Towards integrating spatial localization in convolutional neural networks for brain image segmentation
This work addresses segmentation inconsistencies in brain MRI for medical imaging applications, but it is incremental as it builds on existing CNN methods.
The paper tackled the problem of segmenting brain MRI images into cerebral structures by integrating spatial constraints into convolutional neural networks, resulting in reduced segmentation inconsistencies.
Semantic segmentation is an established while rapidly evolving field in medical imaging. In this paper we focus on the segmentation of brain Magnetic Resonance Images (MRI) into cerebral structures using convolutional neural networks (CNN). CNNs achieve good performance by finding effective high dimensional image features describing the patch content only. In this work, we propose different ways to introduce spatial constraints into the network to further reduce prediction inconsistencies. A patch based CNN architecture was trained, making use of multiple scales to gather contextual information. Spatial constraints were introduced within the CNN through a distance to landmarks feature or through the integration of a probability atlas. We demonstrate experimentally that using spatial information helps to reduce segmentation inconsistencies.