Neopath, Inc.

Tripath Imaging, Inc.

Chih-Chau L Kuan

Shih-Jong James Lee

Seho Oh

Wendy R Bannister

Michael G Meyer

Paul S Wilhelm

Mikel D Rosenlof

Robert C Schmidt

Dayle G Ellison

Larry A Nelson

Keith L Frost

Florence W Patten

Michael J Seo

William E Ortyn

Jon W Hayenga

James K Riley

Dynamic control of the processing flow of an image analyzer such as a biological specimen analyzer as processing proceeds. Data collected and processed from a specimen under analysis, such as a biological specimen on a microscope slide, determines the fate of further processing. If there is enough evidence, based on the data collected from a slide, to make a decision with sufficient confidence, the processing of the slide can be stopped and a decision may be rendered. By avoiding unnecessary additional computation system throughput may be enhanced. Otherwise, data collection and computation continues until either certain termination criteria are met or no more data is left to acquire. This slide-dependent control and decision making method flexibly limits the amount of computation required to reach a system decision about a specimen. By evaluating analysis processing continuously a maximum signal to noise ratio may be achieved by preventing additional noise from entering the analysis and thus swamping signal information.

Dynamic control and decision making method and apparatus

A method and apparatus for optimizing biological and cytological specimen screening and diagnosis. A slide review process is recommended for cytological specimen screening to identify abnormal sub-populations for further review and also diagnosis by a human expert. An automated screener processes a cytological specimen. Using a slide score generated by the automated screener, a slide review process using a slide score classification is determined. The recommendation of slide review processes improves overall performance of the screening process as measured by sensitivity to abnormal specimens, and at the same time reduces the work load of a human reviewer. The system also effectively and smoothly integrates the process of initial screening of the specimen with the process of further review of the specimen and final diagnosis of the specimen.

Method and apparatus for optimizing biological and cytological specimen screening and diagnosis

An incremental concurrent learning method starts with providing potential defects and fabrication information and a primary classification rule and secondary classification rule selection from a knowledge defect database from multiple products with different process cycles. The method then performs a truth inquiry to update a classification rule database for use by the primary classification rule and secondary classification rule selection. The method performs a primary defect classification and checks the confidence of the classification, and performs a secondary defect classification if the confidence is not high. If the confidence of the secondary defect classification is not high, a new defect may have been discovered and a novelty defect detection step is performed to define artifacts or potential new defect types to provide information for the truth inquiry.

Method and apparatus for incremental concurrent learning in automatic

A free-lying cell classifier. An automated microscope system comprising a computer and high speed processing field of view processors identifies free-lying cells. An image of a biological specimen is obtained and the image is segmented to create a set of binary masks. The binary masks are used by a feature calculator to compute the features that characterize objects of interest including free-lying cells, artifacts and other biological objects. The objects are classified to identify their type, their normality or abnormality or their identification as an artifact. The results are summarized and reported. A stain evaluation of the slide is performed as well as a typicality evaluation. The robustness of the measurement is also quantified as a classification confidence value. The free-lying cell evaluation is used by an automated cytology system to classify a biological specimen slide.

Apparatus for the identification of free-lying cells

Measurement of the optical transfer function of an optical system imaging a bar pattern. A one dimensional fast Fourier transform processes sampled image data from the bar pattern. A model of the system utilizes robust measurements of the period, duty cycle and center of each stripe in the pattern. A signal alias free optical transfer function is estimated from the plurality of one dimensional frequency representation of the signal. An idealized bar pattern synthesized from the measured parameters of period, duty cycle and the center of each stripe in the pattern generates an ideal optical transfer function. The noise reduced optical transfer function, OTF, is estimated from OTF of the signal and OTF of the synthesized bar pattern.

Method and apparatus for alias free measurement of optical transfer

The invention detects areas of interest at low magnification, locating possible abnormal cells or other cells of interest using image processing and statistical pattern recognition techniques. Next, at high magnification, the areas identified at low magnification are re-examined. The information from the low magnification and high magnification scans is collated and a determination is made about the slide--whether it is normal, abnormal, contains endocervical component, and so forth. The invention also provides a method and apparatus to train object feature and slide feature classifiers. The invention provides an automated cytology system that can process training slides for use in a feed back classifier development environment. The invention also can classify endocervical groups of cells.

Cytological slide scoring apparatus

A biological specimen classification strategy employs identification and integration of multiple cell patterns. An automated microscope acquires an image of a biological specimen such as a Pap smear and provides an image output to biological classifiers. The classifiers independently detect and classify a number of specimen types and provide classifications to an output field of view integrator. The integrator integrates the classifications. The integrated output then determines whether the classifiers should be reapplied to the image.

Apparatus for identification and integration of multiple cell patterns

Coverslip detection locating all four coverslip edges. A field of view processor receives image data from a charge coupled device camera. The charge coupled device camera images a slide and coverslip that is mounted on a movable frame. The slide and coverslip are illuminated from below with a uniform light source. The moveable frame is under computer control and moves in response to the field of view processor. The field of view processor locates the coverslip by first positioning the movable frame to view a portion of the slide on a predetermined potion of the slide within a predetermined area of the slide. The slide is then re-imaged after the movable frame moves the slide toward a chosen edge of direction. Edge type objects are located and followed over multiple fields of view. If the edge object satisfies a set of predetermined criteria the coverslip edge has been found. The edge is extended to find all four corners of the coverslip. The edge objects are processed using morphological operators.

Method and apparatus for detecting a microscope slide coverslip

Reference information for a biological slide is obtained. The reference information normalizes the measured object features. Calibrated feature measurement, not based on absolute measurements, self adjusts to match the situation of each slide, where each slide is characterized by the reference information. The reference may be different from slide to slide because of the preparation variations. The calibrated features will not carry the inter-slide variations. In addition, the reference information provides a good indication of the slide condition such as dark stained, air dried, etc. which can be used as slide features for the specimen classification. No alteration of the current practice of specimen preparation is required. The many slide context dependent features improve the classification accuracy of the objects in a specimen.

Biological analysis system self calibration apparatus

Image enhancement for a digital image, such as an image of a biological specimen mounted on a microscope slide or a cytological specimen is herein described. A digital representation of the image is dilated to produce a dilated image and eroded to provide an eroded image. The dilated image, eroded image and the digital representation of the image are then processed to produce an enhanced image.

Image enhancement method and apparatus

Field of views of a slide are examined to assess the likelihood of existence of detectable single cells, groups, and thick groups of cells to locate objects of interest by an automated microscope. The FOV features consists of features selected from the distribution profiles of size, shape, layout arrangement, texture and density of all objects within a FOV which are compared against pre-determined criteria. Each field of view is assigned a likelihood value based on FOV features. Areas that are blank, or contain air bubbles, or are too dense for analysis are identified and excluded for further analysis. Each FOV is ranked according to its likelihood of containing SIL (Squamous Intraepithelial Lesion) cells or cell groups of interest. These results, such as SIL, single cell ranking, group ranking are used to arrange the further examination of FOVs in a priority order.

Field prioritization apparatus and method

A classifier measures robustness responsive to object presentation effects and decision boundary effects. A cytological image analysis computer obtains objects of interest and classifies them responsive to a decision tree classifier. The robustness of classification is calculated dynamically as objects are classified responsive to a segmentation robustness and a classification decisiveness measure. The results of the decisiveness measure and the segmentation robustness data are combined to provide enhanced overall classification reliability.

Robustness of classification measurement apparatus and method

A robust biological specimen classifier. An automated microscopy system obtains features from an image of a biological specimen slide. A computer system in the automated microscopy system computes feature variations. Clusters are created that comprise samples of similar characteristics. A cluster membership generator generates membership values for each cluster. Classifiers specialized to certain feature sets are used to provide independent outputs. These outputs are offset and biased by the output of the membership generator. The output of the adjusted classification values are summed to create a slide score output.

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Redmond, WA, United States of America

Chih-Chau L Kuan