759 research outputs found
Towards ultra-high resolution 3D reconstruction of a whole rat brain from 3D-PLI data
3D reconstruction of the fiber connectivity of the rat brain at microscopic
scale enables gaining detailed insight about the complex structural
organization of the brain. We introduce a new method for registration and 3D
reconstruction of high- and ultra-high resolution (64 m and 1.3 m
pixel size) histological images of a Wistar rat brain acquired by 3D polarized
light imaging (3D-PLI). Our method exploits multi-scale and multi-modal 3D-PLI
data up to cellular resolution. We propose a new feature transform-based
similarity measure and a weighted regularization scheme for accurate and robust
non-rigid registration. To transform the 1.3 m ultra-high resolution data
to the reference blockface images a feature-based registration method followed
by a non-rigid registration is proposed. Our approach has been successfully
applied to 278 histological sections of a rat brain and the performance has
been quantitatively evaluated using manually placed landmarks by an expert.Comment: 9 pages, Accepted at 2nd International Workshop on Connectomics in
NeuroImaging (CNI), MICCAI'201
Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization
Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an
area of interest for quantification of regional cardiac function from balanced,
steady state free precession (SSFP) cine sequences. However, currently
available techniques lack full automation, limiting reproducibility. We propose
a fully automated technique whereby a CMR image sequence is first segmented
with a deep, fully convolutional neural network (CNN) architecture, and
quadratic basis splines are fitted simultaneously across all cardiac frames
using least squares optimization. Experiments are performed using data from 42
patients with hypertrophic cardiomyopathy (HCM) and 21 healthy control
subjects. In terms of segmentation, we compared state-of-the-art CNN
frameworks, U-Net and dilated convolution architectures, with and without
temporal context, using cross validation with three folds. Performance relative
to expert manual segmentation was similar across all networks: pixel accuracy
was ~97%, intersection-over-union (IoU) across all classes was ~87%, and IoU
across foreground classes only was ~85%. Endocardial left ventricular
circumferential strain calculated from the proposed pipeline was significantly
different in control and disease subjects (-25.3% vs -29.1%, p = 0.006), in
agreement with the current clinical literature.Comment: Accepted to Functional Imaging and Modeling of the Heart (FIMH) 201
2D Reconstruction of Small Intestine's Interior Wall
Examining and interpreting of a large number of wireless endoscopic images
from the gastrointestinal tract is a tiresome task for physicians. A practical
solution is to automatically construct a two dimensional representation of the
gastrointestinal tract for easy inspection. However, little has been done on
wireless endoscopic image stitching, let alone systematic investigation. The
proposed new wireless endoscopic image stitching method consists of two main
steps to improve the accuracy and efficiency of image registration. First, the
keypoints are extracted by Principle Component Analysis and Scale Invariant
Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood
Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable
keypoints. Second, the optimal transformation parameters obtained from first
step are fed to the Normalised Mutual Information (NMI) algorithm as an initial
solution. With modified Marquardt-Levenberg search strategy in a multiscale
framework, the NMI can find the optimal transformation parameters in the
shortest time. The proposed methodology has been tested on two different
datasets - one with real wireless endoscopic images and another with images
obtained from Micro-Ball (a new wireless cubic endoscopy system with six image
sensors). The results have demonstrated the accuracy and robustness of the
proposed methodology both visually and quantitatively.Comment: Journal draf
Fast interpolation operations in non-rigid image registration
Much literature on image registration1–3 has worked with purely geometric image deformation models. For such models,
interpolation/resampling operations are often the computationally intensive steps when iteratively minimizing the deformation
cost function. This article discusses some techniques for efficiently implementing and accelerating these operations.
To simplify presentation, we discuss our ideas in the context of 2D imaging. However, the concepts readily generalize
to 3D. Our central technique is a table-lookup scheme that makes somewhat liberal use of RAM, but should not strain
the resources of modern processors if certain design parameters are appropriately selected. The technique works by preinterpolating
and tabulating the grid values of the reference image onto a finer grid along one of the axes of the image. The
lookup table can be rapidly constructed using FFTs. Our results show that this technique reduces iterative computation by
an order of magnitude. When a minimization algorithm employing coordinate block alternation is used, one can obtain
still faster computation by storing certain intermediate quantities as state variables. We refer to this technique as state
variable hold-over. When combined with table-lookup, state variable hold-over reduces CPU time by about a factor two,
as compared to table-lookup alone.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85925/1/Fessler207.pd
Quantifying Uncertainties for Prostate Image-Guided Radiotherapy: A 3D Organ Reconstruction and Registration Method
The purpose of this paper is to present a method for volumetric reconstruction, registration and margin assignation
applicable to both conventional CT scans and on board CT imaging. This method does not depend on the shape of the
organs, the bony anatomy or the use of markers, and we apply it to prostate and bladder. 3D reconstructions are performed
by means of spline surfaces and the 3D reconstructed surfaces are registered to a planning surface, using a multidimensional
alignment from the Euclidean distance transform and the Levenberg-Marquardt optimization algorithm. Once the
reconstructed surfaces are registered, we define a mean surface and obtain the corresponding variances from this mean
surface. The method works properly and demonstrates that once translations are insulated by registration, residual uncertainties
can be handled with the margin assigned for delineation variation and organ deformatio
Investigation of Intensity Correction in the Context of Image Registration
An image registration algorithm with intensity correction was developed. A particular goal was to apply intensity correction instead of using multimodal similarity measures.
The algorithm utilises common Levenberg-Marquardt optimisation. The author has chosen two dimensional affine and one dimensional B-Spline model as spatial transformation, as well as intensity correction models specific to CT images. They are global non-linear mapping and smooth local affine correction. The algorithm was tested experimentally using a wide class of simulated images and a limited class of medical images.
Affine registration works properly even for deformations which exceed typical deformation encountered in medical practice. B-Spline registration works properly for small deformations and requires further development to increase capture range.
The idea of separating intensity correction mapping from similarity measure is shown to have advantages. Choosing intensity correction model can make the registration algorithm specific to the image class of interest
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