1,499 research outputs found
Transfer learning for multicenter classification of chronic obstructive pulmonary disease
Chronic obstructive pulmonary disease (COPD) is a lung disease which can be
quantified using chest computed tomography (CT) scans. Recent studies have
shown that COPD can be automatically diagnosed using weakly supervised learning
of intensity and texture distributions. However, up till now such classifiers
have only been evaluated on scans from a single domain, and it is unclear
whether they would generalize across domains, such as different scanners or
scanning protocols. To address this problem, we investigate classification of
COPD in a multi-center dataset with a total of 803 scans from three different
centers, four different scanners, with heterogenous subject distributions. Our
method is based on Gaussian texture features, and a weighted logistic
classifier, which increases the weights of samples similar to the test data. We
show that Gaussian texture features outperform intensity features previously
used in multi-center classification tasks. We also show that a weighting
strategy based on a classifier that is trained to discriminate between scans
from different domains, can further improve the results. To encourage further
research into transfer learning methods for classification of COPD, upon
acceptance of the paper we will release two feature datasets used in this study
on http://bigr.nl/research/projects/copdComment: Accepted at Journal of Biomedical and Health Informatic
Machine learning approaches in medical image analysis: From detection to diagnosis
Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results
Quantitation in MRI : application to ageing and epilepsy
Multi-atlas propagation and label fusion techniques have recently been developed for segmenting
the human brain into multiple anatomical regions. In this thesis, I investigate
possible adaptations of these current state-of-the-art methods. The aim is to study ageing
on the one hand, and on the other hand temporal lobe epilepsy as an example for a
neurological disease.
Overall effects are a confounding factor in such anatomical analyses. Intracranial volume
(ICV) is often preferred to normalize for global effects as it allows to normalize for estimated
maximum brain size and is hence independent of global brain volume loss, as seen
in ageing and disease. I describe systematic differences in ICV measures obtained at 1.5T
versus 3T, and present an automated method of measuring intracranial volume, Reverse
MNI Brain Masking (RBM), based on tissue probability maps in MNI standard space. I
show that this is comparable to manual measurements and robust against field strength
differences.
Correct and robust segmentation of target brains which show gross abnormalities, such as
ventriculomegaly, is important for the study of ageing and disease. We achieved this with
incorporating tissue classification information into the image registration process. The
best results in elderly subjects, patients with TLE and healthy controls were achieved using
a new approach using multi-atlas propagation with enhanced registration (MAPER).
I then applied MAPER to the problem of automatically distinguishing patients with TLE
with (TLE-HA) and without (TLE-N) hippocampal atrophy on MRI from controls, and
determine the side of seizure onset. MAPER-derived structural volumes were used for
a classification step consisting of selecting a set of discriminatory structures and applying
support vector machine on the structural volumes as well as morphological similarity
information such as volume difference obtained with spectral analysis. Acccuracies were
91-100 %, indicating that the method might be clinically useful.
Finally, I used the methods developed in the previous chapters to investigate brain regional
volume changes across the human lifespan in over 500 healthy subjects between 20
to 90 years of age, using data from three different scanners (2x 1.5T, 1x 3T), using the IXI
database. We were able to confirm several known changes, indicating the veracity of the
method. In addition, we describe the first multi-region, whole-brain database of normal
ageing
- …