VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

By Hao Chen, Qi-Yan Dou, Lequan Yu, Jing Qin, P. Heng. NeuroImage, 2017

709 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/a63d0576a8ebbb2ddeef6b84f86311c87ba0cdac

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In Neurology

Citations in the corpus

Unpaired Multi-Modal Segmentation via Knowledge Distillation (2020, background): “This strategy has demonstrated effectiveness on segmenting brain tissue [3]–[5] and brain lesions [6]–[8] in multiple sequences of MRI.”
RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans (2018, background): “According to [50], organs could be robustly examined with multiple imaging modalities.”
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions (2019, background): “onvolutional, neural network. 3D U-Net [7] is proposed for processing 3D volumes instead of 2D images as input. In which, all 2D operations of U-Net are replaced with their 3D counterparts. VoxResNet [5], a deep voxel-wise residual network, was proposed for brain segmentation from MR. This 3D networ”
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm (2019, background): “arning is a new eld of machine learning that is recently revolutionizing the automated analysis of images [8,9]. There are many examples of successful applications of deep learning in medical imaging [10,11,12,13,14] and more specically in brain MRI segmentation [15]. In recent years, progress in de”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “Recent research has focused on important tasks such as deep learning based brain extraction [111], cortical and subcortical segmentation [112], [113], [114], [115], and tumor and lesion segmentation [116], [117].”
Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks (2017, methodology): “This includes automatic segmentation of brain lesions [2,10], tumors [9,15,21], and neuroanatomy [14,22,3], using voxelwise network architectures [14,9,17], and more recently using 3D voxelwise networks [3,10], and fully convolutional networks (FCNs) [4,13,17].”
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, neutral): “…(sub-)structures (Zhang et al., 2015; Akkus et al., 2017; Milletari et al., 2017; Fedorov et al., 2017; Dolz et al., 2018; Thyreau et al., 2018; Chen et al., 2018; Nogovitsyn et al., 2019; Li et al., 2019; Sun et al., 2019; Ito et al., 2019) full brain segmentation into more than 25 classes…”
Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks (2018, methodology): “First, to evaluate the improvements to the state of the art in segmentation accuracy due to our algorithm, we compare three distinct algorithms detailed below: the multi-atlas-label-fusion-based DEEDS+JLF [34], [35], the deep-learning-based VoxResNet [36], and the proposed deep-learning-based DenseV”
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review (2017, methodology): “2017 [27] Intensity normalisation Tissue 2D & 3D CNN Mehta et al.”