DeepAI AI Chat
Log In Sign Up

Towards integrating spatial localization in convolutional neural networks for brain image segmentation

by   Pierre-Antoine Ganaye, et al.

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.


3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles

Detailed whole brain segmentation is an essential quantitative technique...

Introducing Hann windows for reducing edge-effects in patch-based image segmentation

There is a limitation in the size of an image that can be processed usin...

Y-net: 3D intracranial artery segmentation using a convolutional autoencoder

Automated segmentation of intracranial arteries on magnetic resonance an...

Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks

High-fidelity semantic segmentation of magnetic resonance volumes is cri...

Harnessing spatial MRI normalization: patch individual filter layers for CNNs

Neuroimaging studies based on magnetic resonance imaging (MRI) typically...