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Abstract
<p class="first" id="d11741126e94">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 spatial
configuration of landmarks. In our extensive experimental evaluation, we show that
the proposed SCN outperforms related methods in terms of landmark localization error
on a variety of size-limited 2D and 3D landmark localization datasets, i.e., hand
radiographs, lateral cephalograms, hand MRIs, and spine CTs.
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