Robotic autopositioning of the operating microscope.

BACKGROUND: Use of the operating microscope has become pervasive since its introduction to the neurosurgical world. Neuronavigation fused with the operating microscope has allowed accurate correlation of the focal point of the microscope and its location on the downloaded imaging study. However, the robotic ability of the Pentero microscope has not been utilized to orient the angle of the microscope or to change its focal length to hone in on a predefined target. OBJECTIVE: To report a novel technology that allows automatic positioning of the operating microscope onto a set target and utilization of a planned trajectory, either determined with the StealthStation S7 by using preoperative imaging or intraoperatively with the microscope. METHODS: By utilizing the current motorized capabilities of the Zeiss OPMI Pentero microscope, a robotic autopositioning feature was developed in collaboration with Surgical Technologies, Medtronic, Inc. (StealthStation S7). The system is currently being tested at the Barrow Neurological Institute. RESULTS: Three options were developed for automatically positioning the microscope: AutoLock Current Point, Align Parallel to Plan, and Point to Plan Target. These options allow the microscope to pivot around the lesion, hover in a set plane parallel to the determined trajectory, or rotate and point to a set target point, respectively. CONCLUSION: Integration of automatic microscope positioning into the operative workflow has potential to increase operative efficacy and safety. This technology is best suited for precise trajectories and entry points into deep-seated lesions.

Automatic and manual methodology for three-dimensional measurements of distal femoral gender differences and femoral component placement.

Our study aimed to introduce an automatic three-dimensional method for measuring the distal femur and identifying potential gender differences and the effects on femoral component placement in total knee arthroplasty. Three hundred forty-two femora were scanned with computed tomography. Automatic and manual bone resection and component placement were compared using a virtual resection tool. For standard component use, 77.3% of the femora were male and 23.1% were female. For gender-specific component use, 91% were female and 7.3% were male. Surgeon errors in both component translation and rotation existed but were minimal. From these results, gender alone did not dictate component use in primary total knee arthroplasty. The restoration of femoral condylar profile in 3 dimensions can be obtained by accurately measuring patient distal femoral anatomy and the appropriate femoral component design selection. Additional bone cuts, soft-tissue maneuvers, and adverse outcomes in fitting the patient to the femoral component may be avoided.

Assessing A.L. 288-1 femur length using computer-aided three-dimensional reconstruction.

Fossil reconstruction remains a requisite task for many types of paleoanthropological research. While reconstructions are traditionally accomplished by hand, computer modeling offers a novel and mathematically rigorous approach while providing advantages over the manual process. Computer models of fossil specimens can be reflected, scaled, and aligned in virtual space with relative ease; therefore, it is simple to generate multiple reconstructions to find the "best" one. Here we report on the reconstruction of A.L. 288-1ap (left femur) using three-dimensional computer models. Our "best" reconstruction has a maximum length of 277mm, which is very near both the 280mm originally estimated and the frequently cited 281mm.

Patella sex determination by 3D statistical shape models and nonlinear classifiers.

Sex determination is one of the essential steps in personal identification of an individual from skeletal remains. Most elements of the skeleton have been subjected to discriminant function analysis for sex estimation, but little work has been done in terms of the patella. This paper proposes a new sex determination method from the patella using a novel automated feature extraction technique. A dataset of 228 patellae (95 females and 133 males) was amassed from the William M. Bass Donated Skeletal Collection from the University of Tennessee and was subjected to noninvasive high resolution computed tomography (CT). After the CT data were segmented, a set of features was automatically extracted, normalized, and ranked. The segmentation process with surface smoothing minimizes the noise from enthesophytes and ultimately allows our methods to distinguish variations in patellar morphology. These features include geometric features, moments, principal axes, and principal components. A feature vector of dimension 45 for each subject was then constructed. A set of statistical and supervised neural network classification methods were used to classify the sex of the patellar feature vectors. Nonlinear classifiers such as neural networks have been used in previous research to analyze several medical diagnosis problems, including quantitative tissue characterization and automated chromosome classification. In this paper, different classification methods were compared. Classification success ranged from 83.77% average classification rate using labels from a Fuzzy C-Means (FCM) clustering step, to 90.3% for linear discriminant classification (LDC). We obtained results of 96.02% and 93.51% training and testing classification rates, respectively, using feed-forward backpropagation neural networks (NN). These promising results using newly developed features and the application of nonlinear classifiers encourage the usage of these methods in forensic anthropology for identifying the sex of an individual from incomplete skeletons retaining at least one patella.

3D statistical shape models of patella for sex classification.

This paper proposes a new sex classification method from patellae using a novel automated feature extraction technique. A dataset of 228 patellae (95 females and 133 males) was collected and CT scanned. After the CT data was segmented, a set of features was automatically extracted, normalized, and ranked. These features include geometric features, moments, principal axes, and principal components. A feature vector of 45 dimensions for each subject was then constructed. A set of statistical and supervised neural network classification methods were used to classify the patellar feature vectors according to sex. Different classification methods were compared. Classification success ranged from 83.77% average classification rate with labeling using fuzzy C-means method (FCM), to 90.3% for linear discriminant function (LDF) analysis. We obtained results of 96.02% and 93.51% training and testing classification rates (respectively) using feedforward backpropagation neural networks (NN). These promising results encourage the usage of this method in forensic anthropology for identifying the sex from incomplete skeletons containing at least one patella.