Deep neural networks for anatomical brain segmentation

By Alexandre de Brébisson, Giovanni Montana. 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2015

332 citations recorded by the source corpus.

We present a novel approach to automatically segment magnetic resonance (MR) images of the human brain into anatomical regions. Our methodology is based on a deep artificial neural network that assigns each voxel in an MR image of the brain to its corresponding anatomical region. The inputs of the network capture information at different scales around the voxel of interest: 3D and orthogonal 2D intensity patches capture a local spatial context while downscaled large 2D orthogonal patches and distances to the regional centroids enforce global spatial consistency. Contrary to commonly used segmentation methods, our technique does not require any non-linear registration of the MR images. To benchmark our model, we used the dataset provided for the MICCAI 2012 challenge on multi-atlas labelling, which consists of 35 manually segmented MR images of the brain. We obtained competitive results (mean dice coefficient 0.725, error rate 0.163) showing the potential of our approach. To our knowledge, our technique is the first to tackle the anatomical segmentation of the whole brain using deep neural networks.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f080a55727d09ae48ca1a56c66c6d39d0b0e1161

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In Biomedical Engineering

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Citations in the corpus

FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, background): “…lesion segmentation [28, 29, 30, 31, 32], MR image reconstruction [33, 34, 35, 36, 37, 38] or prediction of brain related diseases and their progression [39, 40, 41, 42, 43] full brain segmentation into more than 25 classes has - so far - only been achieved by a few groups [44, 45, 46, 47, 48].”
A survey on deep learning in medical image analysis (2017, methodology): “de Brebisson and Montana (2015) CNN Anatomical segmentation; fusing multi-scale 2D patches with a 3D patch using a CNN”
Deep learning based brain tumor segmentation: a survey (2020, background): “This segmentation assumption is that the brain image does not contain any tumor tissue or other anomalies [7], [8].”
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy (2017, methodology): “, 2014) or distances to centroids (Brebisson and Montana, 2015).”
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation (2017, unknown): “Each patient had multi-parametric MRI scans, including T2-weighted fluid attenuated inversion recovery (Flair), T1-weighted (T1), T1-weighted contrast-enhanced (T1c), and T2-weighted (T2).”
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles (2019, methodology): “As a pioneer, de Brébisson [44] proposed a unified”
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images (2017, unknown): “…(Cicek et al., 2016), parallelized long short-term memory (LSTM) (Stollenga et al., 2015), convolutional neural networks with multiple pathways (de Brebisson and Montana, 2015) or multiple patch and kernel sizes (Moeskops et al., 2016), and 2D fully convolutional networks (FCN) (Nie et al., 2016).”
DRINet for Medical Image Segmentation (2018, background): “clinical applications, including brain segmentation [2], cardiac ventricle segmentation [3], abdominal organ segmentation [4], and cell segmentation in biological images [5].”
Automatic Segmentation of MR Brain Images With a Convolutional Neural Network (2016, methodology): “In recent years, CNNs also gained popularity in the field of medical image analysis [25]–[34].”
A review on automatic fetal and neonatal brain MRI segmentation (2017, unknown): “They manually delineated 50 structures on 20 neonates at different scan ages around term age (an example atlas of Gousias et al. (2012) is illustrated in Figure 5). de Macedo Rodrigues et al. (2015) constructed multiple single-subject atlases at different ages using T1 scans.”
Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound (2016, methodology): “[27], [28], [29], [30], [31], [32], [36], [37], [38]).”