23,396 research outputs found
Anatomical landmark based registration of contrast enhanced T1-weighted MR images
In many problems involving multiple image analysis, an im- age registration step is required. One such problem appears in brain tumor imaging, where baseline and follow-up image volumes from a tu- mor patient are often to-be compared. Nature of the registration for a change detection problem in brain tumor growth analysis is usually rigid or affine. Contrast enhanced T1-weighted MR images are widely used in clinical practice for monitoring brain tumors. Over this modality, con- tours of the active tumor cells and whole tumor borders and margins are visually enhanced. In this study, a new technique to register serial contrast enhanced T1 weighted MR images is presented. The proposed fully-automated method is based on five anatomical landmarks: eye balls, nose, confluence of sagittal sinus, and apex of superior sagittal sinus. Af- ter extraction of anatomical landmarks from fixed and moving volumes, an affine transformation is estimated by minimizing the sum of squared distances between the landmark coordinates. Final result is refined with a surface registration, which is based on head masks confined to the sur- face of the scalp, as well as to a plane constructed from three of the extracted features. The overall registration is not intensity based, and it depends only on the invariant structures. Validation studies using both synthetically transformed MRI data, and real MRI scans, which included several markers over the head of the patient were performed. In addition, comparison studies against manual landmarks marked by a radiologist, as well as against the results obtained from a typical mutual information based method were carried out to demonstrate the effectiveness of the proposed method
Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
This paper aims to solve a fundamental problem in intensity-based 2D/3D
registration, which concerns the limited capture range and need for very good
initialization of state-of-the-art image registration methods. We propose a
regression approach that learns to predict rotation and translations of
arbitrary 2D image slices from 3D volumes, with respect to a learned canonical
atlas co-ordinate system. To this end, we utilize Convolutional Neural Networks
(CNNs) to learn the highly complex regression function that maps 2D image
slices into their correct position and orientation in 3D space. Our approach is
attractive in challenging imaging scenarios, where significant subject motion
complicates reconstruction performance of 3D volumes from 2D slice data. We
extensively evaluate the effectiveness of our approach quantitatively on
simulated MRI brain data with extreme random motion. We further demonstrate
qualitative results on fetal MRI where our method is integrated into a full
reconstruction and motion compensation pipeline. With our CNN regression
approach we obtain an average prediction error of 7mm on simulated data, and
convincing reconstruction quality of images of very young fetuses where
previous methods fail. We further discuss applications to Computed Tomography
and X-ray projections. Our approach is a general solution to the 2D/3D
initialization problem. It is computationally efficient, with prediction times
per slice of a few milliseconds, making it suitable for real-time scenarios.Comment: 8 pages, 4 figures, 6 pages supplemental material, currently under
review for MICCAI 201
Finite Element Based Tracking of Deforming Surfaces
We present an approach to robustly track the geometry of an object that
deforms over time from a set of input point clouds captured from a single
viewpoint. The deformations we consider are caused by applying forces to known
locations on the object's surface. Our method combines the use of prior
information on the geometry of the object modeled by a smooth template and the
use of a linear finite element method to predict the deformation. This allows
the accurate reconstruction of both the observed and the unobserved sides of
the object. We present tracking results for noisy low-quality point clouds
acquired by either a stereo camera or a depth camera, and simulations with
point clouds corrupted by different error terms. We show that our method is
also applicable to large non-linear deformations.Comment: additional experiment
Cramer-Rao Lower Bound for Point Based Image Registration with Heteroscedastic Error Model for Application in Single Molecule Microscopy
The Cramer-Rao lower bound for the estimation of the affine transformation
parameters in a multivariate heteroscedastic errors-in-variables model is
derived. The model is suitable for feature-based image registration in which
both sets of control points are localized with errors whose covariance matrices
vary from point to point. With focus given to the registration of fluorescence
microscopy images, the Cramer-Rao lower bound for the estimation of a feature's
position (e.g. of a single molecule) in a registered image is also derived. In
the particular case where all covariance matrices for the localization errors
are scalar multiples of a common positive definite matrix (e.g. the identity
matrix), as can be assumed in fluorescence microscopy, then simplified
expressions for the Cramer-Rao lower bound are given. Under certain simplifying
assumptions these expressions are shown to match asymptotic distributions for a
previously presented set of estimators. Theoretical results are verified with
simulations and experimental data
Fully Automatic Expression-Invariant Face Correspondence
We consider the problem of computing accurate point-to-point correspondences
among a set of human face scans with varying expressions. Our fully automatic
approach does not require any manually placed markers on the scan. Instead, the
approach learns the locations of a set of landmarks present in a database and
uses this knowledge to automatically predict the locations of these landmarks
on a newly available scan. The predicted landmarks are then used to compute
point-to-point correspondences between a template model and the newly available
scan. To accurately fit the expression of the template to the expression of the
scan, we use as template a blendshape model. Our algorithm was tested on a
database of human faces of different ethnic groups with strongly varying
expressions. Experimental results show that the obtained point-to-point
correspondence is both highly accurate and consistent for most of the tested 3D
face models
Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology
Until recently, Computer-Aided Medical Interventions (CAMI) and Medical
Robotics have focused on rigid and non deformable anatomical structures.
Nowadays, special attention is paid to soft tissues, raising complex issues due
to their mobility and deformation. Mini-invasive digestive surgery was probably
one of the first fields where soft tissues were handled through the development
of simulators, tracking of anatomical structures and specific assistance
robots. However, other clinical domains, for instance urology, are concerned.
Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU,
radiofrequency, or cryoablation), increasingly early detection of cancer, and
use of interventional and diagnostic imaging modalities, recently opened new
challenges to the urologist and scientists involved in CAMI. This resulted in
the last five years in a very significant increase of research and developments
of computer-aided urology systems. In this paper, we propose a description of
the main problems related to computer-aided diagnostic and therapy of soft
tissues and give a survey of the different types of assistance offered to the
urologist: robotization, image fusion, surgical navigation. Both research
projects and operational industrial systems are discussed
Fast Predictive Multimodal Image Registration
We introduce a deep encoder-decoder architecture for image deformation
prediction from multimodal images. Specifically, we design an image-patch-based
deep network that jointly (i) learns an image similarity measure and (ii) the
relationship between image patches and deformation parameters. While our method
can be applied to general image registration formulations, we focus on the
Large Deformation Diffeomorphic Metric Mapping (LDDMM) registration model. By
predicting the initial momentum of the shooting formulation of LDDMM, we
preserve its mathematical properties and drastically reduce the computation
time, compared to optimization-based approaches. Furthermore, we create a
Bayesian probabilistic version of the network that allows evaluation of
registration uncertainty via sampling of the network at test time. We evaluate
our method on a 3D brain MRI dataset using both T1- and T2-weighted images. Our
experiments show that our method generates accurate predictions and that
learning the similarity measure leads to more consistent registrations than
relying on generic multimodal image similarity measures, such as mutual
information. Our approach is an order of magnitude faster than
optimization-based LDDMM.Comment: Accepted as a conference paper for ISBI 201
- …