# Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization

## TL;DR

This paper introduces a novel CNN architecture called SpatialConfiguration-Net (SCN) that enhances landmark localization accuracy in medical images by integrating spatial configuration information, especially effective with limited training data.

## Contribution

The paper presents a new CNN design that splits the localization task into simpler sub-problems, improving robustness and accuracy in landmark detection with small datasets.

## Key findings

- SCN outperforms existing methods in landmark localization error.
- Incorporating spatial configuration improves robustness to ambiguities.
- Effective on size-limited datasets.

## Abstract

In many medical image analysis applications, often only a limited amount of training data is available, which makes training of convolutional neural networks (CNNs) challenging. 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 need for large training datasets. Our fully convolutional SpatialConfiguration-Net (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 experimental evaluation, we show that the proposed SCN outperforms related methods in terms of landmark localization error on size-limited datasets.