Integrating spatial configuration into heatmap regression based CNNs for landmark localization

Christian Payer, Darko Stern, Horst Bischof, Martin Urschler*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 …
Original languageEnglish
Pages (from-to)207-219
JournalMedical Image Analysis
Publication statusPublished - 2019

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