827 research outputs found
Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization
With the introduction of spectral-domain optical coherence tomography (OCT),
resulting in a significant increase in acquisition speed, the fast and accurate
segmentation of 3-D OCT scans has become evermore important. This paper
presents a novel probabilistic approach, that models the appearance of retinal
layers as well as the global shape variations of layer boundaries. Given an OCT
scan, the full posterior distribution over segmentations is approximately
inferred using a variational method enabling efficient probabilistic inference
in terms of computationally tractable model components: Segmenting a full 3-D
volume takes around a minute. Accurate segmentations demonstrate the benefit of
using global shape regularization: We segmented 35 fovea-centered 3-D volumes
with an average unsigned error of 2.46 0.22 {\mu}m as well as 80 normal
and 66 glaucomatous 2-D circular scans with errors of 2.92 0.53 {\mu}m
and 4.09 0.98 {\mu}m respectively. Furthermore, we utilized the inferred
posterior distribution to rate the quality of the segmentation, point out
potentially erroneous regions and discriminate normal from pathological scans.
No pre- or postprocessing was required and we used the same set of parameters
for all data sets, underlining the robustness and out-of-the-box nature of our
approach.Comment: Accepted for publication in Medical Image Analysis (MIA), Elsevie
Towards Developing Computer Vision Algorithms and Architectures for Real-world Applications
abstract: Computer vision technology automatically extracts high level, meaningful information from visual data such as images or videos, and the object recognition and detection algorithms are essential in most computer vision applications. In this dissertation, we focus on developing algorithms used for real life computer vision applications, presenting innovative algorithms for object segmentation and feature extraction for objects and actions recognition in video data, and sparse feature selection algorithms for medical image analysis, as well as automated feature extraction using convolutional neural network for blood cancer grading.
To detect and classify objects in video, the objects have to be separated from the background, and then the discriminant features are extracted from the region of interest before feeding to a classifier. Effective object segmentation and feature extraction are often application specific, and posing major challenges for object detection and classification tasks. In this dissertation, we address effective object flow based ROI generation algorithm for segmenting moving objects in video data, which can be applied in surveillance and self driving vehicle areas. Optical flow can also be used as features in human action recognition algorithm, and we present using optical flow feature in pre-trained convolutional neural network to improve performance of human action recognition algorithms. Both algorithms outperform the state-of-the-arts at their time.
Medical images and videos pose unique challenges for image understanding mainly due to the fact that the tissues and cells are often irregularly shaped, colored, and textured, and hand selecting most discriminant features is often difficult, thus an automated feature selection method is desired. Sparse learning is a technique to extract the most discriminant and representative features from raw visual data. However, sparse learning with \textit{L1} regularization only takes the sparsity in feature dimension into consideration; we improve the algorithm so it selects the type of features as well; less important or noisy feature types are entirely removed from the feature set. We demonstrate this algorithm to analyze the endoscopy images to detect unhealthy abnormalities in esophagus and stomach, such as ulcer and cancer. Besides sparsity constraint, other application specific constraints and prior knowledge may also need to be incorporated in the loss function in sparse learning to obtain the desired results. We demonstrate how to incorporate similar-inhibition constraint, gaze and attention prior in sparse dictionary selection for gastroscopic video summarization that enable intelligent key frame extraction from gastroscopic video data. With recent advancement in multi-layer neural networks, the automatic end-to-end feature learning becomes feasible. Convolutional neural network mimics the mammal visual cortex and can extract most discriminant features automatically from training samples. We present using convolutinal neural network with hierarchical classifier to grade the severity of Follicular Lymphoma, a type of blood cancer, and it reaches 91\% accuracy, on par with analysis by expert pathologists.
Developing real world computer vision applications is more than just developing core vision algorithms to extract and understand information from visual data; it is also subject to many practical requirements and constraints, such as hardware and computing infrastructure, cost, robustness to lighting changes and deformation, ease of use and deployment, etc.The general processing pipeline and system architecture for the computer vision based applications share many similar design principles and architecture. We developed common processing components and a generic framework for computer vision application, and a versatile scale adaptive template matching algorithm for object detection. We demonstrate the design principle and best practices by developing and deploying a complete computer vision application in real life, building a multi-channel water level monitoring system, where the techniques and design methodology can be generalized to other real life applications. The general software engineering principles, such as modularity, abstraction, robust to requirement change, generality, etc., are all demonstrated in this research.Dissertation/ThesisDoctoral Dissertation Computer Science 201
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review
Cancer remains one of the most challenging diseases to treat in the medical
field. Machine learning has enabled in-depth analysis of rich multi-omics
profiles and medical imaging for cancer diagnosis and prognosis. Despite these
advancements, machine learning models face challenges stemming from limited
labeled sample sizes, the intricate interplay of high-dimensionality data
types, the inherent heterogeneity observed among patients and within tumors,
and concerns about interpretability and consistency with existing biomedical
knowledge. One approach to surmount these challenges is to integrate biomedical
knowledge into data-driven models, which has proven potential to improve the
accuracy, robustness, and interpretability of model results. Here, we review
the state-of-the-art machine learning studies that adopted the fusion of
biomedical knowledge and data, termed knowledge-informed machine learning, for
cancer diagnosis and prognosis. Emphasizing the properties inherent in four
primary data types including clinical, imaging, molecular, and treatment data,
we highlight modeling considerations relevant to these contexts. We provide an
overview of diverse forms of knowledge representation and current strategies of
knowledge integration into machine learning pipelines with concrete examples.
We conclude the review article by discussing future directions to advance
cancer research through knowledge-informed machine learning.Comment: 41 pages, 4 figures, 2 table
Bayesian Inference with Combined Dynamic and Sparsity Models: Application in 3D Electrophysiological Imaging
Data-driven inference is widely encountered in various scientific domains to convert the observed measurements into information that cannot be directly observed about a system. Despite the quickly-developing sensor and imaging technologies, in many domains, data collection remains an expensive endeavor due to financial and physical constraints. To overcome the limits in data and to reduce the demand on expensive data collection, it is important to incorporate prior information in order to place the data-driven inference in a domain-relevant context and to improve its accuracy.
Two sources of assumptions have been used successfully in many inverse problem applications. One is the temporal dynamics of the system (dynamic structure). The other is the low-dimensional structure of a system (sparsity structure). In existing work, these two structures have often been explored separately, while in most high-dimensional dynamic system they are commonly co-existing and contain complementary information.
In this work, our main focus is to build a robustness inference framework to combine dynamic and sparsity constraints. The driving application in this work is a biomedical inverse problem of electrophysiological (EP) imaging, which noninvasively and quantitatively reconstruct transmural action potentials from body-surface voltage data with the goal to improve cardiac disease prevention, diagnosis, and treatment. The general framework can be extended to a variety of applications that deal with the inference of high-dimensional dynamic systems
An image segmentation and registration approach to cardiac function analysis using MRI
Cardiovascular diseases (CVDs) are one of the major causes of death in the world. In recent
years, significant progress has been made in the care and treatment of patients with such
diseases. A crucial factor for this progress has been the development of magnetic resonance
(MR) imaging which makes it possible to diagnose and assess the cardiovascular function
of the patient. The ability to obtain high-resolution, cine volume images easily and safely
has made it the preferred method for diagnosis of CVDs. MRI is also unique in its ability
to introduce noninvasive markers directly into the tissue being imaged(MR tagging) during
the image acquisition process. With the development of advanced MR imaging acquisition
technologies, 3D MR imaging is more and more clinically feasible. This recent development has
allowed new potentially 3D image analysis technologies to be deployed. However, quantitative
analysis of cardiovascular system from the images remains a challenging topic.
The work presented in this thesis describes the development of segmentation and motion
analysis techniques for the study of the cardiac anatomy and function in cardiac magnetic
resonance (CMR) images. The first main contribution of the thesis is the development of a fully
automatic cardiac segmentation technique that integrates and combines a series of state-of-the-art
techniques. The proposed segmentation technique is capable of generating an accurate 3D
segmentation from multiple image sequences. The proposed segmentation technique is robust
even in the presence of pathological changes, large anatomical shape variations and locally
varying contrast in the images.
Another main contribution of this thesis is the development of motion tracking techniques that
can integrate motion information from different sources. For example, the radial motion of
the myocardium can be tracked easily in untagged MR imaging since the epi- and endocardial
surfaces are clearly visible. On the other hand, tagged MR imaging allows easy tracking of
both longitudinal and circumferential motion. We propose a novel technique based on non-rigid
image registration for the myocardial motion estimation using both untagged and 3D tagged MR
images. The novel aspect of our technique is its simultaneous use of complementary information
from both untagged and 3D tagged MR imaging. The similarity measure is spatially weighted
to maximise the utility of information from both images.
The thesis also proposes a sparse representation for free-form deformations (FFDs) using the principles of compressed sensing. The sparse free-form deformation (SFFD) model can
capture fine local details such as motion discontinuities without sacrificing robustness. We
demonstrate the capabilities of the proposed framework to accurately estimate smooth as well
as discontinuous deformations in 2D and 3D CMR image sequences. Compared to the standard
FFD approach, a significant increase in registration accuracy can be observed in datasets with
discontinuous motion patterns.
Both the segmentation and motion tracking techniques presented in this thesis have been
applied to clinical studies. We focus on two important clinical applications that can be
addressed by the techniques proposed in this thesis. The first clinical application aims
at measuring longitudinal changes in cardiac morphology and function during the cardiac
remodelling process. The second clinical application aims at selecting patients that positively
respond to cardiac resynchronization therapy (CRT).
The final chapter of this thesis summarises the main conclusions that can be drawn from the
work presented here and also discusses possible avenues for future research
Robust inversion and detection techniques for improved imaging performance
Thesis (Ph.D.)--Boston UniversityIn this thesis we aim to improve the performance of information extraction from imaging systems through three thrusts. First, we develop improved image formation methods for physics-based, complex-valued sensing problems. We propose a regularized inversion method that incorporates prior information about the underlying field into the inversion framework for ultrasound imaging. We use experimental ultrasound data to compute inversion results with the proposed formulation and compare it with conventional inversion techniques to show the robustness of the proposed technique to loss of data.
Second, we propose methods that combine inversion and detection in a unified framework to improve imaging performance. This framework is applicable for cases where the underlying field is label-based such that each pixel of the underlying field can only assume values from a discrete, limited set. We consider this unified framework in the context of combinatorial optimization and propose graph-cut based methods that would result in label-based images, thereby eliminating the need for a separate detection step.
Finally, we propose a robust method of object detection from microscopic nanoparticle images. In particular, we focus on a portable, low cost interferometric imaging platform and propose robust detection algorithms using tools from computer vision. We model the electromagnetic image formation process and use this model to create an enhanced detection technique. The effectiveness of the proposed technique is demonstrated using manually labeled ground-truth data. In addition, we extend these tools to develop a detection based autofocusing algorithm tailored for the high numerical aperture interferometric microscope
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