9,148 research outputs found
Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
Deep neural networks have demonstrated very promising performance on accurate
segmentation of challenging organs (e.g., pancreas) in abdominal CT and MRI
scans. The current deep learning approaches conduct pancreas segmentation by
processing sequences of 2D image slices independently through deep, dense
per-pixel masking for each image, without explicitly enforcing spatial
consistency constraint on segmentation of successive slices. We propose a new
convolutional/recurrent neural network architecture to address the contextual
learning and segmentation consistency problem. A deep convolutional sub-network
is first designed and pre-trained from scratch. The output layer of this
network module is then connected to recurrent layers and can be fine-tuned for
contextual learning, in an end-to-end manner. Our recurrent sub-network is a
type of Long short-term memory (LSTM) network that performs segmentation on an
image by integrating its neighboring slice segmentation predictions, in the
form of a dependent sequence processing. Additionally, a novel
segmentation-direct loss function (named Jaccard Loss) is proposed and deep
networks are trained to optimize Jaccard Index (JI) directly. Extensive
experiments are conducted to validate our proposed deep models, on quantitative
pancreas segmentation using both CT and MRI scans. Our method outperforms the
state-of-the-art work on CT [11] and MRI pancreas segmentation [1],
respectively.Comment: 8 pages, 7 figures, accepted to Medical Image Computing and Computer
Assisted Interventions Conference (MICCAI) 201
Densely Dilated Spatial Pooling Convolutional Network using benign loss functions for imbalanced volumetric prostate segmentation
The high incidence rate of prostate disease poses a requirement in early
detection for diagnosis. As one of the main imaging methods used for prostate
cancer detection, Magnetic Resonance Imaging (MRI) has wide range of appearance
and imbalance problems, making automated prostate segmentation fundamental but
challenging. Here we propose a novel Densely Dilated Spatial Pooling
Convolutional Network (DDSP ConNet) in encoder-decoder structure. It employs
dense structure to combine dilated convolution and global pooling, thus
supplies coarse segmentation results from encoder and decoder subnet and
preserves more contextual information. To obtain richer hierarchical feature
maps, residual long connection is furtherly adopted to fuse contexture
features. Meanwhile, we adopt DSC loss and Jaccard loss functions to train our
DDSP ConNet. We surprisingly found and proved that, in contrast to re-weighted
cross entropy, DSC loss and Jaccard loss have a lot of benign properties in
theory, including symmetry, continuity and differentiability about the
parameters of network. Extensive experiments on the MICCAI PROMISE12 challenge
dataset have been done to corroborate the effectiveness of our DDSP ConNet with
DSC loss and Jaccard loss. Totally, our method achieves a score of 85.78 in the
test dataset, outperforming most of other competitors.Comment: 14pages, 5 figures, anonymous review in IJACAI201
Abdominal multi-organ segmentation with organ-attention networks and statistical fusion
Accurate and robust segmentation of abdominal organs on CT is essential for
many clinical applications such as computer-aided diagnosis and computer-aided
surgery. But this task is challenging due to the weak boundaries of organs, the
complexity of the background, and the variable sizes of different organs. To
address these challenges, we introduce a novel framework for multi-organ
segmentation by using organ-attention networks with reverse connections
(OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output
estimates which are combined by statistical fusion exploiting structural
similarity. OAN is a two-stage deep convolutional network, where deep network
features from the first stage are combined with the original image, in a second
stage, to reduce the complex background and enhance the discriminative
information for the target organs. RCs are added to the first stage to give the
lower layers semantic information thereby enabling them to adapt to the sizes
of different organs. Our networks are trained on 2D views enabling us to use
holistic information and allowing efficient computation. To compensate for the
limited cross-sectional information of the original 3D volumetric CT,
multi-sectional images are reconstructed from the three different 2D view
directions. Then we combine the segmentation results from the different views
using statistical fusion, with a novel term relating the structural similarity
of the 2D views to the original 3D structure. To train the network and evaluate
results, 13 structures were manually annotated by four human raters and
confirmed by a senior expert on 236 normal cases. We tested our algorithm and
computed Dice-Sorensen similarity coefficients and surface distances for
evaluating our estimates of the 13 structures. Our experiments show that the
proposed approach outperforms 2D- and 3D-patch based state-of-the-art methods.Comment: 21 pages, 11 figure
Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning
Automatic pancreas segmentation in radiology images, eg., computed tomography
(CT) and magnetic resonance imaging (MRI), is frequently required by
computer-aided screening, diagnosis, and quantitative assessment. Yet pancreas
is a challenging abdominal organ to segment due to the high inter-patient
anatomical variability in both shape and volume metrics. Recently,
convolutional neural networks (CNNs) have demonstrated promising performance on
accurate segmentation of pancreas. However, the CNN-based method often suffers
from segmentation discontinuity for reasons such as noisy image quality and
blurry pancreatic boundary. From this point, we propose to introduce recurrent
neural networks (RNNs) to address the problem of spatial non-smoothness of
inter-slice pancreas segmentation across adjacent image slices. To inference
initial segmentation, we first train a 2D CNN sub-network, where we modify its
network architecture with deep-supervision and multi-scale feature map
aggregation so that it can be trained from scratch with small-sized training
data and presents superior performance than transferred models. Thereafter, the
successive CNN outputs are processed by another RNN sub-network, which refines
the consistency of segmented shapes. More specifically, the RNN sub-network
consists convolutional long short-term memory (CLSTM) units in both top-down
and bottom-up directions, which regularizes the segmentation of an image by
integrating predictions of its neighboring slices. We train the stacked CNN-RNN
model end-to-end and perform quantitative evaluations on both CT and MRI
images
Accurate Automatic Segmentation of Amygdala Subnuclei and Modeling of Uncertainty via Bayesian Fully Convolutional Neural Network
Recent advances in deep learning have improved the segmentation accuracy of
subcortical brain structures, which would be useful in neuroimaging studies of
many neurological disorders. However, most of the previous deep learning work
does not investigate the specific difficulties that exist in segmenting
extremely small but important brain regions such as the amygdala and its
subregions. To tackle this challenging task, a novel 3D Bayesian fully
convolutional neural network was developed to apply a dilated dualpathway
approach that retains fine details and utilizes both local and more global
contextual information to automatically segment the amygdala and its subregions
at high precision. The proposed method provides insights on network design and
sampling strategy that target segmentations of small 3D structures. In
particular, this study confirms that a large context, enabled by a large field
of view, is beneficial for segmenting small objects; furthermore, precise
contextual information enabled by dilated convolutions allows for better
boundary localization, which is critical for examining the morphology of the
structure. In addition, it is demonstrated that the uncertainty information
estimated from our network may be leveraged to identify atypicality in data.
Our method was compared with two state-of-the-art deep learning models and a
traditional multi-atlas approach, and exhibited excellent performance as
measured both by Dice overlap as well as average symmetric surface distance. To
the best of our knowledge, this work is the first deep learning-based approach
that targets the subregions of the amygdala
Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets: Application to IsoIntense Infant Brain MRI Segmentation
The most recent fast and accurate image segmentation methods are built upon
fully convolutional deep neural networks. In this paper, we propose new deep
learning strategies for DenseNets to improve segmenting images with subtle
differences in intensity values and features. We aim to segment brain tissue on
infant brain MRI at about 6 months of age where white matter and gray matter of
the developing brain show similar T1 and T2 relaxation times, thus appear to
have similar intensity values on both T1- and T2-weighted MRI scans. Brain
tissue segmentation at this age is, therefore, very challenging. To this end,
we propose an exclusive multi-label training strategy to segment the mutually
exclusive brain tissues with similarity loss functions that automatically
balance the training based on class prevalence. Using our proposed training
strategy based on similarity loss functions and patch prediction fusion we
decrease the number of parameters in the network, reduce the complexity of the
training process focusing the attention on less number of tasks, while
mitigating the effects of data imbalance between labels and inaccuracies near
patch borders. By taking advantage of these strategies we were able to perform
fast image segmentation (90 seconds per 3D volume), using a network with less
parameters than many state-of-the-art networks, overcoming issues such as
3Dvs2D training and large vs small patch size selection, while achieving the
top performance in segmenting brain tissue among all methods tested in first
and second round submissions of the isointense infant brain MRI segmentation
(iSeg) challenge according to the official challenge test results. Our proposed
strategy improves the training process through balanced training and by
reducing its complexity while providing a trained model that works for any size
input image and is fast and more accurate than many state-of-the-art methods
Scale-Invariant Structure Saliency Selection for Fast Image Fusion
In this paper, we present a fast yet effective method for pixel-level
scale-invariant image fusion in spatial domain based on the scale-space theory.
Specifically, we propose a scale-invariant structure saliency selection scheme
based on the difference-of-Gaussian (DoG) pyramid of images to build the
weights or activity map. Due to the scale-invariant structure saliency
selection, our method can keep both details of small size objects and the
integrity information of large size objects in images. In addition, our method
is very efficient since there are no complex operation involved and easy to be
implemented and therefore can be used for fast high resolution images fusion.
Experimental results demonstrate the proposed method yields competitive or even
better results comparing to state-of-the-art image fusion methods both in terms
of visual quality and objective evaluation metrics. Furthermore, the proposed
method is very fast and can be used to fuse the high resolution images in
real-time. Code is available at https://github.com/yiqingmy/Fusion
A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling
Robust automated organ segmentation is a prerequisite for computer-aided
diagnosis (CAD), quantitative imaging analysis and surgical assistance. For
high-variability organs such as the pancreas, previous approaches report
undesirably low accuracies. We present a bottom-up approach for pancreas
segmentation in abdominal CT scans that is based on a hierarchy of information
propagation by classifying image patches at different resolutions; and
cascading superpixels. There are four stages: 1) decomposing CT slice images as
a set of disjoint boundary-preserving superpixels; 2) computing pancreas class
probability maps via dense patch labeling; 3) classifying superpixels by
pooling both intensity and probability features to form empirical statistics in
cascaded random forest frameworks; and 4) simple connectivity based
post-processing. The dense image patch labeling are conducted by: efficient
random forest classifier on image histogram, location and texture features; and
more expensive (but with better specificity) deep convolutional neural network
classification on larger image windows (with more spatial contexts). Evaluation
of the approach is performed on a database of 80 manually segmented CT volumes
in six-fold cross-validation (CV). Our achieved results are comparable, or
better than the state-of-the-art methods (evaluated by
"leave-one-patient-out"), with Dice 70.7% and Jaccard 57.9%. The computational
efficiency has been drastically improved in the order of 6~8 minutes, comparing
with others of ~10 hours per case. Finally, we implement a multi-atlas label
fusion (MALF) approach for pancreas segmentation using the same datasets. Under
six-fold CV, our bottom-up segmentation method significantly outperforms its
MALF counterpart: (70.7 +/- 13.0%) versus (52.5 +/- 20.8%) in Dice. Deep CNN
patch labeling confidences offer more numerical stability, reflected by smaller
standard deviations.Comment: 14 pages, 14 figures, 2 table
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
Detailed whole brain segmentation is an essential quantitative technique,
which provides a non-invasive way of measuring brain regions from a structural
magnetic resonance imaging (MRI). Recently, deep convolution neural network
(CNN) has been applied to whole brain segmentation. However, restricted by
current GPU memory, 2D based methods, downsampling based 3D CNN methods, and
patch-based high-resolution 3D CNN methods have been the de facto standard
solutions. 3D patch-based high resolution methods typically yield superior
performance among CNN approaches on detailed whole brain segmentation (>100
labels), however, whose performance are still commonly inferior compared with
multi-atlas segmentation methods (MAS) due to the following challenges: (1) a
single network is typically used to learn both spatial and contextual
information for the patches, (2) limited manually traced whole brain volumes
are available (typically less than 50) for training a network. In this work, we
propose the spatially localized atlas network tiles (SLANT) method to
distribute multiple independent 3D fully convolutional networks (FCN) for
high-resolution whole brain segmentation. To address the first challenge,
multiple spatially distributed networks were used in the SLANT method, in which
each network learned contextual information for a fixed spatial location. To
address the second challenge, auxiliary labels on 5111 initially unlabeled
scans were created by multi-atlas segmentation for training. Since the method
integrated multiple traditional medical image processing methods with deep
learning, we developed a containerized pipeline to deploy the end-to-end
solution. From the results, the proposed method achieved superior performance
compared with multi-atlas segmentation methods, while reducing the
computational time from >30 hours to 15 minutes
(https://github.com/MASILab/SLANTbrainSeg)
Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) For Brain Matter Segmentation
Automation of brain matter segmentation from MR images is a challenging task
due to the irregular boundaries between the grey and white matter regions. In
addition, the presence of intensity inhomogeneity in the MR images further
complicates the problem. In this paper, we propose a texture and vesselness
incorporated version of the ScatterNet Hybrid Deep Learning Network (TS-SHDL)
that extracts hierarchical invariant mid-level features, used by fisher vector
encoding and a conditional random field (CRF) to perform the desired
segmentation. The performance of the proposed network is evaluated by extensive
experimentation and comparison with the state-of-the-art methods on several 2D
MRI scans taken from the synthetic McGill Brain Web as well as on the MRBrainS
dataset of real 3D MRI scans. The advantages of the TS-SHDL network over
supervised deep learning networks is also presented in addition to its superior
performance over the state-of-the-art.Comment: To Appear in the IEEE International Conference on Computer Vision
Workshops (ICCVW) 201
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