Spectral bio-imaging methods for cell classification

• US 5,991,028 A
• Filed: 03/25/1997
• Issued: 11/23/1999
• Est. Priority Date: 02/22/1991
• Status: Expired due to Term

• Current Assignee: Applied Spectral Imaging Ltd.
• Original Assignee: Applied Spectral Imaging Ltd.
• Inventors: Cabib, Dario, Ben-Yosef, Nissim, Malik, Zvi, Buckwald, Robert A.
• Primary Examiner(s): Turner, Samuel A.
• Application Number:
  US08/824,234
• Time in Patent Office: 973 Days
• Field of Search: 356/346, 356/300, 356/319, 356/326, 250/339.07, 250/339.08, 250/339.11, 250/341.8, 435/1, 435/7.23, 382/128, 382/133
• US Class Current: 356/456
• CPC Class Codes: C12Q 1/6816 characterised by the detection means C12Q1/6804 takes precedence
• CPC Class Codes: C12Q 1/6841 In situ hybridisation
• CPC Class Codes: C12Q 1/6883 for diseases caused by alterations of genetic material
• CPC Class Codes: G01J 2003/2866 Markers; Calibrating of scan
• CPC Class Codes: G01J 3/02 Details
• CPC Class Codes: G01J 3/12 Generating the spectrum; Monochromators
• CPC Class Codes: G01J 3/1256 using acousto-optic tunable filter; acousto-optic elements or systems G02F1/11, G02F1/33
• CPC Class Codes: G01J 3/26 using multiple reflection, e.g. Fabry-Perot interferometer, variable interference filters
• CPC Class Codes: G01J 3/2823 Imaging spectrometer
• CPC Class Codes: G01J 3/4406 Fluorescence spectrometry
• CPC Class Codes: G01J 3/453 by correlation of the amplitudes
• CPC Class Codes: G01J 5/10 using electric radiation detectors
• CPC Class Codes: G01N 2021/6417 Spectrofluorimetric devices
• CPC Class Codes: G01N 21/6428 Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes" in vivo A61B5/00; immunoassay G01N33/53
• CPC Class Codes: G01N 21/6458 Fluorescence microscopy fluorescence microscopes per se G02B21/0076 and G02B21/16
• CPC Class Codes: G01N 33/5005 involving human or animal cells immunoassay G01N33/56966; immunoassays of protozoa G01N33/56905; protozoa in screening assays C12Q1/025
• CPC Class Codes: G01N 33/56966 Animal cells
• CPC Class Codes: G01N 33/582 with fluorescent label
• CPC Class Codes: G06V 10/88 Image or video recognition using optical means, e.g. reference filters, holographic masks, frequency domain filters or spatial domain filters
• CPC Class Codes: G06V 20/69 Microscopic objects, e.g. biological cells or cellular parts

Abstract

According to the present invention there is provided a spectral bio-imaging methods which can be used for automatic and/or semiautomatic spectrally resolved morphometric classification of cells, the method comprising the steps of (a) preparing a sample to be spectrally imaged, the sample including at least a portion of at least one cell; (b) viewing the sample through an optical device, the optical device being optically connected to an imaging spectrometer, the optical device and the imaging spectrometer being for obtaining a spectrum of each pixel of the sample; (c) classifying each of the pixels into classification groups according to the pixels spectra; and (d) analyzing the classification groups and thereby classifying the at least one cell into a cell class.

Claims

1. A spectral bio-imaging method for cells classification into classes, the method comprising the steps of:

(a) preparing a sample to be spectrally imaged, said sample including at least a portion of at least one cell;

(b) viewing said sample through an optical device, said optical device being optically connected to an imaging spectrometer, said optical device and said imaging spectrometer being for obtaining a spectrum of each pixel of said sample;

(c) classifying each of said pixels into classification groups according to said pixels spectra; and

(d) analyzing said classification groups and thereby classifying said at least one cell into a cell class.

2. A method as in claim 1, wherein said preparation of step (a) involves staining said cell via a staining method selected from the group consisting of Haematoxylin-Eosin staining, Giemsa staining, mason tricolor staining and papanicolaou staining.

3. A method as in claim 1, wherein said obtainment of said spectrum of each pixel of step (b) is effected by:

(i) collecting incident light simultaneously from all pixels of said sample using collimating optics;

(ii) passing said incident collimated light through an interferometer system having a number of elements, so that said light is first split into two coherent beams which travel in different directions inside said interferometer and then said two coherent beams recombine to interfere with each other to form an exiting light beam;

(iii) passing said exiting light beam through a focusing optical system which focuses said exiting light beam on a detector having a two-dimensional array of detector elements, so that at each instant each of said detector elements is the image of one and always the same pixel of said sample for the entire duration of the measurement, so that the real image of the sample is stationary on the plane of the detector array and at any time during the measurement the image is still visible and recognizable, and so that each of said detector elements produces a signal which is a particular linear combination of light intensity emitted by said pixel at different wavelengths, wherein said linear combination is a function of the instantaneous optical path difference;

(iv) scanning one or more of said elements of said interferometer system, so that said optical path difference between said two coherent beams generated by said interferometer system is scanned simultaneously for all said pixels of said sample; and

(v) recording signals of each of said detector elements as function of time using a recording device to form a spectral cube of data.

4. A method as in claim 1, wherein said optical device is a microscope.

5. A method as in claim 1, wherein said imaging spectrometer includes an element selected from the group consisting of a dispersion element, a filter and an interferometer.

6. A method as in claim 1, wherein pixels classified to a classification group via said classification of step (c) are presented in an image by a preselected artificial color.

7. A method as in claim 1, wherein pixels classified to a classification group via said classification of step (c) are presented as an abundance histogram.

8. A method as in claim 1, wherein said abundance histogram is relative.

9. A method as in claim 7, wherein said abundance histogram serves for said classification of said at least one cell into said cell class of step (d).

10. A method as in claim 1, wherein said classification of each of said pixels into said classification groups according to said pixels spectra of step (c) is effected using a classification map algorithm which employs reference spectra for associating pixels into said classification groups.

11. A method as in claim 10, wherein said reference spectra for classification are of a previously prepared reference library.

12. A method as in claim 10, wherein at least one of said reference spectra for classification is of pixels derived from a cell domain selected from the group consisting of nucleolus, inter-chromosomal region, cytoplasm, a first chromatin region of the nucleus, a second chromatin region of the nucleus and background.

13. A method as in claim 10, wherein pixels classified to a classification group via said classification map algorithm are presented in an image by a preselected artificial color.

14. A method as in claim 10, wherein pixels classified to a classification group via said classification map algorithm are presented as an abundance histogram.

15. A method as in claim 14, wherein said abundance histogram serves for said classification of said at least one cell into said cell class of step (d).

16. A method as in claim 15, wherein said abundance histogram serves for said classification of said at least one cell into said cell class of step (d) via a trained neural network algorithm which associates abundance histogram s with cell classes.

17. A method as in claim 1, wherein said classification of each of said pixels into said classification groups according to said pixels spectra of step (c) is effected using a first trained neural network algorithm which associates a pixel into a classification group according to said pixel'"'"'s spectrum.

18. A method as in claim 17, wherein pixels classified to a classification group via said first trained neural network algorithm are presented in an image by a preselected artificial color.

19. A method as in claim 17, wherein pixels classified to a classification group via said first trained neural network algorithm are presented as an abundance histogram.

20. A method as in claim 19, wherein said abundance histogram serves for said classification of said at least one cell into said cell class of step (d) via a second trained neural network algorithm which associates abundance histogram s with cell classes.

21. A method as in claim 1, wherein said cell is a cancer cell.

22. A method as in claim 21, wherein said cancer is selected from the group consisting of carcinoma, sarcoma, leukemia and lymphoma.