704 research outputs found
Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images
The automatic segmentation of human knee cartilage from 3D MR images is a
useful yet challenging task due to the thin sheet structure of the cartilage
with diffuse boundaries and inhomogeneous intensities. In this paper, we
present an iterative multi-class learning method to segment the femoral, tibial
and patellar cartilage simultaneously, which effectively exploits the spatial
contextual constraints between bone and cartilage, and also between different
cartilages. First, based on the fact that the cartilage grows in only certain
area of the corresponding bone surface, we extract the distance features of not
only to the surface of the bone, but more informatively, to the densely
registered anatomical landmarks on the bone surface. Second, we introduce a set
of iterative discriminative classifiers that at each iteration, probability
comparison features are constructed from the class confidence maps derived by
previously learned classifiers. These features automatically embed the semantic
context information between different cartilages of interest. Validated on a
total of 176 volumes from the Osteoarthritis Initiative (OAI) dataset, the
proposed approach demonstrates high robustness and accuracy of segmentation in
comparison with existing state-of-the-art MR cartilage segmentation methods.Comment: MICCAI 2013: Workshop on Medical Computer Visio
Unbiased Shape Compactness for Segmentation
We propose to constrain segmentation functionals with a dimensionless,
unbiased and position-independent shape compactness prior, which we solve
efficiently with an alternating direction method of multipliers (ADMM).
Involving a squared sum of pairwise potentials, our prior results in a
challenging high-order optimization problem, which involves dense (fully
connected) graphs. We split the problem into a sequence of easier sub-problems,
each performed efficiently at each iteration: (i) a sparse-matrix inversion
based on Woodbury identity, (ii) a closed-form solution of a cubic equation and
(iii) a graph-cut update of a sub-modular pairwise sub-problem with a sparse
graph. We deploy our prior in an energy minimization, in conjunction with a
supervised classifier term based on CNNs and standard regularization
constraints. We demonstrate the usefulness of our energy in several medical
applications. In particular, we report comprehensive evaluations of our fully
automated algorithm over 40 subjects, showing a competitive performance for the
challenging task of abdominal aorta segmentation in MRI.Comment: Accepted at MICCAI 201
Simultaneous Segmentation and Filtering Via Reduced Graph Cuts
12 pagesInternational audienceRecently, optimization with graph cuts became very attractive but generally remains limited to small-scale problems due to the large memory requirement of graphs, even when restricted to binary variables. Unlike previous heuristics which generally fail to fully capture details, another band-based method was proposed for reducing these graphs in image segmentation. This method provides small graphs while preserving thin structures but do not offer low memory usage when the amount of regularization is large. This is typically the case when images are corrupted by an impulsive noise. In this paper, we overcome this situation by embedding a new parameter in this method to both further reducing graphs and filtering the segmentation. This parameter avoids any post-processing steps, appears to be generally less sensitive to noise variations and offers a good robustness against noise. We also provide an empirical way to automatically tune this parameter and illustrate its behavior for segmenting grayscale and color images
Prior-based Coregistration and Cosegmentation
We propose a modular and scalable framework for dense coregistration and
cosegmentation with two key characteristics: first, we substitute ground truth
data with the semantic map output of a classifier; second, we combine this
output with population deformable registration to improve both alignment and
segmentation. Our approach deforms all volumes towards consensus, taking into
account image similarities and label consistency. Our pipeline can incorporate
any classifier and similarity metric. Results on two datasets, containing
annotations of challenging brain structures, demonstrate the potential of our
method.Comment: The first two authors contributed equall
Using strong shape priors for stereo
Abstract. This paper addresses the problem of obtaining an accurate 3D reconstruction from multiple views. Taking inspiration from the recent successes of using strong prior knowledge for image segmentation, we propose a framework for 3D reconstruction which uses such priors to overcome the ambiguity inherent in this problem. Our framework is based on an object-specific Markov Random Field (MRF)[10]. It uses a volumetric scene representation and integrates conventional reconstruction measures such as photo-consistency, surface smoothness and visual hull membership with a strong object-specific prior. Simple parametric models of objects will be used as strong priors in our framework. We will show how parameters of these models can be efficiently estimated by performing inference on the MRF using dynamic graph cuts [7]. This procedure not only gives an accurate object reconstruction, but also provides us with information regarding the pose or state of the object being reconstructed. We will show the results of our method in reconstructing deformable and articulated objects.
Generalized Multi-Camera Scene Reconstruction Using Graph Cuts
Reconstructing a 3-D scene from more than one camera is a classical problem in computer vision. One of the major sources of difficulty is the fact that not all scene elements are visible from all cameras. In the last few years, two promising approaches have been developed [. . .] that formulate the scene reconstruction problem in terms of energy minimization, and minimize the energy using graph cuts. These energy minimization approaches treat the input images symmetrically, handle visibility constraints correctly, and allow spatial smoothness to be enforced. However, these algorithm propose different problem formulations, and handle a limited class of smoothness terms. One algorithm [. . .] uses a problem formulation that is restricted to two-camera stereo, and imposes smoothness between a pair of cameras. The other algorithm [. . .] can handle an arbitrary number of cameras, but imposes smoothness only with respect to a single camera. In this paper we give a more general energy minimization formulation for the problem, which allows a larger class of spatial smoothness constraints. We show that our formulation includes both of the previous approaches as special cases, as well as permitting new energy functions. Experimental results on real data with ground truth are also included.Engineering and Applied Science
Iterative graph cuts for image segmentation with a nonlinear statistical shape prior
Shape-based regularization has proven to be a useful method for delineating
objects within noisy images where one has prior knowledge of the shape of the
targeted object. When a collection of possible shapes is available, the
specification of a shape prior using kernel density estimation is a natural
technique. Unfortunately, energy functionals arising from kernel density
estimation are of a form that makes them impossible to directly minimize using
efficient optimization algorithms such as graph cuts. Our main contribution is
to show how one may recast the energy functional into a form that is
minimizable iteratively and efficiently using graph cuts.Comment: Revision submitted to JMIV (02/24/13
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions
Structured-output learning is a challenging problem; particularly so because
of the difficulty in obtaining large datasets of fully labelled instances for
training. In this paper we try to overcome this difficulty by presenting a
multi-utility learning framework for structured prediction that can learn from
training instances with different forms of supervision. We propose a unified
technique for inferring the loss functions most suitable for quantifying the
consistency of solutions with the given weak annotation. We demonstrate the
effectiveness of our framework on the challenging semantic image segmentation
problem for which a wide variety of annotations can be used. For instance, the
popular training datasets for semantic segmentation are composed of images with
hard-to-generate full pixel labellings, as well as images with easy-to-obtain
weak annotations, such as bounding boxes around objects, or image-level labels
that specify which object categories are present in an image. Experimental
evaluation shows that the use of annotation-specific loss functions
dramatically improves segmentation accuracy compared to the baseline system
where only one type of weak annotation is used
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