Methods and systems for imaging cells

• US 7,920,736 B2
• Filed: 04/19/2010
• Issued: 04/05/2011
• Est. Priority Date: 05/13/2004
• Status: Active Grant

• Current Assignee: Charles Stark Draper Laboratory Incorporated, University of Pittsburgh of The Commonwealth System of Higher Education
• Original Assignee: Charles Stark Draper Laboratory Incorporated, University of Pittsburgh of The Commonwealth System of Higher Education
• Inventors: Jeffreys, Christopher, Desai, Mukund, Mangoubi, Rami, Sammak, Paul J.
• Primary Examiner(s): Azarian; Seyed
• Application Number:
  US12/762,547
• Publication Number:
  US 20100260406A1
• Time in Patent Office: 351 Days
• Field of Search: 382/100, 382/106, 382/107, 382/128, 382/129, 382/130, 382/131, 382/132, 382/133, 382/168, 382/181, 382/191, 382/203, 382/232, 382/255, 382/260, 382/274, 382/276, 382/291, 382317-321, 382/224, 703/2, 250/208.1, 600/544, 119/421, 424/278.1, 424/93.2, 702/19
• US Class Current: 382/133
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/30024 Cell structures in vitro; Tissue sections in vitro
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/41 based on statistical description of texture
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 20/69 Microscopic objects, e.g. biological cells or cellular parts

Abstract

The invention generally provides methods and systems for determining characteristics of cellular structures. The methods include non-invasive, non-perturbing, automatable, and quantitative methods and may be applied to the examination of cells such as stem cells, embryos, and egg cells.

Claims

1. A non-invasive system for determining one or more characteristics of a cell, comprising:

a) a first computer processor for deriving a plurality of numerical image features from an image of said cell at the pixel level and at the cellular structure level using a tiered hierarchical cellular structure paradigm stored in memory, and

b) a second computer processor for classifying a cellular structure according to a statistical classification model based on image features derived at the pixel level and at the cellular structure level.

2. The system of claim 1, wherein the first computer processor and the second computer processor are the same processor.

3. The system of claim 1, wherein the first processor has at least one analysis module for deriving image features from an image of a stem cell and at least one module for determining stemness or plasticity of the stem cell based on the image features.

4. The system of claim 1, wherein at least one of the first and second computer processors is configured to determine characteristics of a cell comprising one or more of:

the viability of the cell, the stemness or plasticity of the cell, the suitability for implantation of the cell and the suitability for fertilization of the cell.

5. The system of claim 1, wherein at least one of the characteristics the processor is configured to determine comprises suitability for fertilization of the cell.

6. The system of claim 1, wherein at least one of the first and second computer processors is configured to determine characteristics of at least one eukaryotic cell.

7. The system of claim 1, wherein at least one of the first and second computer processors is configured to determine characteristics of at least one of an egg cell, an embryo, a stem cell, a tumor cell, a differentiated cell, a colon cell, a breast cell, a lung cell, a prostate cell, a pancreatic cell, a kidney cell, an endometrial cell, a cervical cell, an ovarian cell, and a thyroid cell.

8. The system of claim 1, wherein the second processor has at least one analysis module for classifying a cellular structure selected from at least one of a cell colony, a nucleus, cell membrane, cell wall, mitochondria, golgi, chromatin, DNA, cytoskeleton, ribosome, and endoplasmic reticulum.

9. The system of claim 1, wherein the first processor has at least one analysis module for deriving numerical image features from at least one physiological feature of the imaged cells.

10. The system of claim 9, wherein said at least one physiological feature includes at least one of a cellular border, a colony border, a cell membrane, cell texture, and colony texture.

11. The system of claim 1, wherein the first processor has capacity to derive the plurality of image features from images of cells obtained at different times during one or more time intervals.

12. The system of claim 1, wherein the statistical classification model analyzes at least one change in the image features over the one or more time intervals and identifies at least one change in the characteristics of the cell over an interval of time.

13. The system of claim 1, wherein the statistical classification model takes into account external cellular structure data and data from the derived image features.

14. The system of claim 1, wherein at least one of the first and second computer processors is configured to derive said image features by at least one of wavelet decomposition, multiresolution analysis, time-space frequency analysis, dilate and erode, co-occurrence matrices, Fourier spectral analysis, Gabor methods, wavelet packet decomposition, statistical analysis of wavelet coefficients, Markov Random fields, autoregression models, spatial domain filters, Fourier and Wavelet domain filters, and texture segmentation.

15. The system of claim 1, wherein at least one of the first and second computer processors is configured to derive said image features in a multi-tiered hierarchical cellular structure paradigm by:

a) deriving a plurality of image features at the pixel level in tier 1;

b) deriving a plurality of image features in increasing image resolutions in progressive tiers; and

c) deriving a plurality of image features at the cellular structure level in the final tier.

16. The system of claim 1, wherein at least one of the first and second computer processors is configured to derive said image features in a two-tiered hierarchical cellular structure paradigm, wherein a plurality of image features is derived at the pixel level in the first tier, and wherein a plurality of image features is derived at the cellular structure level in the second tier.

17. The system of claim 1, wherein the statistical classification model is based on a non-parametric class algorithm.

18. The system of claim 1, wherein the statistical classification model is based on a parametric class algorithm.

19. The system of claim 1, wherein said statistical classification model is based on the application of at least one of a Bayes classification scheme, discriminant analysis, Parzen window analysis, multiple hypothesis testing, neural network analysis, k-nearest neighbor calculation, decision tree processing, hierarchical clustering, fuzzy method analysis, component analysis, support vector machines analysis, and Kullback Leibler Distances analysis.

20. A computer readable medium storing computer executable instructions, which, when executed by a processor, cause the processor to carry out a method of determining one or more characteristics of a cell, the method comprising:

a) deriving a plurality of numerical image features from an image of said cell at the pixel level using a tiered hierarchical cellular structure paradigm stored in memory, and at the cellular structure level, and

b) classifying a cellular structure according to a statistical classification model based on image features derived at the pixel level and at the cellular structure level.