Patent Issued for Methods and Systems for Processing Biological Specimens Utilizing Multiple Wavelengths

By a News Reporter-Staff News Editor at Journal of Engineering -- Cytyc Corporation (Marlborough, MA) has been issued patent number 8326014, according to news reporting originating out of Alexandria, Virginia, by VerticalNews editors.

The patent's inventors are Wong, Kam Lin (Bedford, MA); Zahniser, David (Wellesley, MA); Mui, Kathy (Chelmsford, MA); Linder, James (Omaha, NE); Kaufman, Howard (Newton, MA).

This patent was filed on September 28, 2007 and was cleared and issued on December 4, 2012.

From the background information supplied by the inventors, news correspondents obtained the following quote: "In the medical industry, there is often a need for a laboratory technician, e.g., a cytotechnologist, to review a cytological specimen for the presence of specified cell types. For example, there is presently a need to review a cervical-vaginal Papanicolaou (Pap) smear slides. Pap smears have been a powerful tool for detecting cancerous and precancerous cervical lesions. The reliability and efficacy of a cervical screening and screening of other specimens is measured by its ability to diagnose precancerous lesions (sensitivity) while at the same time avoiding false positive diagnosis (specificity). In turn, these criteria depend on the accuracy of the cytological interpretation.

"Traditionally, a pathologist may perform a single cell analysis on a biological specimen by looking at the characteristics of individual cell nuclei, or a contextual analysis on the biological specimen by looking for characteristic patterns in the architecture of the cells as they appear on the slide. To facilitate this review process, automated screening systems have been developed to process multiple microscope slides. In a typical system, an imager is operated to provide a series of images of a cytological specimen slide, each depicting a different portion of the slide. A processor or controller then processes the image data to furnish quantitative and prognostic information about the specimen. The processor can perform either a single cell analysis or a contextual analysis, or both, in providing this diagnostic information.

"In some automated screening systems, the processor uses the diagnostic information to delineate between normal and abnormal or suspicious biological material within each specimen. That is, the processor will focus the cytotechnologist's attention on the most pertinent cells, with the potential to discard the remaining cells from further review. In this case, the screening device uses the diagnostic information to determine the most pertinent biological objects and their locations on the slide. This location information is provided to a review microscope, which automatically proceeds to the identified locations and centers on the biological objects for review by the cytotechnologist. The cytotechnologist can then electronically mark the most pertinent biological objects (for example, objects having attributes consistent with malignant or pre-malignant cells) for further review by a pathologist.

"For example, in one automated system, objects or 'objects of interest' (OOIs) are identified based on the image data. Objects or OOIs may take the form of individual cells and cell clusters of the specimen. The system may be configured to rank identified areas or objects, e.g., based on the degree to which certain cells or objects are at risk of having an abnormal condition such as malignancy or pre-malignancy. For example, a processor may evaluate objects for their nuclear integrated or average optical density, and rank the objects in accordance with their optical density values. The objects, along with their relative ranking and coordinates, may be stored for subsequent processing, review or analysis. Further aspects of a known imaging system and methods of processing image data and OOIs are described in U.S. Publication No. 2004/0254738 A1, the contents of which are incorporated herein by reference.

"In general, the use of automated screening systems has been effective, since the technician's attention is focused on those slides that are suspicious or on a limited number of more pertinent objects within each slide. Automated screening systems, however, can be improved. For example, the manner in which automated systems process artifacts can be improved in order to reduce the rate of false positive or 'false abnormal' results. An artifact may be considered to be an object which has no diagnostic value. One cause of false positives is the presence of artifacts, which may be abundant in a specimen sample and be in the form of large dark objects that mimic abnormal specimens. Artifacts may outrank objects containing normal cells.

"For example, compared to an abnormal nucleus, a normal nucleus usually has less DNA amount and less texture. Without the presence of artifacts in the top ranked objects, the majority of the cells in a normal slide have tightly distributed DNA amounts. However, a large number of artifacts that mimic abnormal cells outrank the majority of the normal cells, and these artifacts create false alarms in data modeling. These artifacts may prevent true cells from being ranked and properly presented in the list of cells with the 'top' DNA amounts. Thus, rather than selecting cells that should be reviewed, automated systems may instead mistakenly believe that an artifact is an abnormal cell and select artifacts that outrank an abnormal nucleus. This results in a selection of a smaller number of objects that actually have cells and selection of a smaller number of abnormal objects that warrant review by a cytotechnologist, thereby potentially resulting in less accurate and inaccurate analyses and diagnosis.

"The occurrence of false positives sometimes results from the limited capabilities or configuration of an automated imager. That is, automated imagers may be limited by the specimen and data provided to them and by their programming. For example, for computational reasons, imagers typically use monochromatic, black and white images for their analyses. Examples of known monochromatic systems are available from Becton Dickinson Company, 1 Becton Drive, Franklin Lakes, N.J. and Cytyc Corporation, 250 Campus Drive, Marlborough, Mass. A specimen, however, may provide a great range of spectral data and other information that can be used to characterize or classify the sample. However, this other data is not available when using a monochromatic imaging and analysis system."

Supplementing the background information on this patent, VerticalNews reporters also obtained the inventors' summary information for this patent: "One embodiment is directed to a method for classifying a biological specimen on a specimen carrier to determine whether the specimen requires further analysis. The method includes acquiring images of objects in the specimen and identifying objects of interest in the images. The method also includes acquiring additional images of the identified objects of interest at a plurality of different wavelengths, extracting cellular features of the identified objects of interest from the additional images and classifying the specimen according to a probabilistic model based on the extracted cellular features to determine whether the specimen requires further analysis.

"Another embodiment is directed to a method for automatically classifying a biological specimen carried on a specimen carrier to determine whether the specimen requires further analysis. The method includes acquiring images of objects in the specimen and identifying objects of interest from the acquired images. The method also includes acquiring additional images of the identified objects of interest at a plurality of different wavelengths, extracting nucleus-related features of the identified objects of interest from the additional images and classifying the specimen according to a probabilistic model based on the extracted nucleus-related features. The probabilistic model includes first and second probability functions. The first probability function indicates a probability that an identified object of interest is an artifact, and the second probability function is based in part on a result of the first probability function. A combination of the first and second probability functions is used to classify the specimen and to determine whether the specimen requires further analysis.

"A further embodiment is directed to a method of processing biological specimens utilizing light at multiple wavelengths and includes acquiring images of objects in the biological specimens and identifying objects of interest in the acquired images. The method also includes acquiring additional images of the identified objects of interest at a plurality of different wavelengths and extracting cellular features of the identified objects of interest from the additional images.

"Yet another embodiment is directed to a method of classifying biological specimens utilizing light at multiple wavelengths and includes acquiring images of objects in the biological specimens and identifying objects of interest in the acquired images. The method also includes acquiring additional images of objects of interest of the biological specimen at a plurality of different wavelengths, extracting cellular features of the objects of interest from acquired images and classifying the biological specimen based on the extracted cellular features.

"An additional embodiment is directed to a method of classifying a biological specimen utilizing light at multiple wavelengths and includes acquiring images of objects of interest of the biological specimen at a plurality of different wavelengths, extracting cellular features of the objects of interest from acquired images and classifying the biological specimen based on the extracted cellular features.

"According to another embodiment, a biological screening system for classifying a biological specimen carried on a specimen carrier to determine whether the biological specimen requires further analysis includes an imaging component and a processor that is operably coupled to the imaging component. The imaging component is configured to acquire digital image data of objects in the biological specimen, and the processor is configured to process and identify objects of interest from the digital image data. The imaging component is also configured to acquire additional images of the identified objects of interest at a plurality of different wavelengths. The processor is further configured to extract cellular features of the identified objects of interest from the additional images, and to classify the biological specimen according to a probabilistic model based on extracted cellular features carriers to determine whether the biological specimen requires further analysis.

"In accordance with a further embodiment, a biological screening system for classifying biological specimens carried on specimen carriers to determine whether a biological specimen requires further analysis includes an imaging component and a processor operably coupled to the imaging component. The imaging component is configured to acquire images of objects in the biological specimen, and the processor is configured to process and identify objects of interest from the acquired images. The imaging component is further configured to obtain additional images of the identified objects of interest at a plurality of different wavelengths. The processor is further configured to extract nucleus-related features of identified objects of interest from the additional images acquired at different wavelengths, and to classify the biological specimen according to a probabilistic model that is based on measured cellular features. The probabilistic model includes first and second probability functions. The first probability function indicates a probability that a selected object is an artifact, and the second probability function is based in part on a result of the first probability function. The combination of the first and second probability functions is used to classify the biological specimen and determine whether the biological specimen requires further analysis.

"A further alternative embodiment is directed to a biological specimen classification system that includes an imaging component and a processor operably coupled to the imaging component. The imaging component is configured to acquire images of objects of interest of a biological specimen at a plurality of different wavelengths, and the processor configured to extract cellular-related features from the acquired images and classify the biological specimen based on the extracted cellular-related features.

"In one or more embodiments, cellular features that are extracted or measured are nucleus-related features, e.g., a standard deviation of an optical density within the nucleus, a variation of an optical density within the nucleus, a corrected optical density of the nucleus, and a shape of a boundary of the nucleus.

"In one or more embodiments, the probabilistic model used for classification includes two probability functions, e.g., posterior probability functions. One probability function indicates an average probability that an identified object of interest of a biological specimen is an artifact, and the other probability function is based in part on a result of the first probability function. Both probability functions may be based on different numbers and types of extracted nucleus-related features. For example, the first probability function may be based on one or more or all of a texture of a nucleus, a standard deviation of an optical density within the nucleus, a variation of an optical density within the nucleus, a corrected optical density of the nucleus, and a shape of a boundary of the nucleus, and the second probability function may be based on the result of the first probability function and one or more of an average of gray value contrast of pixels of images of nuclei of cells of identified objects of interest, and a range of gray value contrast of pixels of images of nuclei of cells of identified objects of interest. The results of the first and second probability functions can be plotted or represented in a graphical format to classify biological specimens to determine whether a specimen requires further review or which specimens of a group of specimens require further review."

For the URL and additional information on this patent, see: Wong, Kam Lin; Zahniser, David; Mui, Kathy; Linder, James; Kaufman, Howard. Methods and Systems for Processing Biological Specimens Utilizing Multiple Wavelengths. U.S. Patent Number 8326014, filed September 28, 2007, and issued December 4, 2012. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=38&u=%2Fnetahtml%2FPTO%2Fsearch-bool.html&r=1868&f=G&l=50&co1=AND&d=PTXT&s1=20121204.PD.&OS=ISD/20121204&RS=ISD/20121204

Keywords for this news article include: Cytyc Corporation.

Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2012, NewsRx LLC