13,787 research outputs found
Deep Neural Networks for Anatomical Brain Segmentation
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 the local spatial context while large, compressed
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
Multi-modal segmentation of 3D brain scans using neural networks
Purpose: To implement a brain segmentation pipeline based on convolutional
neural networks, which rapidly segments 3D volumes into 27 anatomical
structures. To provide an extensive, comparative study of segmentation
performance on various contrasts of magnetic resonance imaging (MRI) and
computed tomography (CT) scans. Methods: Deep convolutional neural networks are
trained to segment 3D MRI (MPRAGE, DWI, FLAIR) and CT scans. A large database
of in total 851 MRI/CT scans is used for neural network training. Training
labels are obtained on the MPRAGE contrast and coregistered to the other
imaging modalities. The segmentation quality is quantified using the Dice
metric for a total of 27 anatomical structures. Dropout sampling is implemented
to identify corrupted input scans or low-quality segmentations. Full
segmentation of 3D volumes with more than 2 million voxels is obtained in less
than 1s of processing time on a graphical processing unit. Results: The best
average Dice score is found on -weighted MPRAGE ().
However, for FLAIR (), DWI () and CT (), good-quality segmentation is feasible for most anatomical
structures. Corrupted input volumes or low-quality segmentations can be
detected using dropout sampling. Conclusion: The flexibility and performance of
deep convolutional neural networks enables the direct, real-time segmentation
of FLAIR, DWI and CT scans without requiring -weighted scans
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
We consider the problem of segmenting a biomedical image into anatomical
regions of interest. We specifically address the frequent scenario where we
have no paired training data that contains images and their manual
segmentations. Instead, we employ unpaired segmentation images to build an
anatomical prior. Critically these segmentations can be derived from imaging
data from a different dataset and imaging modality than the current task. We
introduce a generative probabilistic model that employs the learned prior
through a convolutional neural network to compute segmentations in an
unsupervised setting. We conducted an empirical analysis of the proposed
approach in the context of structural brain MRI segmentation, using a
multi-study dataset of more than 14,000 scans. Our results show that an
anatomical prior can enable fast unsupervised segmentation which is typically
not possible using standard convolutional networks. The integration of
anatomical priors can facilitate CNN-based anatomical segmentation in a range
of novel clinical problems, where few or no annotations are available and thus
standard networks are not trainable. The code is freely available at
http://github.com/adalca/neuron.Comment: Presented at CVPR 2018. IEEE CVPR proceedings pp. 9290-929
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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