Computerized analysis of coronary artery disease: Performance evaluation of segmentation and tracking of coronary arteries in CT angiograms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134780/1/mp0294.pd

Vessel tractography using an intensity based tensor model with branch detection

In this paper, we present a tubular structure seg- mentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmen- tation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient Computed Tomography Angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert

Coronary Artery Segmentation and Motion Modelling

Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated

A framework for intracranial saccular aneurysm detection and quantification using morphological analysis of cerebral angiograms

Reliable early prediction of aneurysm rupture can greatly help neurosurgeons to treat aneurysms at the right time, thus saving lives as well as providing significant cost reduction. Most of the research efforts in this respect involve statistical analysis of collected data or simulation of hemodynamic factors to predict the risk of aneurysmal rupture. Whereas, morphological analysis of cerebral angiogram images for locating and estimating unruptured aneurysms is rarely considered. Since digital subtraction angiography (DSA) is regarded as a standard test by the American Stroke Association and American College of Radiology for identification of aneurysm, this paper aims to perform morphological analysis of DSA to accurately detect saccular aneurysms, precisely determine their sizes, and estimate the probability of their ruptures. The proposed diagnostic framework, intracranial saccular aneurysm detection and quantification, first extracts cerebrovascular structures by denoising angiogram images and delineates regions of interest (ROIs) by using watershed segmentation and distance transformation. Then, it identifies saccular aneurysms among segmented ROIs using multilayer perceptron neural network trained upon robust Haralick texture features, and finally quantifies aneurysm rupture by geometrical analysis of identified aneurysmic ROI. De-identified data set of 59 angiograms is used to evaluate the performance of algorithms for aneurysm detection and risk of rupture quantification. The proposed framework achieves high accuracy of 98% and 86% for aneurysm classification and quantification, respectively

Whole‐brain microcirculation detection after ischemic stroke based on swept‐source optical coherence tomography

The occurrence and development of ischemic stroke are closely related to cerebral blood flow. Real‐time monitoring of cerebral perfusion level is very useful for understanding the mechanisms of the disease. A wide field of view (FOV) is conducive to capturing lesions and observing the progression of the disease. In this paper, we attempt to monitor the whole‐brain microcirculation in middle cerebral artery occlusion (MCAO) rats over time using a wide FOV swept‐source OCT (SS‐OCT) system. A constrained image registration algorithm is used to remove motion artifacts that are prone to occur in a wide FOV angiography. During ischemia, cerebral perfusion levels in the left and right hemispheres, as well as in the whole brain were quantified and compared. Changes in the shape and location of blood vessels were also recorded. The results showed that the trend in cerebral perfusion levels of both hemispheres was highly consistent during MCAO, and the position of the blood vessels varied over time. This work will provide new insights of ischemic stroke and is helpful to assess the effectiveness of potential treatment strategies.En face maximum intensity projections (MIP) of the whole‐brain vascular networks obtained by wide field of view (FOV) swept‐source optical coherence tomography (SS‐OCT) system.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151895/1/jbio201900122_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151895/2/jbio201900122.pd

Computer integrated system: medical imaging & visualization

The intent of this book’s conception is to present research work using a user centered design approach. Due to space constraints, the story of the journey, included in this book is relatively brief. However we believe that it manages to adequately represent the story of the journey, from its humble beginnings in 2008 to the point where it visualizes future trends amongst both researchers and practitioners across the Computer Science and Medical disciplines. This book aims not only to present a representative sampling of real-world collaboration between said disciplines but also to provide insights into the different aspects related to the use of real-world Computer Assisted Medical applications. Readers and potential clients should find the information particularly useful in analyzing the benefits of collaboration between these two fields, the products in and of their institutions. The work discussed here is a compilation of the work of several PhD students under my supervision, who have since graduated and produced several publications either in journals or proceedings of conferences. As their work has been published, this book will be more focused on the research methodology based on medical technology used in their research. The research work presented in this book partially encompasses the work under the MOA for collaborative Research and Development in the field of Computer Assisted Surgery and Diagnostics pertaining to Thoracic and Cardiovascular Diseases between UPM, UKM and IJN, spanning five years beginning from 15 Feb 2013

Adaptive Segmentation Of Cardiovascular Vessels

Coronary collateral vessels may contribute to survival after myocardial infarction by providing blood to the cardiac muscle after coronary arterial occlusion. However, these vessels are not present in all people and can develop after infarction and in some cases they develop prior to infarction for reasons not fully understood. The goal of this thesis is to investigate the segmentation of coronary collateral vessels from micro-computed tomography (microCT) images of a mouse's heart. A problem limiting study of collateral vessels is the exceedingly small size and correspondingly low blood flow of these vessels, making the regions of interest (ROI) below the resolution of most imaging modalities. Segmentation of vessels is a challenge for all imaging modalities and organs. There is no standard algorithm or method that works for all images, therefore, a combination of multiple approaches were used to address this problem

Digital ocular fundus imaging: a review

Ocular fundus imaging plays a key role in monitoring the health status of the human eye. Currently, a large number of imaging modalities allow the assessment and/or quantification of ocular changes from a healthy status. This review focuses on the main digital fundus imaging modality, color fundus photography, with a brief overview of complementary techniques, such as fluorescein angiography. While focusing on two-dimensional color fundus photography, the authors address the evolution from nondigital to digital imaging and its impact on diagnosis. They also compare several studies performed along the transitional path of this technology. Retinal image processing and analysis, automated disease detection and identification of the stage of diabetic retinopathy (DR) are addressed as well. The authors emphasize the problems of image segmentation, focusing on the major landmark structures of the ocular fundus: the vascular network, optic disk and the fovea. Several proposed approaches for the automatic detection of signs of disease onset and progression, such as microaneurysms, are surveyed. A thorough comparison is conducted among different studies with regard to the number of eyes/subjects, imaging modality, fundus camera used, field of view and image resolution to identify the large variation in characteristics from one study to another. Similarly, the main features of the proposed classifications and algorithms for the automatic detection of DR are compared, thereby addressing computer-aided diagnosis and computer-aided detection for use in screening programs.Fundação para a Ciência e TecnologiaFEDErPrograma COMPET

Generalizable automated pixel-level structural segmentation of medical and biological data

Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution. This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D structural segmentation in a more generalizable manner, yet has enough adaptability to address a number of specific image modalities, spanning retinal funduscopy, sequential fluorescein angiography and two-photon microscopy. The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D. To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-) pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations. Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this into consideration, we introduce a 5D orientation mapping to capture these orientation properties. This mapping is incorporated into the local feature map description prior to a learning machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods. For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces