Automated detection and repositioning of micro-objects in microfluidic devices

1. A method for determining a number density of micro-objects disposed within a predetermined area of a microfluidic device, the method comprising:

capturing a digital image of a region in the microfluidic device that contains a micro-object of interest;

determining periodic structures in the digital image using a Fourier transform;

generating a filtered image or a differential image by removing the periodic structures from the digital image;

identifying a micro-object of interest based on the filtered or differential image; and

calculating the number density of micro-objects in the pre-determined area of that region of the microfluidic device using the filtered or differential image.

2. The method of claim 1 , wherein the periodic structures correspond to one or more microfluidic device features.

3. The method of claim 2 , wherein the one or more microfluidic device features include an array of phototransistors and/or walls of sequestration pens.

4. The method of claim 1 , further comprising:

determining a set of light intensity values for one or more pixels corresponding to the filtered image; and

generating a set of positive-value pixels based on the filtered image.

5. The method of claim 4 , further comprising:

analyzing the set of positive-value pixels to identify one or more sets of pixel clusters, wherein each pixel cluster comprises one or more pixels;

determining, for each of the one or more sets of pixel clusters, a feature set comprising information representing one or more of an area of the set of pixel clusters, a circumference of the set of pixel clusters, a global morphology of the set of pixel clusters, a local morphology of the set of pixel clusters and a light intensity value associated with the set of pixel clusters; and

identifying, for each of the one or more sets of pixel clusters, whether the set of pixel clusters corresponds to the micro-object of interest, wherein the identification is based on the feature set determined for the set of pixel clusters.

6. The method of claim 1 , wherein a filtered image is generated, and said filtered image is generated prior to identifying a micro-object of interest.

7. The method of claim 1 , wherein a differential image is generated, and said differential image is generated using a first image and a second image.

8. The method of claim 7 , wherein the first image is taken without removing any features of the microfluidic device.

9. The method of claim 7 , wherein the second image is taken after inducing movement of fluid within the microfluidic device.

10. The method of claim 8 , wherein the second image is taken after the position of the microfluidic device is shifted relative to the imaging device.

11. The method of claim 7 , wherein the differential image is generated by subtracting the first image from the second image.

12. The method of claim 11 , wherein the first and second images are aligned computationally prior to subtracting the first image from the second image, and wherein pixels identified as positive-value pixels represent the current location of a micro-object.

13. The method of claim 7 , wherein the differential image is generated by subtracting the second image from the first image, and wherein pixels identified as positive-value pixels represent the former location of a micro-object and pixels identified as negative-value pixels represent the current location of the micro-object.

14. The method of claim 1 , wherein a light intensity value Li is defined for each pixel Pi (i=1 to n), wherein n is the number of pixels in the image.

15. The method of claim 14 , further comprising analyzing the filtered or differential image wherein analyzing comprises identifying each pixel Pi that has a negative light intensity value Li as being a positive-value pixel and identifying each pixel Pi that has a negative light intensity value Li as a negative value pixel.

16. The method of claim 15 , wherein analyzing further comprises comparing the light intensity value Li of each pixel Pi to a predetermined threshold light intensity value Lo; and identifying each pixel Pi having an Li greater than Lo as a positive-value pixel and each pixel Pi having an Li less than −1*Lo as a negative value pixel.

17. The method of claim 16 , wherein the threshold light intensity value Lo is the average light intensity value Lavg of the set of light intensity values Li obtained from the set of pixels Pi (i=1 to n).

18. The method of claim 16 , wherein the generated image is a differential image, and the differential image is analyzed for pairs of positive-value and negative-value pixels or clusters of pixels.

19. The method of claim 1 , wherein the Fourier Transform is a Discrete Fourier Transform, and one or more pixels corresponding to the frequency domain are filtered out of the filtered or differential image.

20. The method of claim 19 , wherein an inverse Discrete Fourier Transform is applied to produce the filtered or differential image.

21. The method of claim 1 , wherein the pre-determined area of the microfluidic circuit comprises a channel, a sequestration pen, or a trap of the microfluidic device.

22. The method of claim 1 , wherein identifying the micro-object of interest further comprises detecting production of an analyte.

23. The method of claim 22 , wherein the micro-object of interest is a biological micro-object that produces the analyte.

24. The method of claim 23 , wherein the image of the region in the microfluidic device comprising the biological micro-object of interest further comprises one or more micro-objects each comprising a bead configured to produce a localized reaction with the analyte.

25. The method of claim 24 , further comprising correlating the localized reaction with a location of the biological micro-object of interest within the region, thereby identifying the biological micro-object of interest.