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

Robust arbitrary-view gait recognition based on 3D partial similarity matching

Existing view-invariant gait recognition methods encounter difficulties due to limited number of available gait views and varying conditions during training. This paper proposes gait partial similarity matching that assumes a 3-dimensional (3D) object shares common view surfaces in significantly different views. Detecting such surfaces aids the extraction of gait features from multiple views. 3D parametric body models are morphed by pose and shape deformation from a template model using 2-dimensional (2D) gait silhouette as observation. The gait pose is estimated by a level set energy cost function from silhouettes including incomplete ones. Body shape deformation is achieved via Laplacian deformation energy function associated with inpainting gait silhouettes. Partial gait silhouettes in different views are extracted by gait partial region of interest elements selection and re-projected onto 2D space to construct partial gait energy images. A synthetic database with destination views and multi-linear subspace classifier fused with majority voting are used to achieve arbitrary view gait recognition that is robust to varying conditions. Experimental results on CMU, CASIA B, TUM-IITKGP, AVAMVG and KY4D datasets show the efficacy of the propose method

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

Image-based rendering of ancient Chinese artifacts for multi-view displays - a multi-camera approach

Image-based rendering (IBR) is an emerging and promising technology for photo-realistic rendering of scenes and objects from a collection of densely sampled images and videos. This paper proposes an image-based approach to the rendering and multi-view display of ancient Chinese artifacts for cultural heritage preservation. A multiple-camera circular array was constructed to record images of the artifacts. Novel techniques for segmenting and rendering new views of the artifacts from the sampled images are developed. The multiple views so synthesized enable the ancient artifacts to be displayed in modern multi-view displays and conventional stereo systems. Several collections from the University Museum and Art Gallery at the University of Hong Kong are captured and excellent rendering results are obtained. ©2010 IEEE.published_or_final_versionThe 2010 IEEE International Symposium on Circuits and Systems (ISCAS), Paris, France, 30 May-2 June 2010. In IEEE International Symposium on Circuits and Systems Proceedings, 2010, p. 3252-325

Doctor of Philosophy

dissertationNeuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. However, the extremely anisotropic resolution of the data makes segmentation and tracking across slices difficult. Furthermore, the thickness of the slices can make the membranes of the neurons hard to identify. Similarly, structures can change significantly from one section to the next due to slice thickness which makes tracking difficult. This thesis presents a complete method for segmenting many neurons at once in two-dimensional (2D) electron microscopy images and reconstructing and visualizing them in three-dimensions (3D). First, we present an advanced method for identifying neuron membranes in 2D, necessary for whole neuron segmentation, using a machine learning approach. The method described uses a series of artificial neural networks (ANNs) in a framework combined with a feature vector that is composed of image and context; intensities sampled over a stencil neighborhood. Several ANNs are applied in series allowing each ANN to use the classification context; provided by the previous network to improve detection accuracy. To improve the membrane detection, we use information from a nonlinear alignment of sequential learned membrane images in a final ANN that improves membrane detection in each section. The final output, the detected membranes, are used to obtain 2D segmentations of all the neurons in an image. We also present a method that constructs 3D neuron representations by formulating the problem of finding paths through sets of sections as an optimal path computation, which applies a cost function to the identification of a cell from one section to the next and solves this optimization problem using Dijkstras algorithm. This basic formulation accounts for variability or inconsistencies between sections and prioritizes cells based on the evidence of their connectivity. Finally, we present a tool that combines these techniques with a visual user interface that enables users to quickly segment whole neurons in large volumes