Segmentation and Evaluation of Adipose Tissue from Whole Body MRI Scans

Accurate quantification of total body and the distribution of regional adipose tissue using manual segmentation is a challenging problem due to the high variation between manual delineations. Manual segmentation also requires highly trained experts with knowledge of anatomy. We present a hybrid segmentation method that provides robust delineation results for adipose tissue from whole body MRI scans. A formal evaluation of accuracy of the segmentation method is performed. This semi-automatic segmentation algorithm reduces significantly the time required for quantification of adipose tissue, and the accuracy measurements show that the results are close to the ground truth obtained from manual segmentations

A Novel Quantification Method for Determining Previously Undetected Silent Infarcts on MR-perfusion in Patients Following Carotid Endarterectomy

The purpose of this paper is to evaluate the post-operative Magnetic Resonance Perfusion (MRP) scans of patients undergoing carotid endarterectomy (CEA), using a novel image-analysis algorithm, to determine if post-operative neurocognitive decline is associated with cerebral blood flow changes. CEA procedure reduces the risk of stroke in appropriately selected patients with significant carotid artery stenosis. However, 25% of patients experience subtle cognitive deficits after CEA compared to a control group. It was hypothesized that abnormalities in cerebral blood flow (CBF) are responsible for these cognitive deficits. A novel algorithm for analyzing MRperfusion (MRP) scans to identify and quantify the amount of CBF asymmetry in each hemisphere was developed and to quantify the degree of relative difference between three corresponding vascular regions in the ipsilateral and contralateral hemispheres, the Relative Difference Map (RDM). Patients undergoing CEA and spine surgery (controls) were examined preoperatively, and one day postoperatively with a battery of neuropsychometric (NPM) tests, and labeled “injured” patients with significant cognitive deficits, and “normal” if they demonstrated no decline in neurocognitive function. There are apparently significant RDM differences with MRP scans between the two hemispheres in patients with cognitive deficits which can be used to guide expert reviews of the imagery. The proposed methodology aids in the analysis of MRP parameters in patients with cognitive impairment

A Novel Drill Set for the Enhancement and Assessment of Robotic Surgical Performance

Background: There currently exist several training modules to improve performance during video-assisted surgery. The unique characteristics of robotic surgery make these platforms an inadequate environment for the development and assessment of robotic surgical performance. Methods: Expert surgeons (n=4) (greater than 50 clinical robotic procedures and greater than 2 years of clinical robotic experience) were compared to novice surgeons (n=17) (less than 5 clinical cases and limited laboratory experience) using the da Vinci Surgical System. Seven drills were designed to simulate clinical robotic surgical tasks. Performance score was calculated by the equation Time to Completion + (minor error) x 5 + (major error) x 10. The Robotic Learning Curve (RLC) was expressed as a trend line of the performance scores corresponding to each repeated drill. Results: Performance scores for experts were better than novices in all 7 drills (p less than 0.05). The RLC for novices reflected an improvement in scores (p less than 0.05). In contrast, experts demonstrated a flat RLC for 6 drills and an improvement in one drill (p=0.027). Conclusion: This new drill set provides a framework for performance assessment during robotic surgery. The inclusion of particular drills and their role in training robotic surgeons of the future awaits larger validation studies

Dilation Based Modeling of Perfusion Datasets

A new approach to the modeling of the marker in Perfusion CT and Perfusion MR datasets is outlined and initial results given. The technique is based on estimation of the dilation and delay of an estimated bolus shape and a template fit to an new solution of the heat equation. Initial results are provided

Hybrid Segmentation of the Visible Human Data

In this this paper we develop and test new hybrid methods for segmenting the Visible Human color cryosection and radiological (patient) data (e.g. CT, MRI, PET). The novelty stems from the integration of region-based and deformable model-based segmentation methods with a variety of region-based and statistical methods which aims toward the development of segmentation methods that yield high precision