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.

Originalsprache	Englisch
Titel	Lecture Notes in Computer Science
Herausgeber (Verlag)	Springer
Erscheinungsdatum	2022
Seiten	3-7
ISBN (Print)	978-3-031-11202-7
ISBN (elektronisch)	978-3-031-11203-4
DOIs	https://doi.org/10.1007/978-3-031-11203-4_1
Publikationsstatus	Veröffentlicht - 2022

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

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

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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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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.

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

M3 - Conference contribution

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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

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