60 research outputs found
Dictionary Learning Based Image Descriptor for Myocardial Registration of CP-BOLD MR:Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP-BOLD) MRI is a new contrast agent- and stress-free imaging technique for the assessment of myocardial ischemia at rest. The precise registration among the cardiac phases in this cine type acquisition is essential for automating the analysis of images of this technique, since it can potentially lead to better specificity of ischemia detection. However, inconsistency in myocardial intensity patterns and the changes in myocardial shape due to the heart’s motion lead to low registration performance for state-of-the-art methods. This low accuracy can be explained by the lack of distinguishable features in CP-BOLD and inappropriate metric definitions in current intensity-based registration frameworks. In this paper, the sparse representations, which are defined by a discriminative dictionary learning approach for source and target images, are used to improve myocardial registration. This method combines appearance with Gabor and HOG features in a dictionary learning framework to sparsely represent features in a low dimensional space. The sum of absolute differences of these distinctive sparse representations are used to define a similarity term in the registration framework. The proposed approach is validated on a dataset of CP-BOLD MR and standard CINE MR acquired in baseline and ischemic condition across 10 canines
Unsupervised Myocardial Segmentation for Cardiac BOLD
A fully automated 2-D+time myocardial segmentation framework is proposed for cardiac magnetic resonance (CMR)
blood-oxygen-level-dependent (BOLD) data sets. Ischemia detection with CINE BOLD CMR relies on spatio-temporal patterns in myocardial
intensity, but these patterns also trouble supervised segmentation methods, the de facto standard for myocardial segmentation in cine MRI.
Segmentation errors severely undermine the accurate extraction of these patterns. In this paper, we build a joint motion and appearance method
that relies on dictionary learning to find a suitable subspace.Our method is based on variational pre-processing and spatial regularization using
Markov random fields, to further improve performance. The superiority of the proposed segmentation technique is demonstrated on a data set
containing cardiac phase resolved BOLD MR and standard CINE MR image sequences acquired in baseline and is chemic condition across ten
canine subjects. Our unsupervised approach outperforms even supervised state-of-the-art segmentation techniques by at least 10% when using
Dice to measure accuracy on BOLD data and performs at par for standard CINE MR. Furthermore, a novel segmental analysis method attuned
for BOLD time series is utilized to demonstrate the effectiveness of the proposed method in preserving key BOLD patterns
Pattern Recognition
A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition
Towards a system of concepts for Family Medicine. Multilingual indexing in General Practice/ Family Medicine in the era of Semantic Web
UNIVERSITY OF LIÈGE, BELGIUM
Executive Summary
Faculty of Medicine
Département Universitaire de Médecine Générale.
Unité de recherche Soins Primaires et Santé
Doctor in biomedical sciences
Towards a system of concepts for Family Medicine.
Multilingual indexing in General Practice/ Family Medicine in the era
of SemanticWeb
by Dr. Marc JAMOULLE
Introduction
This thesis is about giving visibility to the often overlooked work of family
physicians and consequently, is about grey literature in General Practice
and Family Medicine (GP/FM). It often seems that conference organizers
do not think of GP/FM as a knowledge-producing discipline that deserves
active dissemination. A conference is organized, but not much is done with
the knowledge shared at these meetings. In turn, the knowledge cannot be
reused or reapplied. This these is also about indexing. To find knowledge
back, indexing is mandatory. We must prepare tools that will automatically
index the thousands of abstracts that family doctors produce each year in
various languages. And finally this work is about semantics1. It is an introduction
to health terminologies, ontologies, semantic data, and linked
open data. All are expressions of the next step: Semantic Web for health
care data. Concepts, units of thought expressed by terms, will be our target
and must have the ability to be expressed in multiple languages. In turn,
three areas of knowledge are at stake in this study: (i) Family Medicine as a
pillar of primary health care, (ii) computational linguistics, and (iii) health
information systems.
Aim
• To identify knowledge produced by General practitioners (GPs) by
improving annotation of grey literature in Primary Health Care
• To propose an experimental indexing system, acting as draft for a
standardized table of content of GP/GM
• To improve the searchability of repositories for grey literature in GP/GM.
1For specific terms, see the Glossary page 257
x
Methods
The first step aimed to design the taxonomy by identifying relevant concepts
in a compiled corpus of GP/FM texts. We have studied the concepts
identified in nearly two thousand communications of GPs during
conferences. The relevant concepts belong to the fields that are focusing
on GP/FM activities (e.g. teaching, ethics, management or environmental
hazard issues).
The second step was the development of an on-line, multilingual, terminological
resource for each category of the resulting taxonomy, named
Q-Codes. We have designed this terminology in the form of a lightweight
ontology, accessible on-line for readers and ready for use by computers of
the semantic web. It is also fit for the Linked Open Data universe.
Results
We propose 182 Q-Codes in an on-line multilingual database (10 languages)
(www.hetop.eu/Q) acting each as a filter for Medline. Q-Codes are also available
under the form of Unique Resource Identifiers (URIs) and are exportable
in Web Ontology Language (OWL). The International Classification of Primary
Care (ICPC) is linked to Q-Codes in order to form the Core Content
Classification in General Practice/Family Medicine (3CGP). So far, 3CGP is
in use by humans in pedagogy, in bibliographic studies, in indexing congresses,
master theses and other forms of grey literature in GP/FM. Use by
computers is experimented in automatic classifiers, annotators and natural
language processing.
Discussion
To the best of our knowledge, this is the first attempt to expand the ICPC
coding system with an extension for family physician contextual issues,
thus covering non-clinical content of practice. It remains to be proven that
our proposed terminology will help in dealing with more complex systems,
such as MeSH, to support information storage and retrieval activities.
However, this exercise is proposed as a first step in the creation of an ontology
of GP/FM and as an opening to the complex world of Semantic Web
technologies.
Conclusion
We expect that the creation of this terminological resource for indexing abstracts
and for facilitating Medline searches for general practitioners, researchers
and students in medicine will reduce loss of knowledge in the
domain of GP/FM. In addition, through better indexing of the grey literature
(congress abstracts, master’s and doctoral theses), we hope to enhance
the accessibility of research results and give visibility to the invisible work
of family physicians
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