Thom Lobe

Our initial experience of the transaxillary totally endoscopic approach for hemithyroidectomy.

OBJECTIVE: To report our initial experience with the transaxillary totally endoscopic (TATE) approach to the thyroid gland. STUDY DESIGN: A historic cohort study of patients undergoing TATE procedures compared with open procedures for hemi-thyroidectomy with isthmusectomy. SETTING: Private-practice otolaryngology group. SUBJECT AND METHODS: Patients selected for benign thyroid disease confirmed by fine-needle aspiration and requiring hemithyroidectomy with isthmusectomy. A historic cohort study of 24 patients who underwent TATE procedures for hemithyroidectomy with isthmusectomy. Comparison of the first 10 TATE approaches to a control group of 10 consecutive open approaches by the senior authors group. RESULTS: All 24 TATE patients were successful without the need to convert to an open procedure. The TATE approach had longer operative times than the open group (142 vs 105), but these operative times decreased as the number of procedures increased (first five TATE = 170, last five TATE = 114, n = 24, average = 114). No patients had peri- or postoperative complications. CONCLUSIONS: The TATE approach to the thyroid gland is safe and effective. Operative time is longer but decreases with experience. The TATE approach is one option to treat young patients with unilateral benign thyroid disease who are seeking to avoid visible scars and limit morbidity.

Evaluating Mental Workload of Two-Dimensional and Three-Dimensional Visualization for Anatomical Structure Localization

Visualization of medical data in three-dimensional (3D) or two-dimensional (2D) views is a complex area of research. In many fields 3D views are used to understand the shape of an object, and 2D views are used to understand spatial relationships. It is unclear how 2D/3D views play a role in the medical field. Using 3D views can potentially decrease the learning curve experienced with traditional 2D views by providing a whole representation of the patients anatomy. However, there are challenges with 3D views compared with 2D. This current study expands on a previous study to evaluate the mental workload associated with both 2D and 3D views. Twenty-five first-year medical students were asked to localize three anatomical structures--gallbladder, celiac trunk, and superior mesenteric artery--in either 2D or 3D environments. Accuracy and time were taken as the objective measures for mental workload. The NASA Task Load Index (NASA-TLX) was used as a subjective measure for mental workload. Results showed that participants viewing in 3D had higher localization accuracy and a lower subjective measure of mental workload, specifically, the mental demand component of the NASA-TLX. Results from this study may prove useful for designing curricula in anatomy education and improving training procedures for surgeons.

A Framework for Interactive Visualization of Digital Medical Images

The visualization of medical images obtained from scanning techniques such as computed tomography and magnetic resonance imaging is a well-researched field. However, advanced tools and methods to manipulate these data for surgical planning and other tasks have not seen widespread use among medical professionals. Radiologists have begun using more advanced visualization packages on desktop computer systems, but most physicians continue to work with basic two-dimensional grayscale images or not work directly with the data at all. In addition, new display technologies that are in use in other fields have yet to be fully applied in medicine. It is our estimation that usability is the key aspect in keeping this new technology from being more widely used by the medical community at large. Therefore, we have a software and hardware framework that not only make use of advanced visualization techniques, but also feature powerful, yet simple-to-use, interfaces. A virtual reality system was created to display volume-rendered medical models in three dimensions. It was designed to run in many configurations, from a large cluster of machines powering a multiwalled display down to a single desktop computer. An augmented reality system was also created for, literally, hands-on interaction when viewing models of medical data. Last, a desktop application was designed to provide a simple visualization tool, which can be run on nearly any computer at a users disposal. This research is directed toward improving the capabilities of medical professionals in the tasks of preoperative planning, surgical training, diagnostic assistance, and patient education.

Three-dimensional segmentation of tumors from CT image data using an adaptive fuzzy system

A new segmentation method using a fuzzy rule based system to segment tumors in a three-dimensional CT data was developed. To initialize the segmentation process, the user selects a region of interest (ROI) within the tumor in the first image of the CT study set. Using the ROIs spatial and intensity properties, fuzzy inputs are generated for use in the fuzzy rules inference system. With a set of predefined fuzzy rules, the system generates a defuzzified output for every pixel in terms of similarity to the object. Pixels with the highest similarity values are selected as tumor. This process is automatically repeated for every subsequent slice in the CT set without further user input, as the segmented region from the previous slice is used as the ROI for the current slice. This creates a propagation of information from the previous slices, used to segment the current slice. The membership functions used during the fuzzification and defuzzification processes are adaptive to the changes in the size and pixel intensities of the current ROI. The method is highly customizable to suit different needs of a user, requiring information from only a single two-dimensional image. Test cases success in segmenting the tumor from seven of the 10 CT datasets with <10% false positive errors and five test cases with <10% false negative errors. The consistency of the segmentation results statistics also showed a high repeatability factor, with low values of inter- and intra-user variability for both methods.

Isis: Patient Data Visualization and Surgical Planning in an Interactive Virtual Environment

As medical scanning technology continues to accommodate the need for higher quality medical imaging, there is a continuing need for additional research in efficient ways of extracting crucial information from these vast amounts of data. The visualization software, Isis, has been developed to view and manipulate digital medical images in an immersive environment for surgical planning. Isis is designed to display any DICOM/PACS compatible three-dimensional image data for visualization and interaction in an immersive environment. Pseudo-coloring can be applied in real time, with multiple interactive clipping planes to slice into the volume for an interior view, and the windowing feature controls the tissue density ranges to display. Features such as virtual trocars placement, tumor inspection, and an endoscopic view provides surgeons with essential tools for surgical planning. A wireless gamepad controller and an intuitive menu interface allow the user to interact with the software. By wearing a pair of stereo glasses, the surgeon is immersed within the model, providing a sense of realism as if the surgeon is “inside” the patient.Copyright

Automated probabilistic segmentation of tumors from CT data using spatial and intensity properties

This paper presents a probabilistic segmentation process developed using the selection process from the Simulated Annealing optimization algorithm as a foundation. This process allows pixels to be segmented based on a probability selection process. An automated seed and search region selection processes multiple image slices automatically as an objects size, shape, and location changes between subsequent slices. Apart from the first slice in the dataset, where the user manually selects the seed and search region for segmentation, the method performs automatically for all other slices. From the test cases, the automated seed selection process was efficient in searching for new seed locations, as the object changed size, location, and orientation in each slice of the study. Segmentation results from both algorithms showed success in segmenting the tumor from nine of the ten CT datasets with less than 17% false positive errors and seven test cases with less than 20% false negative errors. Statistical testing of the results showed a high repeatability factor, with low values of inter- and intra-user variability. Furthermore, the method requires information from only a two-dimensional image data at a time to accommodate performance on a regular personal computer.

Adaptive Fuzzy Segmentation of Tumors in Three-Dimensional Computed Tomography (CT) Image Data

The continuing advancement of computed tomography (CT) technology has improved the analysis and visualization of tumor data. As imaging technology continues to accommodate the need for high quality medical image data, this encourages the research for more efficient ways of extracting crucial information from these vast amounts of data. A new segmentation method using a fuzzy rule based system to segment tumors in a three-dimensional CT data has been developed. To initialize the segmentation process, the user selects the region of interest (ROI) within the tumor in the first image of the CT study set. Using the ROI’s spatial and intensity properties, fuzzy inputs are generated for use in the fuzzy inference system. From a set of predefined fuzzy rules, the system generates a defuzzified output for every pixel in terms of similarity to the object. Pixels with the highest similarity values are selected to be the tumor. This process is repeated for every subsequent slice in the CT set, and the segmented region from the previous slice is used as the ROI for the current slice. This creates a propagation of information from the previous slices, to be used to segment the current slice. The membership functions used during the fuzzification and defuzzification processes are adaptive to the changes in the size and pixel intensities of the current ROI. The proposed method is highly customizable to suit different needs of a user, requiring information from only a single two-dimensional image. Implementing the fuzzy segmentation on two distinct CT sets, the fuzzy segmentation algorithm was able to successfully extract the tumor from the CT image data. Based on the results statistics, the developed segmentation technique is approximately 96% accurate when compared to the results of manual segmentations performed.Copyright