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Landmark localisation in radiographs using weighted heatmap displacement voting

Abstract:

We propose a new method for fully automatic landmark localisation using Convolutional Neural Networks (CNNs). Training a CNN to estimate a Gaussian response (“heatmap”) around each target point is known to be effective for this task. We show that better results can be obtained by training a CNN to predict the offset to the target point at every location, then using these predictions to vote for the point position. We show the advantages of the approach, including those of using a novel loss f...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-11166-3_7

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
NDORMS
Oxford college:
Wolfson College
Role:
Author
ORCID:
0000-0001-8420-8252
Publisher:
Springer, Cham
Host title:
MSKI 2018: Computational Methods and Clinical Applications in Musculoskeletal Imaging
Series:
Lecture Notes in Computer Science
Journal:
MSKI 2018: Computational Methods and Clinical Applications in Musculoskeletal Imaging, More from this journal
Volume:
11404
Pages:
73-85
Publication date:
2019-01-09
Acceptance date:
2018-07-26
DOI:
ISSN:
0302-9743
ISBN:
9783030111663
Keywords:
Pubs id:
pubs:969214
UUID:
uuid:0b180555-cc02-44be-991c-190ab0a74ae8
Local pid:
pubs:969214
Source identifiers:
969214
Deposit date:
2019-02-07

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