2,368 research outputs found
HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation
Recently, dense connections have attracted substantial attention in computer
vision because they facilitate gradient flow and implicit deep supervision
during training. Particularly, DenseNet, which connects each layer to every
other layer in a feed-forward fashion, has shown impressive performances in
natural image classification tasks. We propose HyperDenseNet, a 3D fully
convolutional neural network that extends the definition of dense connectivity
to multi-modal segmentation problems. Each imaging modality has a path, and
dense connections occur not only between the pairs of layers within the same
path, but also between those across different paths. This contrasts with the
existing multi-modal CNN approaches, in which modeling several modalities
relies entirely on a single joint layer (or level of abstraction) for fusion,
typically either at the input or at the output of the network. Therefore, the
proposed network has total freedom to learn more complex combinations between
the modalities, within and in-between all the levels of abstraction, which
increases significantly the learning representation. We report extensive
evaluations over two different and highly competitive multi-modal brain tissue
segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing
on 6-month infant data and the latter on adult images. HyperDenseNet yielded
significant improvements over many state-of-the-art segmentation networks,
ranking at the top on both benchmarks. We further provide a comprehensive
experimental analysis of features re-use, which confirms the importance of
hyper-dense connections in multi-modal representation learning. Our code is
publicly available at https://www.github.com/josedolz/HyperDenseNet.Comment: Paper accepted at IEEE TMI in October 2018. Last version of this
paper updates the reference to the IEEE TMI paper which compares the
submissions to the iSEG 2017 MICCAI Challeng
Sub-cortical brain structure segmentation using F-CNN's
In this paper we propose a deep learning approach for segmenting sub-cortical
structures of the human brain in Magnetic Resonance (MR) image data. We draw
inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN)
architecture for semantic segmentation of objects in natural images, and adapt
it to our task. Unlike previous CNN-based methods that operate on image
patches, our model is applied on a full blown 2D image, without any alignment
or registration steps at testing time. We further improve segmentation results
by interpreting the CNN output as potentials of a Markov Random Field (MRF),
whose topology corresponds to a volumetric grid. Alpha-expansion is used to
perform approximate inference imposing spatial volumetric homogeneity to the
CNN priors. We compare the performance of the proposed pipeline with a similar
system using Random Forest-based priors, as well as state-of-art segmentation
algorithms, and show promising results on two different brain MRI datasets.Comment: ISBI 2016: International Symposium on Biomedical Imaging, Apr 2016,
Prague, Czech Republi
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Convolutional Neural Networks (CNNs) have been recently employed to solve
problems from both the computer vision and medical image analysis fields.
Despite their popularity, most approaches are only able to process 2D images
while most medical data used in clinical practice consists of 3D volumes. In
this work we propose an approach to 3D image segmentation based on a
volumetric, fully convolutional, neural network. Our CNN is trained end-to-end
on MRI volumes depicting prostate, and learns to predict segmentation for the
whole volume at once. We introduce a novel objective function, that we optimise
during training, based on Dice coefficient. In this way we can deal with
situations where there is a strong imbalance between the number of foreground
and background voxels. To cope with the limited number of annotated volumes
available for training, we augment the data applying random non-linear
transformations and histogram matching. We show in our experimental evaluation
that our approach achieves good performances on challenging test data while
requiring only a fraction of the processing time needed by other previous
methods
Recommended from our members
Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.
The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks
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
End-to-end learning of brain tissue segmentation from imperfect labeling
Segmenting a structural magnetic resonance imaging (MRI) scan is an important
pre-processing step for analytic procedures and subsequent inferences about
longitudinal tissue changes. Manual segmentation defines the current gold
standard in quality but is prohibitively expensive. Automatic approaches are
computationally intensive, incredibly slow at scale, and error prone due to
usually involving many potentially faulty intermediate steps. In order to
streamline the segmentation, we introduce a deep learning model that is based
on volumetric dilated convolutions, subsequently reducing both processing time
and errors. Compared to its competitors, the model has a reduced set of
parameters and thus is easier to train and much faster to execute. The contrast
in performance between the dilated network and its competitors becomes obvious
when both are tested on a large dataset of unprocessed human brain volumes. The
dilated network consistently outperforms not only another state-of-the-art deep
learning approach, the up convolutional network, but also the ground truth on
which it was trained. Not only can the incredible speed of our model make large
scale analyses much easier but we also believe it has great potential in a
clinical setting where, with little to no substantial delay, a patient and
provider can go over test results.Comment: Published as a conference paper at IJCNN 2017 Preprint versio
- …