Deep MRI brain extraction: A 3D convolutional neural network for skull stripping

By Jens Kleesiek, Gregor Urban, Alexander Hubert, Daniel Schwarz, Klaus Hermann Maier-Hein, Martin Bendszus, Armin Biller. 2016

491 citations recorded by the source corpus.

Subfields: Astronomy and Astrophysics, Computational Mechanics

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In Astronomy and Astrophysics

Arvid Lundervold

In Computational Mechanics

Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “Brain extraction [269] A 3D CNN for skull stripping”
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation (2016, methodology): “The approach of Kleesiek et al. [6] is one of few end-to-end 3D CNN approaches for 3D segmentation.”
Deep Learning for Health Informatics (2017, background): “CNNs have also been proposed for the segmentation of isointense stage brain tissues [131] and brain extraction from multimodality MR images [56].”
Deep Learning in Medical Image Analysis (2017, background): “Particularly, great improvements in computer vision inspired its use to medical image analysis such as image segmentation (22, 23), image registration (24), image fusion (25), image annotation (26), computer-aided diagnosis and prognosis (27, 28, 29), lesion/landmark detection (30, 31, 32), and…”
Learning a Variational Network for Reconstruction of Accelerated MRI Data (2017, methodology): “In medical imaging, deep learning has also been applied to areas like segmentation (7, 8), q-space image processing (9), and skull stripping (10).”
Automated brain extraction of multisequence MRI using artificial neural networks (2019, background): “However, this approach is not only very labor-intensive and time-consuming, but also shows a strong interindividual and intraindividual variability (Kleesiek et al., 2016; Smith, 2002; Souza et al., 2018) that could ultimately bias the analysis and consequently hamper the reproducibility of clinical”
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task (2017, background): “Most of the existing network architectures follow a fully convolutional downsample-upsample pathway [11,4,15,3,16,13].”
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker (2016, background): “Recently, 3D convolutional neural networks have been also proposed for Alzheimer's disease classification (Payan and Montana, 2015; Sarraf and Tofighi, 2016), brain lesion segmentation (Kamnitsas et al., 2017) and skull stripping (Kleesiek et al., 2016).”
Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, unknown): “Application domains Related works Transportation [189], [199], [125], [32], [30], [6], [33], [136], [60], [121], [27], [114], [177], [90], [23], [73], [117], [135], [89], [148], [85], [137], [155], [157], [79], [107], [201], [22], [24], [108], [105], [16], [106], [93], [15], [175], [176], [104], [14”
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions (2019, methodology): “RI images. The authors ﬁrst divided the input images into some patches and then utilized these patches for training CNN. To handle an arbitrary number of modalities as the input data, Kleesiek et al. [14] proposed a 3D 1Source code is available on https://github.com/rezazad68/BCDU-Net. CNN for brain”
SynthStrip: skull-stripping for any brain image (2022, unknown): “An early, cross-contrast approach, Deep MRI Brain Extraction (DMBE) (Kleesiek et al., 2016), trains a 3D CNN on combinations of T1w, T2w, and FLAIR contrasts and matches the accuracy of classical baselines for several datasets, including clinical scans with brain tumors.”