7,793 research outputs found
Segmentation of Tubular Structures Using Iterative Training with Tailored Samples
We propose a minimal path method to simultaneously compute segmentation masks
and extract centerlines of tubular structures with line-topology. Minimal path
methods are commonly used for the segmentation of tubular structures in a wide
variety of applications. Recent methods use features extracted by CNNs, and
often outperform methods using hand-tuned features. However, for CNN-based
methods, the samples used for training may be generated inappropriately, so
that they can be very different from samples encountered during inference. We
approach this discrepancy by introducing a novel iterative training scheme,
which enables generating better training samples specifically tailored for the
minimal path methods without changing existing annotations. In our method,
segmentation masks and centerlines are not determined after one another by
post-processing, but obtained using the same steps. Our method requires only
very few annotated training images. Comparison with seven previous approaches
on three public datasets, including satellite images and medical images, shows
that our method achieves state-of-the-art results both for segmentation masks
and centerlines.Comment: Accepted to IEEE/CVF International Conference on Computer Vision
(ICCV), Paris, 202
A graph-based mathematical morphology reader
This survey paper aims at providing a "literary" anthology of mathematical
morphology on graphs. It describes in the English language many ideas stemming
from a large number of different papers, hence providing a unified view of an
active and diverse field of research
Minimal Path Methods for Segmentation and Analysis of 2D and 3D Line Structures
Image segmentation plays a vital role in many applications of computer vision. Segmentation is not only an important task in its own right, but also a prerequisite for many further image analysis steps. Consequently, segmentation is one of the most active research areas of computer vision. In this thesis, line structures are considered, which have quite different characteristics compared to common objects in natural 2D images: Line structures are much thinner and longer, and often they have little color or texture information such as blood vessels in medical images. To cope with these challenges, minimal path methods are commonly used. In this thesis, two new methods are introduced which are extensions of existing minimal path methods.
The first method is a novel hybrid approach for automatic 3D segmentation and quantification of high-resolution 7 Tesla magnetic resonance angiography (MRA) images of the human cerebral vasculature. Our approach consists of two main steps. First, a 3D model-based approach is used to segment and quantify thick vessels and most parts of thin vessels. Second, remaining vessel gaps of the first step in low-contrast and noisy regions are completed using a 3D minimal path approach, which exploits directional information. We present two novel minimal path approaches: The first is an explicit approach based on energy minimization using probabilistic sampling, and the second is an implicit approach based on fast marching with anisotropic directional prior.
The second method we introduce is a novel minimal path method for the segmentation of 2D and 3D line structures. Minimal path methods perform propagation of a wavefront emanating from a start point at a speed derived from image features, followed by path extraction using backtracing. Usually, the computation of the speed and the propagation of the wave are two separate steps, and point features are used to compute a static speed. We introduce a new continuous minimal path method which steers the wave propagation progressively using dynamic speed based on path features. We present three instances of our method, using an appearance feature of the path, a geometric feature based on the curvature of the path, and a joint appearance and geometric feature based on the tangent of the wavefront. Such features have not been used in previous continuous minimal path methods. We compute the features dynamically during the wave propagation, and also efficiently using a fast numerical scheme and a low-dimensional parameter space. Our method does not suffer from discretization or metrication errors.
We conducted quantitative and qualitative experimental evaluations of our methods using 2D and 3D images from different application areas, including synthetic images, retinal images, satellite images of streets, rivers, and bridges, and 3D 7T MRA images of human brain vessels
Dimensional Changes of Upper Airway after Rapid Maxillary Expansion: A Prospective Cone-beam Computed Tomography Study
Introduction: The aim of this prospective study was to use cone-beam computed tomography to assess the dimensional changes of the upper airway in orthodontic patients with maxillary constriction treated by rapid maxillary expansion.
Methods: Fourteen orthodontic patients (mean age, 12.9 years; range, 9.7-16 years) were recruited. The patients with posterior crossbite and constricted maxilla were treated with rapid maxillary expansion as the initial part of their comprehensive orthodontic treatments. Before and after rapid maxillary expansion conebeam computed tomography scans were taken to measure the retropalatal and retroglossal airway changes in terms of volume, and sagittal and cross-sectional areas. The transverse expansions by rapid maxillary expansion were assessed between the midlingual alveolar bone plates at the maxillary first molar and first premolar levels. The measurements of the before and after rapid maxillary expansion scans were compared by using paired t tests with the Bonferroni adjustment for multiple comparisons.
Results: After rapid maxillary expansion, significant and equal amounts of 4.8 mm of expansion were observed at the first molar (P 5 0.0000) and the first premolar (P 5 0.0000) levels. The width increase at the first premolar level (20.0%) was significantly greater than that at the first molar level (15.0%) (P 5 0.035). As the primary outcome variable, the cross-sectional airway measured from the posterior nasal spine to basion level was the only parameter showing a significant increase of 99.4 mm2 (59.6%) after rapid maxillary expansion (P 5 0.0004).
Conclusions: These results confirm the findings of previous studies of the effect of rapid maxillary expansion on the maxilla. Additionally, we found that only the cross-sectional area of the upper airway at the posterior nasal spine to basion level significantly gains a moderate increase after rapid maxillary expansion
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Coronary Artery Segmentation and Motion Modelling
Conventional coronary artery bypass surgery requires invasive sternotomy and the
use of a cardiopulmonary bypass, which leads to long recovery period and has high
infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery
based on image guided robotic surgical approaches have been developed to allow the
clinicians to conduct the bypass surgery off-pump with only three pin holes incisions
in the chest cavity, through which two robotic arms and one stereo endoscopic camera
are inserted. However, the restricted field of view of the stereo endoscopic images leads
to possible vessel misidentification and coronary artery mis-localization. This results
in 20-30% conversion rates from TECAB surgery to the conventional approach.
We have constructed patient-specific 3D + time coronary artery and left ventricle
motion models from preoperative 4D Computed Tomography Angiography (CTA)
scans. Through temporally and spatially aligning this model with the intraoperative
endoscopic views of the patient's beating heart, this work assists the surgeon to identify
and locate the correct coronaries during the TECAB precedures. Thus this work has
the prospect of reducing the conversion rate from TECAB to conventional coronary
bypass procedures.
This thesis mainly focus on designing segmentation and motion tracking methods
of the coronary arteries in order to build pre-operative patient-specific motion models.
Various vessel centreline extraction and lumen segmentation algorithms are presented,
including intensity based approaches, geometric model matching method and
morphology-based method. A probabilistic atlas of the coronary arteries is formed
from a group of subjects to facilitate the vascular segmentation and registration procedures.
Non-rigid registration framework based on a free-form deformation model
and multi-level multi-channel large deformation diffeomorphic metric mapping are
proposed to track the coronary motion. The methods are applied to 4D CTA images
acquired from various groups of patients and quantitatively evaluated
Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video
Thin structures, such as wire-frame sculptures, fences, cables, power lines,
and tree branches, are common in the real world. It is extremely challenging to
acquire their 3D digital models using traditional image-based or depth-based
reconstruction methods because thin structures often lack distinct point
features and have severe self-occlusion. We propose the first approach that
simultaneously estimates camera motion and reconstructs the geometry of complex
3D thin structures in high quality from a color video captured by a handheld
camera. Specifically, we present a new curve-based approach to estimate
accurate camera poses by establishing correspondences between featureless thin
objects in the foreground in consecutive video frames, without requiring visual
texture in the background scene to lock on. Enabled by this effective
curve-based camera pose estimation strategy, we develop an iterative
optimization method with tailored measures on geometry, topology as well as
self-occlusion handling for reconstructing 3D thin structures. Extensive
validations on a variety of thin structures show that our method achieves
accurate camera pose estimation and faithful reconstruction of 3D thin
structures with complex shape and topology at a level that has not been
attained by other existing reconstruction methods.Comment: Accepted by SIGGRAPH 202
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