12,393 research outputs found
Simultaneous Multiple Surface Segmentation Using Deep Learning
The task of automatically segmenting 3-D surfaces representing boundaries of
objects is important for quantitative analysis of volumetric images, and plays
a vital role in biomedical image analysis. Recently, graph-based methods with a
global optimization property have been developed and optimized for various
medical imaging applications. Despite their widespread use, these require human
experts to design transformations, image features, surface smoothness priors,
and re-design for a different tissue, organ or imaging modality. Here, we
propose a Deep Learning based approach for segmentation of the surfaces in
volumetric medical images, by learning the essential features and
transformations from training data, without any human expert intervention. We
employ a regional approach to learn the local surface profiles. The proposed
approach was evaluated on simultaneous intraretinal layer segmentation of
optical coherence tomography (OCT) images of normal retinas and retinas
affected by age related macular degeneration (AMD). The proposed approach was
validated on 40 retina OCT volumes including 20 normal and 20 AMD subjects. The
experiments showed statistically significant improvement in accuracy for our
approach compared to state-of-the-art graph based optimal surface segmentation
with convex priors (G-OSC). A single Convolution Neural Network (CNN) was used
to learn the surfaces for both normal and diseased images. The mean unsigned
surface positioning errors obtained by G-OSC method 2.31 voxels (95% CI
2.02-2.60 voxels) was improved to voxels (95% CI 1.14-1.40 voxels) using
our new approach. On average, our approach takes 94.34 s, requiring 95.35 MB
memory, which is much faster than the 2837.46 s and 6.87 GB memory required by
the G-OSC method on the same computer system.Comment: 8 page
Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene
The goal of this paper is to take a single 2D image of a scene and recover
the 3D structure in terms of a small set of factors: a layout representing the
enclosing surfaces as well as a set of objects represented in terms of shape
and pose. We propose a convolutional neural network-based approach to predict
this representation and benchmark it on a large dataset of indoor scenes. Our
experiments evaluate a number of practical design questions, demonstrate that
we can infer this representation, and quantitatively and qualitatively
demonstrate its merits compared to alternate representations.Comment: Project url with code: https://shubhtuls.github.io/factored3
3D Well-composed Polyhedral Complexes
A binary three-dimensional (3D) image is well-composed if the boundary
surface of its continuous analog is a 2D manifold. Since 3D images are not
often well-composed, there are several voxel-based methods ("repairing"
algorithms) for turning them into well-composed ones but these methods either
do not guarantee the topological equivalence between the original image and its
corresponding well-composed one or involve sub-sampling the whole image.
In this paper, we present a method to locally "repair" the cubical complex
(embedded in ) associated to to obtain a polyhedral
complex homotopy equivalent to such that the boundary of every
connected component of is a 2D manifold. The reparation is performed via
a new codification system for under the form of a 3D grayscale image
that allows an efficient access to cells and their faces
Hybrid model for vascular tree structures
This paper proposes a new representation scheme of the cerebral blood
vessels. This model provides information on the semantics of the
vascular structure: the topological relationships between vessels and
the labeling of vascular accidents such as aneurysms and stenoses.
In addition, the model keeps information of the inner surface geometry
as well as of the vascular map volume properties, i.e. the tissue
density, the blood flow velocity and the vessel wall elasticity.
The model can be constructed automatically in a pre-process from a set
of segmented MRA images. Its memory requirements are optimized on the
basis of the sparseness of the vascular structure. It allows fast
queries and efficient traversals and navigations. The visualizations
of the vessel surface can be performed at different levels of
detail. The direct rendering of the volume is fast because the model
provides a natural way to skip over empty data.
The paper analyzes the memory requirements of the model along with the
costs of the most important operations on it.Postprint (published version
Path-tracing Monte Carlo Library for 3D Radiative Transfer in Highly Resolved Cloudy Atmospheres
Interactions between clouds and radiation are at the root of many
difficulties in numerically predicting future weather and climate and in
retrieving the state of the atmosphere from remote sensing observations. The
large range of issues related to these interactions, and in particular to
three-dimensional interactions, motivated the development of accurate radiative
tools able to compute all types of radiative metrics, from monochromatic, local
and directional observables, to integrated energetic quantities. In the
continuity of this community effort, we propose here an open-source library for
general use in Monte Carlo algorithms. This library is devoted to the
acceleration of path-tracing in complex data, typically high-resolution
large-domain grounds and clouds. The main algorithmic advances embedded in the
library are those related to the construction and traversal of hierarchical
grids accelerating the tracing of paths through heterogeneous fields in
null-collision (maximum cross-section) algorithms. We show that with these
hierarchical grids, the computing time is only weakly sensitivive to the
refinement of the volumetric data. The library is tested with a rendering
algorithm that produces synthetic images of cloud radiances. Two other examples
are given as illustrations, that are respectively used to analyse the
transmission of solar radiation under a cloud together with its sensitivity to
an optical parameter, and to assess a parametrization of 3D radiative effects
of clouds.Comment: Submitted to JAMES, revised and submitted again (this is v2
Volume-Enclosing Surface Extraction
In this paper we present a new method, which allows for the construction of
triangular isosurfaces from three-dimensional data sets, such as 3D image data
and/or numerical simulation data that are based on regularly shaped, cubic
lattices. This novel volume-enclosing surface extraction technique, which has
been named VESTA, can produce up to six different results due to the nature of
the discretized 3D space under consideration. VESTA is neither template-based
nor it is necessarily required to operate on 2x2x2 voxel cell neighborhoods
only. The surface tiles are determined with a very fast and robust construction
technique while potential ambiguities are detected and resolved. Here, we
provide an in-depth comparison between VESTA and various versions of the
well-known and very popular Marching Cubes algorithm for the very first time.
In an application section, we demonstrate the extraction of VESTA isosurfaces
for various data sets ranging from computer tomographic scan data to simulation
data of relativistic hydrodynamic fireball expansions.Comment: 24 pages, 33 figures, 4 tables, final versio
Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies
In motion analysis and understanding it is important to be able to fit a
suitable model or structure to the temporal series of observed data, in order
to describe motion patterns in a compact way, and to discriminate between them.
In an unsupervised context, i.e., no prior model of the moving object(s) is
available, such a structure has to be learned from the data in a bottom-up
fashion. In recent times, volumetric approaches in which the motion is captured
from a number of cameras and a voxel-set representation of the body is built
from the camera views, have gained ground due to attractive features such as
inherent view-invariance and robustness to occlusions. Automatic, unsupervised
segmentation of moving bodies along entire sequences, in a temporally-coherent
and robust way, has the potential to provide a means of constructing a
bottom-up model of the moving body, and track motion cues that may be later
exploited for motion classification. Spectral methods such as locally linear
embedding (LLE) can be useful in this context, as they preserve "protrusions",
i.e., high-curvature regions of the 3D volume, of articulated shapes, while
improving their separation in a lower dimensional space, making them in this
way easier to cluster. In this paper we therefore propose a spectral approach
to unsupervised and temporally-coherent body-protrusion segmentation along time
sequences. Volumetric shapes are clustered in an embedding space, clusters are
propagated in time to ensure coherence, and merged or split to accommodate
changes in the body's topology. Experiments on both synthetic and real
sequences of dense voxel-set data are shown. This supports the ability of the
proposed method to cluster body-parts consistently over time in a totally
unsupervised fashion, its robustness to sampling density and shape quality, and
its potential for bottom-up model constructionComment: 31 pages, 26 figure
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