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

Segmentation of the Oral and Facial Regions from Imaging Modalities with Reduced or No Ionizing Radiation

Ph.DDOCTOR OF PHILOSOPH

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society