Image analysis – Applications – Biomedical applications
Reexamination Certificate
2002-01-22
2003-02-11
Johns, Andrew W. (Department: 2621)
Image analysis
Applications
Biomedical applications
Reexamination Certificate
active
06519355
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to projection imaging systems in general and cell classification, and more particularly, to high throughput flow based automated systems using projection imaging, such as flow optical tomography (FOT), for detecting abnormal and malignant cells and for detecting rare cells based on highly quantative measurements of nuclear and cytoplasmic densitometric features (NDFs and CDFs) associated with disease.
BACKGROUND OF THE INVENTION
The most common method of diagnosing cancer in patients is by obtaining a sample of the suspect tissue and examining it under a microscope for the presence of obviously malignant cells. While this process is relatively easy when the anatomic location of the suspect tissue is known, it is not so easy when there is no readily identifiable tumor or pre-cancerous lesion. For example, to detect the presence of lung cancer from a sputum sample requires one or more relatively rare cancer cells to be present in the sample. Therefore, patients having lung cancer may not be diagnosed properly if the sample does not perceptively and accurately reflect the conditions of the lung.
One example of a microscope-based system and method for detecting diagnostic cells and cells having malignancy-associated changes is disclosed in Palcic et al., U.S. Pat. No. 6,026,174. The Palcic et al. system includes an automated classifier having a conventional microscope, camera, image digitizer, a computer system for controlling and interfacing these components, a primary classifier for initial cell classification, and a secondary classifier for subsequent cell classification. The method utilizes the automated classifier to automatically detect diagnostic cells and cells having malignancy-associated changes. However, the quality of the diagnostic result is limited by the use of a conventional microscope, which does not permit accurate measurement of stain densities. The method of Palcic et al. does not address the use of molecular probes.
With the advent of molecular probes, such as antibody probes and nucleic acid hybridization probes, new disease related questions can be addressed by tagging these molecular probes and then measuring their location and concentration within biological cells and tissues. As the need to more accurately localize and quantify these probes is emerging, there is a concomitant need for improved techniques to measure probe densities microscopically in two dimensions (2D) and three dimensions (3D). Conventional light microscopy, which utilizes cells mounted on glass slides, can only approximate 2D and 3D measurements because of limitations in focal plane depth, sampling angles, and problems with cell preparations that typically cause cells to overlap in the plane of the image. Another drawback of light microscopy is the inherent limitation of viewing through an objective lens where only the area within the narrow focal plane provides accurate data for analysis.
Flow cytometry methods generally overcome the cell overlap problem by causing cells to flow one-by-one in a fluid stream. Unfortunately, flow cytometry systems do not generate images of cells of the same quality as traditional light microscopy, and, in any case, the images are not three-dimensional. For background, those skilled in the art are directed to Shapiro, H M,
Practical Flow Cytometry
, 3
rd
ed., Wiley-Liss, 1995.
In the area of computer aided tomography, U.S. Pat. No. 5,402,460, issued Mar. 28, 1995, to Johnson, et al. entitled “Three-dimensional Microtomographic Analysis System” discloses a microtomographic system for generating high-resolution, three-dimensional images of a specimen using an x-ray generator and an x-ray detector that measures the attenuation of the x-ray beam through the specimen. Two projections, each using a different energy x-ray beam, of each view of the specimen are made with Johnson, et al's microtomographic system. After the two projections of one view of the specimen are made, the specimen is rotated on the specimen holder and another set of projections is made. The projections of each view of the specimen are analyzed together to provide a quantitative indication of the phase fraction of the material comprising the specimen. The projections of the different views are combined to provide a three-dimensional image of the specimen. U.S. Pat. No. 5,402,460 is incorporated herein by reference. Although the x-ray technology as taught by U.S. Pat. No. 5,402,460 is useful for some applications, it does not provide an optical solution useful for flow cytometry, whereby one could measure the 3D distribution of molecular density within a biological cell.
To overcome the aforementioned limitations and others found in such systems, it is a motivation of this invention to combine the one-by-one cell presentation of flow cytometry with computational optical tomography from multiple point source projections to reconstruct density information within a cell from a plurality of projections. The reconstructed 3D density information enables the accurate measurement of nuclear densitometric features (NDFs) and cytoplasmic densitometric features (CDFs).
The NDFs and CDFs referred to herein are unique to projection imaging systems that do not require the use of lenses and focal planes with their inherent unwanted blur artifact due to unfocussed structures outside the narrow focal plane. Because projection imaging systems do not require lenses and focal planes, but rather, produce shadowgrams wherein all structures are in clear focus all at once, the measurement of density features will be more quantitative and accurate than in conventional microscopy systems. Therefore, the terms “NDF” and “CDF” refer to density feature measurements from shadowgrams and tomographic reconstructions using shadowgrams. NDFs and CDFs are subtle changes that are known to take place in cells associated with cancer tissue. These changes are indicative of deviation from normalcy and reflect molecular changes associated with the disease process.
However, NDFs and CDFs have not yet achieved wide acceptance for screening to determine whether a patient has or will develop cancer, because the methods of measurement have not provided adequate accuracy and/or throughput. Traditionally, NDFs and CDFs have been detected by carefully selecting a cell sample from a location near a tumor or pre-cancerous lesion and viewing the cells under a microscope using relatively high magnification. However, it is believed that the NDF and CDF changes that take place in the cells may be too subtle to be reliably detected by a human pathologist working with conventional microscopy equipment, especially because the pathologist is typically not making quantitative measurements. For example, an NDF change may be indicated by the distribution and density of DNA within the nucleus coupled with slight variations in the shape of the nucleus. Because human observers cannot easily quantify such subtle cell changes, it is difficult to determine which cells exhibit NDF alterations.
SUMMARY OF THE INVENTION
In one embodiment, the present invention provides a method for detecting cells of interest in a cell sample, comprising the steps of obtaining a cell sample and suspending the cells in solution; if required, fixing the cells of the cell sample in solution; staining and/or labeling the cells to generate optical densities associated with nuclear molecules or other structures within each cell of the sample; illuminating the sample with a “point” source of light and obtaining one or more projection images (e.g. shadowgrams) through the sample with a digital array detector; compensating the projection images for variations in background illumination; analyzing the projection images to detect objects of interest; calculating a set of one-dimensional (1D) and two-dimensional (2D) feature values for each object of interest; and providing the set of feature values to at least one classifier that identifies and characterizes cells of interest in the cell sample.
In another aspect, the present in
Johns Andrew W.
Leone George A.
Nakhjavan Shervin
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