2,979 research outputs found
Medical image segmentation using object atlas versus object cloud models
Medical image segmentation is crucial for quantitative organ analysis and surgical planning. Since interactive segmentation is not practical in a production-mode clinical setting, automatic methods based on 3D object appearance models have been proposed. Among them, approaches based on object atlas are the most actively investigated. A key drawback of these approaches is that they require a time-costly image registration process to build and deploy the atlas. Object cloud models (OCM) have been introduced to avoid registration, considerably speeding up the whole process, but they have not been compared to object atlas models (OAM). The present paper fills this gap by presenting a comparative analysis of the two approaches in the task of individually segmenting nine anatomical structures of the human body. Our results indicate that OCM achieve a statistically significant better accuracy for seven anatomical structures, in terms of Dice Similarity Coefficient and Average Symmetric Surface Distance.Medical image segmentation is crucial for quantitative organ analysis and surgical planning. Since interactive segmentation is not practical in a production-mode clinical setting, automatic methods based on 3D object appearance models have been proposed.9415CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTĂFICO E TECNOLĂGICOFAPESP - FUNDAĂĂO DE AMPARO Ă PESQUISA DO ESTADO DE SĂO PAULO303673/2010-9; 479070/2013-0; 131835/2013-0sem informaçãoSPIE - international society for optical engineering. medical imagin
Rich probabilistic models for semantic labeling
Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere BeitrÀge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinÀren Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung
Image databases in medical applications
The number of medical images acquired yearly in hospitals increases all the time. These imaging data contain lots of information on the characteristics of anatomical structures and on their variations. This information can be utilized in numerous medical applications. In deformable model-based segmentation and registration methods, the information in the image databases can be used to give a priori information on the shape of the object studied and the gray-level values in the image, and on their variations. On the other hand, by studying the variations of the object of interest in different populations, the effects of, for example, aging, gender, and diseases on anatomical structures can be detected.
In the work described in this Thesis, methods that utilize image databases in medical applications were studied. Methods were developed and compared for deformable model-based segmentation and registration. Model selection procedure, mean models, and combination of classifiers were studied for the construction of a good a priori model. Statistical and probabilistic shape models were generated to constrain the deformations in segmentation and registration so that only the shapes typical to the object studied were accepted. In the shape analysis of the striatum, both volume and local shape changes were studied. The effects of aging and gender, and also the asymmetries were examined.
The results proved that the segmentation and registration accuracy of deformable model-based methods can be improved by utilizing the information in image databases. The databases used were relatively small. Therefore, the statistical and probabilistic methods were not able to model all the population-specific variation. On the other hand, the simpler methods, the model selection procedure, mean models, and combination of classifiers, gave good results also with the small image databases. Two main applications were the reconstruction of 3-D geometry from incomplete data and the segmentation of heart ventricles and atria from short- and long-axis magnetic resonance images. In both applications, the methods studied provided promising results. The shape analysis of the striatum showed that the volume of the striatum decreases in aging. Also, the shape of the striatum changes locally. Asymmetries in the shape were found, too, but any gender-related local shape differences were not found.reviewe
Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback
Ultrasound (US) imaging is widely employed for diagnosis and staging of
peripheral vascular diseases (PVD), mainly due to its high availability and the
fact it does not emit radiation. However, high inter-operator variability and a
lack of repeatability of US image acquisition hinder the implementation of
extensive screening programs. To address this challenge, we propose an
end-to-end workflow for automatic robotic US screening of tubular structures
using only the real-time US imaging feedback. We first train a U-Net for
real-time segmentation of the vascular structure from cross-sectional US
images. Then, we represent the detected vascular structure as a 3D point cloud
and use it to estimate the longitudinal axis of the target tubular structure
and its mean radius by solving a constrained non-linear optimization problem.
Iterating the previous processes, the US probe is automatically aligned to the
orientation normal to the target tubular tissue and adjusted online to center
the tracked tissue based on the spatial calibration. The real-time segmentation
result is evaluated both on a phantom and in-vivo on brachial arteries of
volunteers. In addition, the whole process is validated both in simulation and
physical phantoms. The mean absolute radius error and orientation error (
SD) in the simulation are and ,
respectively. On a gel phantom, these errors are and
. This shows that the method is able to automatically screen
tubular tissues with an optimal probe orientation (i.e. normal to the vessel)
and at the same to accurately estimate the mean radius, both in real-time.Comment: Accepted for publication in IEEE Transactions on Industrial
Electronics Video: https://www.youtube.com/watch?v=VAaNZL0I5i
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
Cloud-Based Benchmarking of Medical Image Analysis
Medical imagin
- âŠ