No description available.

The hippocampus is a complex brain structure that has been studied extensively and is subject to abnormal structural change in various neuropsychiatric disorders. The highest definition in vivo method of visualizing the anatomy of this structure is structural Magnetic Resonance Imaging (MRI). Gross structure can be assessed by the naked eye inspection of MRI scans but measurement is required to compare scans from individuals within normal ranges, and to assess change over time in individuals. The gold standard of such measurement is manual tracing of the boundaries of the hippocampus on scans. This is known as a Region Of Interest (ROI) approach. ROI is laborious and there are difficulties with test-retest and inter-rater reliability. These difficulties are primarily due to uncertainty in designation of the hippocampus boundary. An improved, less labour intensive and more reliable method is clearly desirable. This thesis describes a fully automated hybrid methodology that is able to first locate and then extract hippocampal volumes from 3D 1.5T MRI T1 brain scans automatically. The hybrid algorithm uses brain atlas mappings and fuzzy inference to locate hippocampal areas and create initial hippocampal boundaries. This initial location is used to seed a deformable manifold algorithm. Rule based deformations are then applied to refine the estimate of the hippocampus locations. Finally, the hippocampus boundaries are corrected through an inference process that assures adherence to an expected hippocampus volume. The ICC values of this methodology when compared to the manual segmentation of the same hippocampi result in a 0.73 for the left and 0.81 for the right hippocampi. These values both fall within the range of reliability testing according to the manual ‘gold standard’ technique. Thus, this thesis describes the development and validation of a genuinely automated approach to hippocampal volume extraction of potential utility in studies of a range of neuropsychiatric disorders and could eventually find clinical applications.

The recurrent laryngeal nerve (RLN) innervates all the intrinsic muscles of the larynx that are responsible for human vocalization and language. The RLN runs along the tracheoesophageal groove bilaterally and is often accidentally damaged or transected during head and neck surgical procedures. RLN palsy and vocal cord paralysis are the most common and serious post op complications of thyroid surgeries. Patients who suffer from RLN injury can develop unilateral or bilateral vocal fold paralysis (BVFP). Theoretically, selective reinnervation of the posterior cricoarytenoid muscle would be the best treatment for BVFP. The phrenic nerve has been shown in several studies to be the best candidate to anastomose to the distal end of a severed RLN to restore glottal abduction. Successful PCA reinnervation has been sporadically achieved in both human patients and in animal models. Another notable ramification of recurrent laryngeal nerve injury is vocal instability caused by the alteration of mechanical properties within the larynx. In phonosurgery, alterations to the position and framework of the laryngeal apparatus are made to improve voice quality. Accurate and realistic synthetic models are greatly needed to predict the outcome of various adjustments to vocal cord tension and position that could be made surgically. Despite the sporadically successful attempts at PCA reinnervation, thus far, there are still several deficits in our anatomical familiarity and technological capability, which hinder the regularity of successful PCA reinnervation surgeries and our capacity to generate synthetic models of the human larynx that are both realistic and functional. We will address three of these deficits in this project using the porcine larynx as a model. Firstly, we will identify the anatomical variations of the porcine recurrent laryngeal nerve branches. A microscribe digitizer will be used to create three-dimensional mapping of the recurrent laryngeal nerve branches that are relevant to the posterior cricoarytenoid muscle and the abduction of the vocal folds. Secondly, we will develop a magnetic resonance imaging technique to correlate recurrent laryngeal nerve branching patterns with high-resolution MR images that can be used to determine the branching patterns present in a given specimen without surgery. Lastly, we will determine the distribution and composition of different tissue types found within human vocal folds. High resolution MRI, and Mallory's trichrome and H&E histological staining will be used to distinguish and identify the tissue composition of the vocal folds and surrounding laryngeal structures. Detailed information regarding vocal fold tissue composition and histological geometry will enable laryngeal modelers to select more sophisticated and life-like materials with which to construct synthetic vocal fold models.

Background: Volumetric analysis of brain structures from structural Mag- netic Resonance (MR) images advances the understanding of the brain by providing means to study brain morphometric changes quantitatively along aging, development, and disease status. Due to the recent increased emphasis on large-scale multicenter brain MR study design, the demand for an automated brain MRI processing tool has increased as well. This dissertation describes an automatic segmentation framework for subcortical structures of brain MRI that is robust for a wide variety of MR data. Method: The proposed segmentation framework, BRAINSCut, is an inte- gration of robust data standardization techniques and machine-learning approaches. First, a robust multi-modal pre-processing tool for automated registration, bias cor- rection, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. The segmentation framework was then constructed to achieve robustness for large-scale data via the following comparative experiments: 1) Find the best machine-learning algorithm among several available approaches in the field. 2) Find an efficient intensity normalization technique for the proposed region-specific localized normalization with a choice of robust statistics. 3) Find high quality features that best characterize the MR brain subcortical structures. Our tool is built upon 32 handpicked multi-modal muticenter MR images with man- ual traces of six subcortical structures (nucleus accumben, caudate nucleus, globus pallidum, putamen, thalamus, and hippocampus) from three experts. A fundamental task associated with brain MR image segmentation for re- search and clinical trials is the validation of segmentation accuracy. This dissertation evaluated the proposed segmentation framework in terms of validity and reliability. Three groups of data were employed for the various evaluation aspects: 1) traveling human phantom data for the multicenter reliability, 2) a set of repeated scans for the measurement stability across various disease statuses, and 3) a large-scale data from Huntington's disease (HD) study for software robustness as well as segmentation accuracy. Result: Segmentation accuracy of six subcortical structures was improved with 1) the bias-corrected inputs, 2) the two region-specific intensity normalization strategies and 3) the random forest machine-learning algorithm with the selected feature-enhanced image. The analysis of traveling human phantom data showed no center-specific bias in volume measurements from BRAINSCut. The repeated mea- sure reliability of the most of structures also displayed no specific association to disease progression except for caudate nucleus from the group of high risk for HD. The constructed segmentation framework was successfully applied on multicenter MR data from PREDICT-HD [133] study ( < 10% failure rate over 3000 scan sessions pro- cessed). Conclusion: Random-forest based segmentation method is effective and robust to large-scale multicenter data variation, especially with a proper choice of the intensity normalization techniques. Benefits of proper normalization approaches are more apparent compared to the custom set of feature-enhanced images for the ccuracy and robustness of the segmentation tool. BRAINSCut effectively produced subcortical volumetric measurements that are robust to center and disease status with validity confirmed by human experts and low failure rate from large-scale multicenter MR data. Sample size estimation, which is crutial for designing efficient clinical and research trials, is provided based on our experiments for six subcortical structures.

One of the main problems in neurosurgery is knowledge about human brain. It's very important to see the whole brain with its critical neurostructures in virtual reality. This document is about three dimensional human brain visualization strategies. Review most recently used three dimensional objects building strategies from two dimensional medical MRI images. This task was split into 4 significant problems: image segmentation, point-sets correspondence, image registration and its frequently used transformation functions with image matching measurements. All these problems were addressed reader to show most recently used algorithms with advantages and disadvantages. Atlas types, patterns and maps survey was introduced with widely popular brain model coordinate systems. In order to find a better correspondence between two point sets it was modeled a new robust and accurate Overhauser spline points location optimization algorithm. Instead of deletion outlier points from overloaded point set, this algorithm generates more points in other set at optimized points locations. Determination of an accurate point location and choosing the correct transformation function are the key steps in registration process. Whereas registration is vital task in precise human brain visualization for neurosurgeries at preoperative and intraoperative process.