Integrating spatial configuration into heatmap regression based CNNs for landmark localization

Horst Bischof, Darko Stern, Christian Payer, Martin Urschler

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Abstract

In many medical image analysis applications, only a limited amount of training data is available due to the costs of image acquisition and the large manual annotation effort required from experts. Training recent state-of-the-art machine learning methods like convolutional neural networks (CNNs) from small datasets is a challenging task. In this work on anatomical landmark localization, we propose a CNN architecture that learns to split the localization task into two simpler sub-problems, reducing the overall need for large training datasets. Our fully convolutional SpatialConfiguration-Net (SCN) learns this simplification due to multiplying the heatmap predictions of its two components and by training the network in an end-to-end manner. Thus, the SCN dedicates one component to locally accurate but ambiguous candidate predictions, while the other component improves robustness to ambiguities by incorporating the …
Originalsprachedeutsch
Seiten (von - bis)207-219
FachzeitschriftMedical image analysis
PublikationsstatusVeröffentlicht - 2019

Dies zitieren

Integrating spatial configuration into heatmap regression based CNNs for landmark localization. / Bischof, Horst; Stern, Darko; Payer, Christian; Urschler, Martin.

in: Medical image analysis, 2019, S. 207-219.

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

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