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2019 | OriginalPaper | Buchkapitel

Landmark Localisation in Radiographs Using Weighted Heatmap Displacement Voting

verfasst von : Adrian K. Davison, Claudia Lindner, Daniel C. Perry, Weisang Luo, Timothy F. Cootes, Medical Student Annotation Collaborative

Erschienen in: Computational Methods and Clinical Applications in Musculoskeletal Imaging

Verlag: Springer International Publishing

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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 function and weighting scheme. We evaluate on a dataset of radiographs of child hips, including both normal and severely diseased cases. We show the effect of varying the training set size. Our results show significant improvements in accuracy and robustness for the proposed method compared to a standard heatmap prediction approach and comparable results with a traditional Random Forest method.

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Vorheriges Kapitel Bone Adaptation as Level Set Motion

Nächstes Kapitel Perthes Disease Classification Using Shape and Appearance Modelling

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Titel: Landmark Localisation in Radiographs Using Weighted Heatmap Displacement Voting
verfasst von: Adrian K. Davison
Claudia Lindner
Daniel C. Perry
Weisang Luo
Timothy F. Cootes
Medical Student Annotation Collaborative
Verlag: Springer International Publishing
Buch: Computational Methods and Clinical Applications in Musculoskeletal Imaging
Print ISBN: 978-3-030-11165-6

Electronic ISBN: 978-3-030-11166-3

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-11166-3_7

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