46 research outputs found
Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation
Image segmentation is a fundamental and challenging problem in computer
vision with applications spanning multiple areas, such as medical imaging,
remote sensing, and autonomous vehicles. Recently, convolutional neural
networks (CNNs) have gained traction in the design of automated segmentation
pipelines. Although CNN-based models are adept at learning abstract features
from raw image data, their performance is dependent on the availability and
size of suitable training datasets. Additionally, these models are often unable
to capture the details of object boundaries and generalize poorly to unseen
classes. In this thesis, we devise novel methodologies that address these
issues and establish robust representation learning frameworks for
fully-automatic semantic segmentation in medical imaging and mainstream
computer vision. In particular, our contributions include (1) state-of-the-art
2D and 3D image segmentation networks for computer vision and medical image
analysis, (2) an end-to-end trainable image segmentation framework that unifies
CNNs and active contour models with learnable parameters for fast and robust
object delineation, (3) a novel approach for disentangling edge and texture
processing in segmentation networks, and (4) a novel few-shot learning model in
both supervised settings and semi-supervised settings where synergies between
latent and image spaces are leveraged to learn to segment images given limited
training data.Comment: PhD dissertation, UCLA, 202
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From Fully-Supervised, Single-Task to Scarcely-Supervised, Multi-Task Deep Learning for Medical Image Analysis
Image analysis based on machine learning has gained prominence with the advent of deep learning, particularly in medical imaging. To be effective in addressing challenging image analysis tasks, however, conventional deep neural networks require large corpora of annotated training data, which are unfortunately scarce in the medical domain, thus often rendering fully-supervised learning strategies ineffective.This thesis devises for use in a variety of medical image analysis applications a series of novel deep learning methods, ranging from fully-supervised, single-task learning to scarcely-supervised, multi-task learning that makes efficient use of annotated training data. Specifically, its main contributions include (1) fully-supervised, single-task learning for the segmentation of pulmonary lobes from chest CT scans and the analysis of scoliosis from spine X-ray images; (2) supervised, single-task, domain-generalized pulmonary segmentation in chest X-ray images and retinal vasculature segmentation in fundoscopic images; (3) largely-unsupervised, multiple-task learning via deep generative modeling for the joint synthesis and classification of medical image data; and (4) partly-supervised, multiple-task learning for the combined segmentation and classification of chest and spine X-ray images
Open-source virtual bronchoscopy for image guided navigation
This thesis describes the development of an open-source system for virtual bronchoscopy used in combination with electromagnetic instrument tracking. The end application is virtual navigation of the lung for biopsy of early stage cancer nodules. The open-source platform 3D Slicer was used for creating freely available algorithms for virtual bronchscopy. Firstly, the development of an open-source semi-automatic algorithm for prediction of solitary pulmonary nodule malignancy is presented. This approach may help the physician decide whether to proceed with biopsy of the nodule. The user-selected nodule is segmented in order to extract radiological characteristics (i.e., size, location, edge smoothness, calcification presence, cavity wall thickness) which are combined with patient information to calculate likelihood of malignancy. The overall accuracy of the algorithm is shown to be high compared to independent experts' assessment of malignancy. The algorithm is also compared with two different predictors, and our approach is shown to provide the best overall prediction accuracy. The development of an airway segmentation algorithm which extracts the airway tree from surrounding structures on chest Computed Tomography (CT) images is then described. This represents the first fundamental step toward the creation of a virtual bronchoscopy system. Clinical and ex-vivo images are used to evaluate performance of the algorithm. Different CT scan parameters are investigated and parameters for successful airway segmentation are optimized. Slice thickness is the most affecting parameter, while variation of reconstruction kernel and radiation dose is shown to be less critical. Airway segmentation is used to create a 3D rendered model of the airway tree for virtual navigation. Finally, the first open-source virtual bronchoscopy system was combined with electromagnetic tracking of the bronchoscope for the development of a GPS-like system for navigating within the lungs. Tools for pre-procedural planning and for helping with navigation are provided. Registration between the lungs of the patient and the virtually reconstructed airway tree is achieved using a landmark-based approach. In an attempt to reduce difficulties with registration errors, we also implemented a landmark-free registration method based on a balanced airway survey. In-vitro and in-vivo testing showed good accuracy for this registration approach. The centreline of the 3D airway model is extracted and used to compensate for possible registration errors. Tools are provided to select a target for biopsy on the patient CT image, and pathways from the trachea towards the selected targets are automatically created. The pathways guide the physician during navigation, while distance to target information is updated in real-time and presented to the user. During navigation, video from the bronchoscope is streamed and presented to the physician next to the 3D rendered image. The electromagnetic tracking is implemented with 5 DOF sensing that does not provide roll rotation information. An intensity-based image registration approach is implemented to rotate the virtual image according to the bronchoscope's rotations. The virtual bronchoscopy system is shown to be easy to use and accurate in replicating the clinical setting, as demonstrated in the pre-clinical environment of a breathing lung method. Animal studies were performed to evaluate the overall system performance
Basic Science to Clinical Research: Segmentation of Ultrasound and Modelling in Clinical Informatics
The world of basic science is a world of minutia; it boils down to improving even a fraction of a percent over the baseline standard. It is a domain of peer reviewed fractions of seconds and the world of squeezing every last ounce of efficiency from a processor, a storage medium, or an algorithm. The field of health data is based on extracting knowledge from segments of data that may improve some clinical process or practice guideline to improve the time and quality of care. Clinical informatics and knowledge translation provide this information in order to reveal insights to the world of improving patient treatments, regimens, and overall outcomes.
In my world of minutia, or basic science, the movement of blood served an integral role. The novel detection of sound reverberations map out the landscape for my research. I have applied my algorithms to the various anatomical structures of the heart and artery system. This serves as a basis for segmentation, active contouring, and shape priors. The algorithms presented, leverage novel applications in segmentation by using anatomical features of the heart for shape priors and the integration of optical flow models to improve tracking. The presented techniques show improvements over traditional methods in the estimation of left ventricular size and function, along with plaque estimation in the carotid artery.
In my clinical world of data understanding, I have endeavoured to decipher trends in Alzheimer’s disease, Sepsis of hospital patients, and the burden of Melanoma using mathematical modelling methods. The use of decision trees, Markov models, and various clustering techniques provide insights into data sets that are otherwise hidden. Finally, I demonstrate how efficient data capture from providers can achieve rapid results and actionable information on patient medical records. This culminated in generating studies on the burden of illness and their associated costs.
A selection of published works from my research in the world of basic sciences to clinical informatics has been included in this thesis to detail my transition. This is my journey from one contented realm to a turbulent one
Characterising pattern asymmetry in pigmented skin lesions
Abstract. In clinical diagnosis of pigmented skin lesions asymmetric pigmentation is often indicative of
melanoma. This paper describes a method and measures for characterizing lesion symmetry. The estimate of
mirror symmetry is computed first for a number of axes at different degrees of rotation with respect to the
lesion centre. The statistics of these estimates are the used to assess the overall symmetry. The method is
applied to three different lesion representations showing the overall pigmentation, the pigmentation pattern,
and the pattern of dermal melanin. The best measure is a 100% sensitive and 96% specific indicator of
melanoma on a test set of 33 lesions, with a separate training set consisting of 66 lesions