo Kepp; Jan Ehrhardt; Claus von der Burchard; Johann Roider; Heinz Handels

doi:10.1007/978-3-031-11203-4_1

Unsupervised Non-correspondence Detection in Medical Images Using an Image Registration Convolutional Neural Network.

Julia Andresen, Timo Kepp, Jan Ehrhardt, Claus von der Burchard, Johann Roider, Heinz Handels

Abstract

Medical image registration allows comparing images from different patients, modalities or time-points, but often suffers from missing correspondences due to pathologies and inter-patient variations.

Original language	English
Title of host publication	Lecture Notes in Computer Science
Publisher	Springer
Publication date	2022
Pages	3-7
ISBN (Print)	978-3-031-11202-7
ISBN (Electronic)	978-3-031-11203-4
DOIs	https://doi.org/10.1007/978-3-031-11203-4_1
Publication status	Published - 2022

Research Areas and Centers

Centers: Center for Artificial Intelligence Luebeck (ZKIL)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-031-11203-4_1

https://dblp.org/rec/conf/wbir/AndresenKEBRH22

Cite this

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title = "Unsupervised Non-correspondence Detection in Medical Images Using an Image Registration Convolutional Neural Network.",

abstract = "Medical image registration allows comparing images from different patients, modalities or time-points, but often suffers from missing correspondences due to pathologies and inter-patient variations.",

author = "Julia Andresen and Timo Kepp and Jan Ehrhardt and Burchard, {Claus von der} and Johann Roider and Heinz Handels",

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doi = "10.1007/978-3-031-11203-4_1",

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AU - Andresen, Julia

AU - Kepp, Timo

AU - Ehrhardt, Jan

AU - Burchard, Claus von der

AU - Roider, Johann

AU - Handels, Heinz

N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2022

Y1 - 2022

N2 - Medical image registration allows comparing images from different patients, modalities or time-points, but often suffers from missing correspondences due to pathologies and inter-patient variations.

AB - Medical image registration allows comparing images from different patients, modalities or time-points, but often suffers from missing correspondences due to pathologies and inter-patient variations.

UR - https://link.springer.com/chapter/10.1007/978-3-031-11203-4_1

U2 - 10.1007/978-3-031-11203-4_1

DO - 10.1007/978-3-031-11203-4_1

M3 - Conference contribution

SN - 978-3-031-11202-7

SP - 3

EP - 7

BT - Lecture Notes in Computer Science

PB - Springer

ER -

Unsupervised Non-correspondence Detection in Medical Images Using an Image Registration Convolutional Neural Network.

Abstract

Research Areas and Centers

UN SDGs

Access to Document

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