4,037 research outputs found
Fully Deformable 3D Digital Partition Model with Topological Control
International audienceWe propose a purely discrete deformable partition model for segmenting 3D images. Its main ability is to maintain the topology of the partition during the minimization process. To do so, our main contribution is a new definition of multi-label simple points (ML simple point) that is easily computable. An ML simple point can be relabeled without modifying the overall topology of the partition. The definition is based on intervoxel properties, and uses the notion of collapse on cubical complexes. This work is an extension of a former restricted definition [DupasAl09] that prohibits the move of intersections of boundary surfaces. A deformation process is carried out with a greedy energy minimization algorithm. A discrete area estimator is used to approach at best standard regularizers classically used in continuous energy minimizing methods. We illustrate the potential of our approach with the segmentation of 3D medical images with known expected topology
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
Combining Topological Maps, Multi-Label Simple Points, and Minimum-Length Polygons for Efficient Digital Partition Model
International audienceDeformable models have shown great potential for image segmentation. They include discrete models whose combinatorial formulation leads to efficient and sometimes optimal minimization algorithms. In this paper, we propose a new discrete framework to deform any partition while preserving its topology. We show how to combine the use of multi-label simple points, topological maps and minimum-length polygons in order to implement an efficient digital deformable partition model. Our experimental results illustrate the potential of our framework for segmenting images, since it allows the mixing of region-based, contour-based and regularization energies, while keeping the overall image structure
Deep learning in medical image registration: introduction and survey
Image registration (IR) is a process that deforms images to align them with
respect to a reference space, making it easier for medical practitioners to
examine various medical images in a standardized reference frame, such as
having the same rotation and scale. This document introduces image registration
using a simple numeric example. It provides a definition of image registration
along with a space-oriented symbolic representation. This review covers various
aspects of image transformations, including affine, deformable, invertible, and
bidirectional transformations, as well as medical image registration algorithms
such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It
also explores atlas-based registration and multistage image registration
techniques, including coarse-fine and pyramid approaches. Furthermore, this
survey paper discusses medical image registration taxonomies, datasets,
evaluation measures, such as correlation-based metrics, segmentation-based
metrics, processing time, and model size. It also explores applications in
image-guided surgery, motion tracking, and tumor diagnosis. Finally, the
document addresses future research directions, including the further
development of transformers
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Statistical Shape Modelling and Segmentation of the Respiratory Airway
The human respiratory airway consists of the upper (nasal cavity, pharynx) and the lower (trachea, bronchi) respiratory tracts. Accurate segmentation of these two airway tracts can lead to better diagnosis and interpretation of airway-specific diseases, and lead to improvement in the localization of abnormal metabolic or pathological sites found within and/or surrounding the respiratory regions. Due to the complexity and the variability displayed in the anatomical structure of the upper respiratory airway along with the challenges in distinguishing the nasal cavity from non-respiratory regions such as the paranasal sinuses, it is difficult for existing algorithms to accurately segment the upper airway without manual intervention. This thesis presents an implicit non-parametric framework for constructing a statistical shape model (SSM) of the upper and lower respiratory tract, capable of distinct shape generation and be adapted for segmentation. An SSM of the nasal cavity was successfully constructed using 50 nasal CT scans. The performance of the SSM was evaluated for compactness, specificity and generality. An averaged distance error of 1.47 mm was measured for the generality assessment. The constructed SSM was further adapted with a modified locally constrained random walk algorithm to segment the nasal cavity. The proposed algorithm was evaluated on 30 CT images and outperformed comparative state-of-the-art and conventional algorithms. For the lower airway, a separate algorithm was proposed to automatically segment the trachea and bronchi, and was designed to tolerate the image characteristics inherent in low-contrast CT images. The algorithm was evaluated on 20 clinical low-contrast CT from PET-CT patient studies and demonstrated better performance (87.1±2.8 DSC and distance error of 0.37±0.08 mm) in segmentation results against comparative state-of-the-art algorithms
Superquadric representation of scenes from multi-view range data
Object representation denotes representing three-dimensional (3D) real-world objects with known graphic or mathematic primitives recognizable to computers. This research has numerous applications for object-related tasks in areas including computer vision, computer graphics, reverse engineering, etc. Superquadrics, as volumetric and parametric models, have been selected to be the representation primitives throughout this research. Superquadrics are able to represent a large family of solid shapes by a single equation with only a few parameters. This dissertation addresses superquadric representation of multi-part objects and multiobject scenes. Two issues motivate this research. First, superquadric representation of multipart objects or multi-object scenes has been an unsolved problem due to the complex geometry of objects. Second, superquadrics recovered from single-view range data tend to have low confidence and accuracy due to partially scanned object surfaces caused by inherent occlusions. To address these two problems, this dissertation proposes a multi-view superquadric representation algorithm. By incorporating both part decomposition and multi-view range data, the proposed algorithm is able to not only represent multi-part objects or multi-object scenes, but also achieve high confidence and accuracy of recovered superquadrics. The multi-view superquadric representation algorithm consists of (i) initial superquadric model recovery from single-view range data, (ii) pairwise view registration based on recovered superquadric models, (iii) view integration, (iv) part decomposition, and (v) final superquadric fitting for each decomposed part. Within the multi-view superquadric representation framework, this dissertation proposes a 3D part decomposition algorithm to automatically decompose multi-part objects or multiobject scenes into their constituent single parts consistent with human visual perception. Superquadrics can then be recovered for each decomposed single-part object. The proposed part decomposition algorithm is based on curvature analysis, and includes (i) Gaussian curvature estimation, (ii) boundary labeling, (iii) part growing and labeling, and (iv) post-processing. In addition, this dissertation proposes an extended view registration algorithm based on superquadrics. The proposed view registration algorithm is able to handle deformable superquadrics as well as 3D unstructured data sets. For superquadric fitting, two objective functions primarily used in the literature have been comprehensively investigated with respect to noise, viewpoints, sample resolutions, etc. The objective function proved to have better performance has been used throughout this dissertation. In summary, the three algorithms (contributions) proposed in this dissertation are generic and flexible in the sense of handling triangle meshes, which are standard surface primitives in computer vision and graphics. For each proposed algorithm, the dissertation presents both theory and experimental results. The results demonstrate the efficiency of the algorithms using both synthetic and real range data of a large variety of objects and scenes. In addition, the experimental results include comparisons with previous methods from the literature. Finally, the dissertation concludes with a summary of the contributions to the state of the art in superquadric representation, and presents possible future extensions to this research
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