Image analysis – Applications – Biomedical applications
Reexamination Certificate
1996-11-27
2001-04-10
Bella, Matthew (Department: 2721)
Image analysis
Applications
Biomedical applications
C382S134000, C382S274000, C382S308000
Reexamination Certificate
active
06215892
ABSTRACT:
BACKGROUND OF THE INVENTION
In the field of medical diagnostics including oncology, the detection, identification, quantitation and characterization of cells of interest, such as cancer cells, through testing of biological specimens is an important aspect of diagnosis. Typically, a biological specimen such as bone marrow, lymph nodes, peripheral blood, cerebrospinal fluid, urine, effusions, fine needle aspirates, peripheral blood scrapings or other materials are prepared by staining the specimen to identify cells of interest. One method of cell specimen preparation is to react a specimen with a specific probe which can be a monoclonal antibody, a polyclonal antiserum, or a nucleic acid which is reactive with a component of the cells of interest, such as tumor cells. The reaction may be detected using an enzymatic reaction, such as alkaline phosphatase or glucose oxidase or peroxidase to convert a soluble colorless substrate to a colored insoluble precipitate, or by directly conjugating a dye to the probe.
Examination of biological specimens in the past has been performed manually by either a lab technician or a pathologist. In the manual method, a slide prepared with a biological specimen is viewed at a low magnification under a microscope to visually locate candidate cells of interest. Those areas of the slide where cells of interest are located are then viewed at a higher magnification to confirm those objects as cells of interest, such as tumor or cancer cells. The manual method is time consuming and prone to error including missing areas of the slide.
Automated cell analysis systems have been developed to improve the speed and accuracy of the testing process. One known interactive system includes a single high power microscope objective for scanning a rack of slides, portions of which have been previously identified for assay by an operator. In that system, the operator first scans each slide at a low magnification similar to the manual method and notes the points of interest on the slide for later analysis. The operator then stores the address of the noted location and the associated function in a data file. Once the points of interest have been located and stored by the operator, the slide is then positioned in an automated analysis apparatus which acquires images of the slide at the marked points and performs an image analysis.
SUMMARY OF THE INVENTION
A problem with the foregoing automated system is the continued need for operator input to initially locate cell objects for analysis. Such continued dependence on manual input can lead to errors including cells of interest being missed. Such errors can be critical especially in assays for so-called rare events, e.g., finding one tumor cell in a cell population of one million normal cells. Additionally, manual methods can be extremely time consuming and can require a high degree of training to properly identify and/or quantify cells. This is not only true for tumor cell detection, but also for other applications ranging from neutrophil alkaline phosphatase assays, reticulocyte counting and maturation assessment, and others. The associated manual labor leads to a high cost for these procedures in addition to the potential errors that can arise from long, tedious manual examinations. A need exists, therefore, for an improved automated cell analysis system which can quickly and accurately scan large amounts of biological material on a slide. Accordingly, the present invention provides a method and apparatus for automated cell analysis which eliminates the need for operator input to locate cell objects for analysis.
In accordance with the present invention, a slide prepared with a biological specimen and reagent is placed in a slide carrier which preferably holds four slides. The slide carriers are loaded into an input hopper of the automated system. The operator may then enter data identifying the size, shape and location of a scan area on each slide, or, preferably, the system automatically locates a scan area for each slide during slide processing. The operator then activates the system for slide processing. At system activation, a slide carrier is positioned on an X-Y stage of an optical system. Any bar codes used to identify slides are then read and stored for each slide in a carrier. The entire slide is rapidly scanned at a low magnification, typically 10×. At each location of the scan, a low magnification image is acquired and processed to detect candidate objects of interest. Preferably, color, size and shape are used to identify objects of interest. The location of each candidate object of interest is stored.
At the completion of the low level scan for each slide in the carrier on the stage, the optical system is adjusted to a high magnification such as 40× or 60×, and the X-Y stage is positioned to the stored locations for the candidate objects of interest on each slide in the carrier. A high magnification image is acquired for each candidate object of interest and a series of image processing steps are performed to confirm the analysis which was performed at low magnification. A high magnification image is stored for each confirmed object of interest. These images are then available for retrieval by a pathologist or cytotechnologist to review for final diagnostic evaluation. Having stored the location of each object of interest, a mosaic comprised of the candidate objects of interest for a slide may be generated and stored. The pathologist or cytotechnologist may view the mosaic or may also directly view the slide at the location of an object of interest in the mosaic for further evaluation. The mosaic may be stored on magnetic media for future reference or may be transmitted to a remote site for review and/or storage. The entire process involved in examining a single slide takes on the order of 2-15 minutes depending on scan area size and the number of detected candidate objects of interest.
The present invention has utility in the field of oncology for the early detection of minimal residual disease (“micrometastases”). Other useful applications include prenatal diagnosis of fetal cells in maternal blood and in the field of infectious diseases to identify pathogens and viral loads, alkaline phosphatase assessments, reticulocyte counting, and others.
The processing of images acquired in the automated scanning of the present invention preferably includes the steps of transforming the image to a different color space; filtering the transformed image with a low pass filter; dynamically thresholding the pixels of the filtered image to suppress background material; performing a morphological function to remove artifacts from the thresholded image; analyzing the thresholded image to determine the presence of one or more regions of connected pixels having the same color; and categorizing every region having a size greater than a minimum size as a candidate object of interest.
According to another aspect of the invention, the scan area is automatically determined by scanning the slide; acquiring an image at each slide position; analyzing texture information of each image to detect the edges of the specimen; and storing the locations corresponding to the detected edges to define the scan area.
According to yet another aspect of the invention, automated focusing of the optical system is achieved by initially determining a focal plane from an array of points or locations in the scan area. The derived focal plane enables subsequent rapid automatic focusing in the low power scanning operation. The focal plane is determined by determining proper focal positions across an array of locations and performing an analysis such as a least squares fit of the array of focal positions to yield a focal plane across the array. Preferably, a focal position at each location is determined by incrementing the position of a Z stage for a fixed number of coarse and fine iterations. At each iteration, an image is acquired and a pixel variance or other optical parameter about a pixel mean for the acquired image is calculated to f
Douglass James W.
Riding Thomas J.
Ring James E.
Bella Matthew
ChromaVision Medical Systems, Inc.
Fish & Richardson P.C.
Patel Kanji
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