1,570 research outputs found
Estimation of vector fields in unconstrained and inequality constrained variational problems for segmentation and registration
Vector fields arise in many problems of computer vision, particularly in non-rigid registration. In this paper, we develop coupled partial differential equations (PDEs) to estimate vector fields that define the deformation between
objects, and the contour or surface that defines the segmentation of the objects as well.We also explore the utility of inequality constraints applied to variational problems in vision such as estimation of deformation fields in non-rigid registration and tracking. To solve inequality constrained vector
field estimation problems, we apply tools from the Kuhn-Tucker theorem in optimization theory. Our technique differs from recently popular joint segmentation and registration algorithms, particularly in its coupled set of PDEs derived from the same set of energy terms for registration and
segmentation. We present both the theory and results that demonstrate our approach
Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging
An Unsupervised Learning Model for Deformable Medical Image Registration
We present a fast learning-based algorithm for deformable, pairwise 3D
medical image registration. Current registration methods optimize an objective
function independently for each pair of images, which can be time-consuming for
large data. We define registration as a parametric function, and optimize its
parameters given a set of images from a collection of interest. Given a new
pair of scans, we can quickly compute a registration field by directly
evaluating the function using the learned parameters. We model this function
using a convolutional neural network (CNN), and use a spatial transform layer
to reconstruct one image from another while imposing smoothness constraints on
the registration field. The proposed method does not require supervised
information such as ground truth registration fields or anatomical landmarks.
We demonstrate registration accuracy comparable to state-of-the-art 3D image
registration, while operating orders of magnitude faster in practice. Our
method promises to significantly speed up medical image analysis and processing
pipelines, while facilitating novel directions in learning-based registration
and its applications. Our code is available at
https://github.com/balakg/voxelmorph .Comment: 9 pages, in CVPR 201
Affine Registration of label maps in Label Space
Two key aspects of coupled multi-object shape\ud
analysis and atlas generation are the choice of representation\ud
and subsequent registration methods used to align the sample\ud
set. For example, a typical brain image can be labeled into\ud
three structures: grey matter, white matter and cerebrospinal\ud
fluid. Many manipulations such as interpolation, transformation,\ud
smoothing, or registration need to be performed on these images\ud
before they can be used in further analysis. Current techniques\ud
for such analysis tend to trade off performance between the two\ud
tasks, performing well for one task but developing problems when\ud
used for the other.\ud
This article proposes to use a representation that is both\ud
flexible and well suited for both tasks. We propose to map object\ud
labels to vertices of a regular simplex, e.g. the unit interval for\ud
two labels, a triangle for three labels, a tetrahedron for four\ud
labels, etc. This representation, which is routinely used in fuzzy\ud
classification, is ideally suited for representing and registering\ud
multiple shapes. On closer examination, this representation\ud
reveals several desirable properties: algebraic operations may\ud
be done directly, label uncertainty is expressed as a weighted\ud
mixture of labels (probabilistic interpretation), interpolation is\ud
unbiased toward any label or the background, and registration\ud
may be performed directly.\ud
We demonstrate these properties by using label space in a gradient\ud
descent based registration scheme to obtain a probabilistic\ud
atlas. While straightforward, this iterative method is very slow,\ud
could get stuck in local minima, and depends heavily on the initial\ud
conditions. To address these issues, two fast methods are proposed\ud
which serve as coarse registration schemes following which the\ud
iterative descent method can be used to refine the results. Further,\ud
we derive an analytical formulation for direct computation of the\ud
"group mean" from the parameters of pairwise registration of all\ud
the images in the sample set. We show results on richly labeled\ud
2D and 3D data sets
Registration of brain tumor images using hyper-elastic regularization
In this paper, we present a method to estimate a deformation
field between two instances of a brain volume having tumor. The novelties
include the assessment of the disease progress by observing the healthy tissue
deformation and usage of the Neo-Hookean strain energy density model as
a regularizer in deformable registration framework. Implementations on synthetic
and patient data provide promising results, which might have relevant
use in clinical problems
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