Bayesian convolutional neural network based MRI brain extraction on nonhuman primates

Abstract

Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory on nonhuman primates, which are a necessary part of neuroscience research. To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected three-dimensional (3D) conditional random field (CRF). The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine. As a probabilistic network, it is not only able to perform accurate high-resolution pixel-wise brain segmentation, but also capable of measuring the model uncertainty by Monte Carlo sampling with dropout in the testing stage. Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume. The proposed method was evaluated with a manually brain-extracted dataset comprising T1w images of 100 nonhuman primates. Our method outperforms six popular publicly available brain extraction packages and three well-established deep learning based methods with a mean Dice coefficient of 0.985 and a mean average symmetric surface distance of 0.220 mm. A better performance against all the compared methods was verified by statistical tests (all p-values<10-4, two-sided, Bonferroni corrected). The maximum uncertainty of the model on nonhuman primate brain extraction has a mean value of 0.116 across all the 100 subjects...

37 pages, 14 figures

References in corpus (10)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Deep Learning in Neural Networks: An Overview
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
• 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
• DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
• Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

Cited by in corpus (8)

• Automated brain extraction of multi-sequence MRI using artificial neural networks
• Integrating uncertainty in deep neural networks for MRI based stroke analysis
• Accuracy, Uncertainty, and Adaptability of Automatic Myocardial ASL Segmentation using Deep CNN
• Particle image velocimetry analysis with simultaneous uncertainty quantification using Bayesian neural networks
• SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction
• Deep Learning and Bayesian Deep Learning Based Gender Prediction in Multi-Scale Brain Functional Connectivity
• MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for efficient MR T2 mapping
• SUSAN: Segment Unannotated image Structure using Adversarial Network