5,110 research outputs found
An Optimized PatchMatch for Multi-scale and Multi-feature Label Fusion
Automatic segmentation methods are important tools for quantitative analysis
of Magnetic Resonance Images (MRI). Recently, patch-based label fusion
approaches have demonstrated state-of-the-art segmentation accuracy. In this
paper, we introduce a new patch-based label fusion framework to perform
segmentation of anatomical structures. The proposed approach uses an Optimized
PAtchMatch Label fusion (OPAL) strategy that drastically reduces the
computation time required for the search of similar patches. The reduced
computation time of OPAL opens the way for new strategies and facilitates
processing on large databases. In this paper, we investigate new perspectives
offered by OPAL, by introducing a new multi-scale and multi-feature framework.
During our validation on hippocampus segmentation we use two datasets: young
adults in the ICBM cohort and elderly adults in the EADC-ADNI dataset. For
both, OPAL is compared to state-of-the-art methods. Results show that OPAL
obtained the highest median Dice coefficient (89.9% for ICBM and 90.1% for
EADC-ADNI). Moreover, in both cases, OPAL produced a segmentation accuracy
similar to inter-expert variability. On the EADC-ADNI dataset, we compare the
hippocampal volumes obtained by manual and automatic segmentation. The volumes
appear to be highly correlated that enables to perform more accurate separation
of pathological populations.Comment: Neuroimage 201
Segmentation of Levator Hiatus Using Multi-Scale Local Region Active contours and Boundary Shape Similarity Constraint
In this paper, a multi-scale framework with local region based active contour
and boundary shape similarity constraint is proposed for the segmentation of
levator hiatus in ultrasound images. In this paper, we proposed a multiscale
active contour framework to segment levator hiatus ultrasound images by
combining the local region information and boundary shape similarity
constraint. In order to get more precisely initializations and reduce the
computational cost, Gaussian pyramid method is used to decompose the image into
coarse-to-fine scales. A localized region active contour model is firstly
performed on the coarsest scale image to get a rough contour of the levator
hiatus, then the segmentation result on the coarse scale is interpolated into
the finer scale image as the initialization. The boundary shape similarity
between different scales is incorporate into the local region based active
contour model so that the result from coarse scale can guide the contour
evolution at finer scale. By incorporating the multi-scale and boundary shape
similarity, the proposed method can precisely locate the levator hiatus
boundaries despite various ultrasound image artifacts. With a data set of 90
levator hiatus ultrasound images, the efficiency and accuracy of the proposed
method are validated by quantitative and qualitative evaluations (TP, FP, Js)
and comparison with other two state-of-art active contour segmentation methods
(C-V model, DRLSE model)
Incorporating prior knowledge in medical image segmentation: a survey
Medical image segmentation, the task of partitioning an image into meaningful
parts, is an important step toward automating medical image analysis and is at
the crux of a variety of medical imaging applications, such as computer aided
diagnosis, therapy planning and delivery, and computer aided interventions.
However, the existence of noise, low contrast and objects' complexity in
medical images are critical obstacles that stand in the way of achieving an
ideal segmentation system. Incorporating prior knowledge into image
segmentation algorithms has proven useful for obtaining more accurate and
plausible results. This paper surveys the different types of prior knowledge
that have been utilized in different segmentation frameworks. We focus our
survey on optimization-based methods that incorporate prior information into
their frameworks. We review and compare these methods in terms of the types of
prior employed, the domain of formulation (continuous vs. discrete), and the
optimization techniques (global vs. local). We also created an interactive
online database of existing works and categorized them based on the type of
prior knowledge they use. Our website is interactive so that researchers can
contribute to keep the database up to date. We conclude the survey by
discussing different aspects of designing an energy functional for image
segmentation, open problems, and future perspectives.Comment: Survey paper, 30 page
Localizing Region-Based Active Contours
©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2008.2004611In this paper, we propose a natural framework that allows any region-based segmentation energy to be re-formulated in a local way. We consider local rather than global image statistics and evolve a contour based on local information. Localized contours are capable of segmenting objects with heterogeneous feature profiles that would be difficult to capture correctly using a standard global method. The presented technique is versatile enough to be used with any global region-based active contour energy and instill in it the benefits of localization. We describe this framework and demonstrate the localization of three well-known energies in order to illustrate how our framework can be applied to any energy. We then compare each localized energy to its global counterpart to show the improvements that can be achieved. Next, an in-depth study of the behaviors of these energies in response to the degree of localization is given. Finally, we show results on challenging images to illustrate the robust and accurate segmentations that are possible with this new class of active contour models
A deep level set method for image segmentation
This paper proposes a novel image segmentation approachthat integrates fully
convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the
integrated method can incorporatesmoothing and prior information to achieve an
accurate segmentation.Furthermore, different than using the level set model as
a post-processingtool, we integrate it into the training phase to fine-tune the
FCN. Thisallows the use of unlabeled data during training in a
semi-supervisedsetting. Using two types of medical imaging data (liver CT and
left ven-tricle MRI data), we show that the integrated method achieves
goodperformance even when little training data is available, outperformingthe
FCN or the level set model alone
Boundary-Aware Network for Fast and High-Accuracy Portrait Segmentation
Compared with other semantic segmentation tasks, portrait segmentation
requires both higher precision and faster inference speed. However, this
problem has not been well studied in previous works. In this paper, we propose
a lightweight network architecture, called Boundary-Aware Network (BANet) which
selectively extracts detail information in boundary area to make high-quality
segmentation output with real-time( >25FPS) speed. In addition, we design a new
loss function called refine loss which supervises the network with image level
gradient information. Our model is able to produce finer segmentation results
which has richer details than annotations
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration
With an aim to increase the capture range and accelerate the performance of
state-of-the-art inter-subject and subject-to-template 3D registration, we
propose deep learning-based methods that are trained to find the 3D position of
arbitrarily oriented subjects or anatomy based on slices or volumes of medical
images. For this, we propose regression CNNs that learn to predict the
angle-axis representation of 3D rotations and translations using image
features. We use and compare mean square error and geodesic loss to train
regression CNNs for 3D pose estimation used in two different scenarios:
slice-to-volume registration and volume-to-volume registration. Our results
show that in such registration applications that are amendable to learning, the
proposed deep learning methods with geodesic loss minimization can achieve
accurate results with a wide capture range in real-time (<100ms). We also
tested the generalization capability of the trained CNNs on an expanded age
range and on images of newborn subjects with similar and different MR image
contrasts. We trained our models on T2-weighted fetal brain MRI scans and used
them to predict the 3D pose of newborn brains based on T1-weighted MRI scans.
We showed that the trained models generalized well for the new domain when we
performed image contrast transfer through a conditional generative adversarial
network. This indicates that the domain of application of the trained deep
regression CNNs can be further expanded to image modalities and contrasts other
than those used in training. A combination of our proposed methods with
accelerated optimization-based registration algorithms can dramatically enhance
the performance of automatic imaging devices and image processing methods of
the future.Comment: This work has been submitted to TM
Multi-region segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks
Precise segmentation of bladder walls and tumor regions is an essential step
towards non-invasive identification of tumor stage and grade, which is critical
for treatment decision and prognosis of patients with bladder cancer (BC).
However, the automatic delineation of bladder walls and tumor in magnetic
resonance images (MRI) is a challenging task, due to important bladder shape
variations, strong intensity inhomogeneity in urine and very high variability
across population, particularly on tumors appearance. To tackle these issues,
we propose to use a deep fully convolutional neural network. The proposed
network includes dilated convolutions to increase the receptive field without
incurring extra cost nor degrading its performance. Furthermore, we introduce
progressive dilations in each convolutional block, thereby enabling extensive
receptive fields without the need for large dilation rates. The proposed
network is evaluated on 3.0T T2-weighted MRI scans from 60 pathologically
confirmed patients with BC. Experiments shows the proposed model to achieve
high accuracy, with a mean Dice similarity coefficient of 0.98, 0.84 and 0.69
for inner wall, outer wall and tumor region, respectively. These results
represent a very good agreement with reference contours and an increase in
performance compared to existing methods. In addition, inference times are less
than a second for a whole 3D volume, which is between 2-3 orders of magnitude
faster than related state-of-the-art methods for this application. We showed
that a CNN can yield precise segmentation of bladder walls and tumors in
bladder cancer patients on MRI. The whole segmentation process is
fully-automatic and yields results in very good agreement with the reference
standard, demonstrating the viability of deep learning models for the automatic
multi-region segmentation of bladder cancer MRI images.Comment: Published at the journal of Medical Physic
SEGCloud: Semantic Segmentation of 3D Point Clouds
3D semantic scene labeling is fundamental to agents operating in the real
world. In particular, labeling raw 3D point sets from sensors provides
fine-grained semantics. Recent works leverage the capabilities of Neural
Networks (NNs), but are limited to coarse voxel predictions and do not
explicitly enforce global consistency. We present SEGCloud, an end-to-end
framework to obtain 3D point-level segmentation that combines the advantages of
NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields
(FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are
transferred back to the raw 3D points via trilinear interpolation. Then the
FC-CRF enforces global consistency and provides fine-grained semantics on the
points. We implement the latter as a differentiable Recurrent NN to allow joint
optimization. We evaluate the framework on two indoor and two outdoor 3D
datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance
comparable or superior to the state-of-the-art on all datasets.Comment: Accepted as a spotlight at the International Conference of 3D Vision
(3DV 2017
A deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging
We propose a novel automatic method for accurate segmentation of the prostate
in T2-weighted magnetic resonance imaging (MRI). Our method is based on
convolutional neural networks (CNNs). Because of the large variability in the
shape, size, and appearance of the prostate and the scarcity of annotated
training data, we suggest training two separate CNNs. A global CNN will
determine a prostate bounding box, which is then resampled and sent to a local
CNN for accurate delineation of the prostate boundary. This way, the local CNN
can effectively learn to segment the fine details that distinguish the prostate
from the surrounding tissue using the small amount of available training data.
To fully exploit the training data, we synthesize additional data by deforming
the training images and segmentations using a learned shape model. We apply the
proposed method on the PROMISE12 challenge dataset and achieve state of the art
results. Our proposed method generates accurate, smooth, and artifact-free
segmentations. On the test images, we achieve an average Dice score of 90.6
with a small standard deviation of 2.2, which is superior to all previous
methods. Our two-step segmentation approach and data augmentation strategy may
be highly effective in segmentation of other organs from small amounts of
annotated medical images
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