The Bethesda Interobserver Reproducibility Study (BIRST): a web-based assessment of the Bethesda 2001 System for classifying cervical cytology.
(25/87)
BACKGROUND: The Bethesda System (TBS) along with its companion atlas was updated in 2001 to improve standardization, clarity, and reproducibility of cervical cytology reporting. METHODS: The authors used a novel web-based format to compare assessments of 77 images demonstrating a range of classical and borderline cytologic changes by a self-selected group of United States cytotechnologists (n = 216) and pathologists (n = 185). RESULTS: Participants were highly experienced, with 71.2% of cytotechnologists and 53.0% of pathologists reporting >10 years of practice. The mean percentage of exact agreement with the panel was slightly though significantly higher for cytotechnologists (57.0%) compared with pathologists (53.4%), adjusted for experience (P = .004); cervical cytology percentage effort (P = .0005); or cervical accession volume (P = .0002). Compared with the TBS panel, exact agreement was achieved for 55.1% of image ratings compared with 82.3% agreement at the level of Negative vs non-Negative for images with a single-panel interpretation. Agreement with the panel was highest for images classified as Low-Grade Squamous Intraepithelial Lesion and lowest for Atypical Squamous Cells qualified as either of Undetermined Significance or Cannot Exclude a High-Grade Squamous Intraepithelial Lesion. Reviewers were less sensitive in identifying high-grade glandular lesions than they were in identifying high-grade squamous lesions at any threshold (P < .001). CONCLUSIONS: Morphologic appearances of images were more important determinants than participants' academic or professional degrees with regard to interobserver reproducibility in classifying cervical cytology images. Experienced cytotechnologists and pathologists performed similarly. Participants achieved higher sensitivity for identifying high-grade squamous lesions than they did for high-grade glandular lesions. These findings demonstrated that web-based studies may be useful in assessing interobserver agreement in classifying images. (+info)
An atlas-based method to compensate for brain shift: preliminary results.
(26/87)
Compensating for intraoperative brain shift using computational models has shown promising results. Since computational time is an important factor during neurosurgery, a priori knowledge of the possible sources of deformation can increase the accuracy of model-updated image-guided systems. In this paper, a strategy to compensate for distributed loading conditions in the brain such as brain sag, volume changes due to drug reactions, and brain swelling due to edema is presented. An atlas of model deformations based on these complex loading conditions is computed preoperatively and used with a constrained linear inverse model to predict the intraoperative distributed brain shift. This relatively simple inverse finite-element approach is investigated within the context of a series of phantom experiments, two in vivo cases, and a simulation study. Preliminary results indicate that the approach recaptured on average 93% of surface shift for the simulation, phantom, and in vivo experiments. With respect to subsurface shift, comparisons were only made with simulation and phantom experiments and demonstrated an ability to recapture 85% of the shift. This translates to a remaining surface and subsurface shift error of 0.7+/-0.3 mm, and 1.0+/-0.4 mm, respectively, for deformations on the order of 1cm. (+info)
Volumetric neuroimage analysis extensions for the MIPAV software package.
(27/87)
We describe a new collection of publicly available software tools for performing quantitative neuroimage analysis. The tools perform semi-automatic brain extraction, tissue classification, Talairach alignment, and atlas-based measurements within a user-friendly graphical environment. They are implemented as plug-ins for MIPAV, a freely available medical image processing software package from the National Institutes of Health. Because the plug-ins and MIPAV are implemented in Java, both can be utilized on nearly any operating system platform. In addition to the software plug-ins, we have also released a digital version of the Talairach atlas that can be used to perform regional volumetric analyses. Several studies are conducted applying the new tools to simulated and real neuroimaging data sets. (+info)
Atlas stratification.
(28/87)
The process of constructing an atlas typically involves selecting one individual from a sample on which to base or root the atlas. If the individual selected is far from the population mean, then the resulting atlas is biased towards this individual. This, in turn, may bias any inferences made with the atlas. Unbiased atlas construction addresses this issue by either basing the atlas on the individual which is the median of the sample or by an iterative technique whereby the atlas converges to the unknown population mean. In this paper, we explore the question of whether a single atlas is appropriate for a given sample or whether there is sufficient image based evidence from which we can infer multiple atlases, each constructed from a subset of the data. We refer to this process as atlas stratification. Essentially, we determine whether the sample, and hence the population, is multi-modal and is best represented by an atlas per mode. In this preliminary work, we use the mean shift algorithm to identify the modes of the sample and multidimensional scaling to visualize the clustering process on clinical MRI neurological image datasets. (+info)
Multimodal, multidimensional models of mouse brain.
(29/87)
Naturally occurring mutants and genetically manipulated strains of mice are widely used to model a variety of human diseases. Atlases are an invaluable aid in understanding the impact of such manipulations by providing a standard for comparison and to facilitate the integration of anatomic, genetic, and physiologic observations from multiple subjects and experiments. We have developed digital atlases of the C57BL/6J mouse brain (adult and neonate) as comprehensive frameworks for storing and accessing the myriad types of information about the mouse brain. Along with raw and annotated images, these contain database management systems and a set of tools for comparing information from different techniques and different animals. Each atlas establishes a canonical representation of the mouse brain and provides the tools for the manipulation and analysis of new data. We describe both these atlases and discuss how they may be put to use in organizing and analyzing data from mouse models of epilepsy. (+info)
A rhesus monkey reference label atlas for template driven segmentation.
(30/87)