Whole Brain Segmentation with Full Volume Neural Network

Abstract

Whole brain segmentation is an important neuroimaging task that segments the whole brain volume into anatomically labeled regions-of-interest. Convolutional neural networks have demonstrated good performance in this task. Existing solutions, usually segment the brain image by classifying the voxels, or labeling the slices or the sub-volumes separately. Their representation learning is based on parts of the whole volume whereas their labeling result is produced by aggregation of partial segmentation. Learning and inference with incomplete information could lead to sub-optimal final segmentation result. To address these issues, we propose to adopt a full volume framework, which feeds the full volume brain image into the segmentation network and directly outputs the segmentation result for the whole brain volume. The framework makes use of complete information in each volume and can be implemented easily. An effective instance in this framework is given subsequently. We adopt the $3$D high-resolution network (HRNet) for learning spatially fine-grained representations and the mixed precision training scheme for memory-efficient training. Extensive experiment results on a publicly available $3$D MRI brain dataset show that our proposed model advances the state-of-the-art methods in terms of segmentation performance. Source code is publicly available at https://github.com/microsoft/VoxHRNet.

arXiv commentAccepted to CMIG

References in corpus (6)

• FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline
• 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
• DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
• On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
• Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features
• A cross-center smoothness prior for variational Bayesian brain tissue segmentation

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