2,781 research outputs found
A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI
Segmentation of the left ventricle (LV) from cardiac magnetic resonance
imaging (MRI) datasets is an essential step for calculation of clinical indices
such as ventricular volume and ejection fraction. In this work, we employ deep
learning algorithms combined with deformable models to develop and evaluate a
fully automatic segmentation tool for the LV from short-axis cardiac MRI
datasets. The method employs deep learning algorithms to learn the segmentation
task from the ground true data. Convolutional networks are employed to
automatically detect the LV chamber in MRI dataset. Stacked autoencoders are
utilized to infer the shape of the LV. The inferred shape is incorporated into
deformable models to improve the accuracy and robustness of the segmentation.
We validated our method using 45 cardiac MR datasets taken from the MICCAI 2009
LV segmentation challenge and showed that it outperforms the state-of-the art
methods. Excellent agreement with the ground truth was achieved. Validation
metrics, percentage of good contours, Dice metric, average perpendicular
distance and conformity, were computed as 96.69%, 0.94, 1.81mm and 0.86, versus
those of 79.2%-95.62%, 0.87-0.9, 1.76-2.97mm and 0.67-0.78, obtained by other
methods, respectively.Comment: to appear in Medical Image Analysi
Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net
Segmentation of pancreas is important for medical image analysis, yet it
faces great challenges of class imbalance, background distractions and
non-rigid geometrical features. To address these difficulties, we introduce a
Deep Q Network(DQN) driven approach with deformable U-Net to accurately segment
the pancreas by explicitly interacting with contextual information and extract
anisotropic features from pancreas. The DQN based model learns a
context-adaptive localization policy to produce a visually tightened and
precise localization bounding box of the pancreas. Furthermore, deformable
U-Net captures geometry-aware information of pancreas by learning geometrically
deformable filters for feature extraction. Experiments on NIH dataset validate
the effectiveness of the proposed framework in pancreas segmentation.Comment: in IEEE Transactions on Medical Imaging (2019
cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey
The "cvpaper.challenge" is a group composed of members from AIST, Tokyo Denki
Univ. (TDU), and Univ. of Tsukuba that aims to systematically summarize papers
on computer vision, pattern recognition, and related fields. For this
particular review, we focused on reading the ALL 602 conference papers
presented at the CVPR2015, the premier annual computer vision event held in
June 2015, in order to grasp the trends in the field. Further, we are proposing
"DeepSurvey" as a mechanism embodying the entire process from the reading
through all the papers, the generation of ideas, and to the writing of paper.Comment: Survey Pape
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
Probabilistic atlas priors have been commonly used to derive adaptive and
robust brain MRI segmentation algorithms. Widely-used neuroimage analysis
pipelines rely heavily on these techniques, which are often computationally
expensive. In contrast, there has been a recent surge of approaches that
leverage deep learning to implement segmentation tools that are computationally
efficient at test time. However, most of these strategies rely on learning from
manually annotated images. These supervised deep learning methods are therefore
sensitive to the intensity profiles in the training dataset. To develop a deep
learning-based segmentation model for a new image dataset (e.g., of different
contrast), one usually needs to create a new labeled training dataset, which
can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or
augmentation approaches. In this paper, we propose an alternative strategy that
combines a conventional probabilistic atlas-based segmentation with deep
learning, enabling one to train a segmentation model for new MRI scans without
the need for any manually segmented images. Our experiments include thousands
of brain MRI scans and demonstrate that the proposed method achieves good
accuracy for a brain MRI segmentation task for different MRI contrasts,
requiring only approximately 15 seconds at test time on a GPU. The code is
freely available at http://voxelmorph.mit.edu.Comment: MICCAI 201
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
We introduce DeepNAT, a 3D Deep convolutional neural network for the
automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance
images. DeepNAT is an end-to-end learning-based approach to brain segmentation
that jointly learns an abstract feature representation and a multi-class
classification. We propose a 3D patch-based approach, where we do not only
predict the center voxel of the patch but also neighbors, which is formulated
as multi-task learning. To address a class imbalance problem, we arrange two
networks hierarchically, where the first one separates foreground from
background, and the second one identifies 25 brain structures on the
foreground. Since patches lack spatial context, we augment them with
coordinates. To this end, we introduce a novel intrinsic parameterization of
the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As
network architecture, we use three convolutional layers with pooling, batch
normalization, and non-linearities, followed by fully connected layers with
dropout. The final segmentation is inferred from the probabilistic output of
the network with a 3D fully connected conditional random field, which ensures
label agreement between close voxels. The roughly 2.7 million parameters in the
network are learned with stochastic gradient descent. Our results show that
DeepNAT compares favorably to state-of-the-art methods. Finally, the purely
learning-based method may have a high potential for the adaptation to young,
old, or diseased brains by fine-tuning the pre-trained network with a small
training sample on the target application, where the availability of larger
datasets with manual annotations may boost the overall segmentation accuracy in
the future.Comment: Accepted for publication in NeuroImage, special issue "Brain
Segmentation and Parcellation", 201
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
The Non-Local Network (NLNet) presents a pioneering approach for capturing
long-range dependencies, via aggregating query-specific global context to each
query position. However, through a rigorous empirical analysis, we have found
that the global contexts modeled by non-local network are almost the same for
different query positions within an image. In this paper, we take advantage of
this finding to create a simplified network based on a query-independent
formulation, which maintains the accuracy of NLNet but with significantly less
computation. We further observe that this simplified design shares similar
structure with Squeeze-Excitation Network (SENet). Hence we unify them into a
three-step general framework for global context modeling. Within the general
framework, we design a better instantiation, called the global context (GC)
block, which is lightweight and can effectively model the global context. The
lightweight property allows us to apply it for multiple layers in a backbone
network to construct a global context network (GCNet), which generally
outperforms both simplified NLNet and SENet on major benchmarks for various
recognition tasks. The code and configurations are released at
https://github.com/xvjiarui/GCNet
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Classical deformable registration techniques achieve impressive results and
offer a rigorous theoretical treatment, but are computationally intensive since
they solve an optimization problem for each image pair. Recently,
learning-based methods have facilitated fast registration by learning spatial
deformation functions. However, these approaches use restricted deformation
models, require supervised labels, or do not guarantee a diffeomorphic
(topology-preserving) registration. Furthermore, learning-based registration
tools have not been derived from a probabilistic framework that can offer
uncertainty estimates.
In this paper, we build a connection between classical and learning-based
methods. We present a probabilistic generative model and derive an unsupervised
learning-based inference algorithm that uses insights from classical
registration methods and makes use of recent developments in convolutional
neural networks (CNNs). We demonstrate our method on a 3D brain registration
task for both images and anatomical surfaces, and provide extensive empirical
analyses. Our principled approach results in state of the art accuracy and very
fast runtimes, while providing diffeomorphic guarantees. Our implementation is
available at http://voxelmorph.csail.mit.edu.Comment: MedIA: Medical Image Analysis (MICCAI2018 Special Issue). Expands on
MICCAI 2018 paper (arXiv:1805.04605) by introducing an extension to
anatomical surface registration, new experiments, and analysis of
diffeomorphic implementations. Keywords: medical image registration;
diffeomorphic; invertible; probabilistic modeling; variational inference.
Code available at http://voxelmorph.csail.mit.edu. arXiv admin note: text
overlap with arXiv:1805.0460
VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation
Deep learning (DL) approaches are state-of-the-art for many medical image
segmentation tasks. They offer a number of advantages: they can be trained for
specific tasks, computations are fast at test time, and segmentation quality is
typically high. In contrast, previously popular multi-atlas segmentation (MAS)
methods are relatively slow (as they rely on costly registrations) and even
though sophisticated label fusion strategies have been proposed, DL approaches
generally outperform MAS. In this work, we propose a DL-based label fusion
strategy (VoteNet) which locally selects a set of reliable atlases whose labels
are then fused via plurality voting. Experiments on 3D brain MRI data show that
by selecting a good initial atlas set MAS with VoteNet significantly
outperforms a number of other label fusion strategies as well as a direct DL
segmentation approach. We also provide an experimental analysis of the upper
performance bound achievable by our method. While unlikely achievable in
practice, this bound suggests room for further performance improvements.
Lastly, to address the runtime disadvantage of standard MAS, all our results
make use of a fast DL registration approach
Deep Learning for Generic Object Detection: A Survey
Object detection, one of the most fundamental and challenging problems in
computer vision, seeks to locate object instances from a large number of
predefined categories in natural images. Deep learning techniques have emerged
as a powerful strategy for learning feature representations directly from data
and have led to remarkable breakthroughs in the field of generic object
detection. Given this period of rapid evolution, the goal of this paper is to
provide a comprehensive survey of the recent achievements in this field brought
about by deep learning techniques. More than 300 research contributions are
included in this survey, covering many aspects of generic object detection:
detection frameworks, object feature representation, object proposal
generation, context modeling, training strategies, and evaluation metrics. We
finish the survey by identifying promising directions for future research.Comment: IJCV Mino
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In
2015 and 2016, we thoroughly study 1,600+ papers in several
conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV
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