Claims
- 1. A method for automatically analyzing a biological sample with a microscope, comprising the steps of:
obtaining a first digital image of a first field of view, in the microscope, of the biological sample; automatically determining cell data that indicates an area co-located in the first digital image with a cell set of one or more cells of a particular type; automatically determining anomalous data that indicates an area co-located in the first digital image with an anomalous set of zero or more particular objects that are anomalous to normal cells of the particular type; automatically combining the cell data and the anomalous data to determine the particular objects inside the cell set of one or more cells of the particular type in the first digital image; and generating an analytical result for the biological sample based on the particular objects inside the cell.
- 2. The method as recited in claim 1, said step of obtaining the first digital image further comprising the step of archiving the first digital image in a database that can be accessed by at least one of a local user and a remote user.
- 3. The method as recited in claim 1, said step of generating the analytical result further comprising the step of archiving the analytical result in association with the first digital image in a database that can be accessed by at least one or a local user and a remote user.
- 4. The method as recited in claim 1, said step of obtaining the first digital image further comprising obtaining the first digital image of a plurality of digital images corresponding to a plurality of fields of view of the microscope by performing the steps of:
automatically controlling a horizontal position of a platform holding the biological sample relative to the microscope to achieve a current horizontal position; automatically controlling a depth of focus of the microscope to achieve a current depth of focus; and automatically scanning a scan set of one or more picture elements of the first digital image for the current horizontal position and the current depth of focus.
- 5. The method as recited in claim 4, said step of automatically controlling the depth of focus further comprising the steps of:
holding the depth of focus constant for the first digital image; and changing the depth of focus for a different, second digital image of the plurality of digital images by a depth increment based on a size associated with the particular objects anomalous to cells of the particular type.
- 6. The method as recited in claim 4,wherein;
said step of obtaining the first digital image comprises obtaining the plurality of digital images; said step of automatically determining cell data in the first digital image comprises automatically determining cell data in each digital image of the plurality of digital images; said step of automatically determining anomalous data in the first digital image comprises automatically determining anomalous data in each digital image of the plurality of digital images; said step of automatically combining the cell data and the anomalous data to determine the particular objects inside the cell in the first digital image comprises automatically combining the cell data and the anomalous data in each digital image of the plurality of digital images to determine the particular objects inside the cell in each digital image of the plurality of digital images; and said step of generating the analytical result further comprises determining a statistic for the biological sample based on a number of the particular objects inside the cell in every digital image of the plurality of digital images.
- 7. The method as recited in claim 6, wherein:
said step of obtaining the first digital image further comprises determining a target accuracy for quantifying the statistic for the sample; and said step of automatically controlling the horizontal position further comprising the step of controlling the horizontal position to place in the plurality of digital images a percentage less than 100% of an area of the biological sample on the platform; and the percentage is based on the target accuracy.
- 8. The method as recited in claim 1, said step of automatically combining the cell data and the anomalous data further comprising determining a number of the particular objects inside the cell set.
- 9. The method as recited in claim 8, said step of automatically determining cell data further comprising determining a number of cells in the cell set.
- 10. The method as recited in claim 9, said step of automatically combining the cell data and the anomalous data further comprising determining a level per cell based on the number of the particular objects inside the cell set and the number of cells in the cell set.
- 11. The method as recited in claim 10, said step of generating the analytical result further comprising determining a level per unit volume based on a normal number of the cells per unit volume.
- 12. The method as recited in claim 1, said step of generating the analytical result further comprising determining a stage of a disease based at least in part on the anomalous data associated with the particular objects inside the cell set.
- 13. The method as recited in claim 12, said step of generating the analytical result further comprising determining a number the particular objects inside the cell set associated with the stage of the disease.
- 14. The method as recited in claim 12, said step of determining the stage of the disease further comprising determining a shape of the particular objects inside the cell set.
- 15. The method as recited in claim 14, wherein:
said step of obtaining the first digital image further comprises obtaining a second digital image of a second field of view of the biological sample at a different depth of focus of the microscope; said step of automatically determining anomalous data in the first digital image further comprises automatically determining anomalous data in the second digital image; and said step of determining the shape of the particular objects inside the cell set further comprising determining the shape of a first particular object inside the cell set by performing the steps of
determining a location of the first particular object in the first digital image based at least in part on the anomalous data in the first digital image, automatically determining second anomalous data in the anomalous data of the second digital image that is in a horizontal vicinity of the location of the first particular object in the first digital image, and determining the shape of the first particular object based at least in part on the second anomalous data.
- 16. The method as recited in claim 1, said step of generating the analytical result further comprising determining a strain of a disease based at least in part on the anomalous data associated with the particular objects inside the cell set.
- 17. The method as recited in claim 1, said step of determining the cell data further comprising determining pixels above a threshold value in a color plane of the digital image.
- 18. The method as recited in claim 1, said step of determining the cell data further comprising determining pixels above a threshold value in a weighted sum of at least two color planes of the digital image.
- 19. The method as recited in claim 1, said step of determining the anomalous data further comprising determining pixels above a threshold value in a color plane of the digital image.
- 20. The method as recited in claim 1, said step of determining the anomalous data further comprising determining pixels above a threshold value in a weighted sum of at least two color planes of the digital image.
- 21. The method as recited in claim 17, said step of determining the anomalous data further comprising determining pixels above a different threshold value in a color plane of the digital image.
- 22. The method as recited in claim 18, said step of determining the anomalous data further comprising determining pixels above a different threshold value in the weighted sum of at least two color planes of the digital image.
- 23. The method as recited in claim 18, said step of determining the anomalous data further comprising determining pixels above a different threshold value in a different weighted sum of at least two color planes of the digital image.
- 24. The method as recited in claim 1, said step of determining the cell data further comprising determining edges in the digital image.
- 25. The method as recited in claim 1, said step of determining the anomalous data further comprising determining edges in the digital image.
- 26. The method as recited in claim 1, wherein:
the method further comprising determining a calibration factor for producing an analytical result that agrees with a standard; and said step of generating the analytical result further comprises generating the analytical result based at least in part on the calibration factor.
- 27. The method as recited in claim 26, wherein:
said step of determining a calibration factor further comprising determining a calibration factor that depends on a parameter that can be derived from a digital image of a biological sample; and said step of generating the analytical result further comprises determining the parameter based on the first digital image, and generating the calibration factor based on the parameter.
- 28. The method as recited in claim 1, wherein the particular objects are parasites.
- 29. The method as recited in claim 1, wherein the particular type of cells is a red blood cell.
- 30. The method as recited in claim 28, wherein the parasites are Malaria parasites.
- 31. The method as recited in claim 1, wherein the biological sample is a blood smear and the analytical result includes an indication of one of a presence of Malaria parasites and an absence of Malaria parasites.
- 32. The method as recited in claim 1, wherein the biological sample is a blood smear and the analytical result includes a parasitemia level for Malaria.
- 33. The method as recited in claim 1, wherein the biological sample is a blood smear and the analytical result includes an identification of Malaria parasite stage.
- 34. The method as recited in claim 1, wherein the biological sample is a blood smear and the analytical result includes an identification of Malaria parasite strain.
- 35. The method as recited in claim 1, wherein the particular objects are anomalously-shaped cell structures.
- 36. The method as recited in claim 35, wherein the analytical result includes an indication of one of a presence of babesiosis and an absence of babesiosis.
- 37. A computer-readable medium carrying one or more sequences of instructions for automatically analyzing a biological sample with a microscope, wherein execution of the one or more sequences of instructions by one or more processors causes the one or more processors to perform the steps of:
receiving a first digital image of a first field of view, in the microscope, of the biological sample; determining cell data that indicates an area co-located in the first digital image with a cell set of one or more cells of a particular type; determining anomalous data that indicates an area co-located in the first digital image with a anomalous set of zero or more particular objects that are anomalous to normal cells of the particular type; combining the cell data and the anomalous data to determine the particular objects inside the cell set of one or more cells of the particular type in the first digital image; and generating an analytical result for the biological sample based on the particular objects inside the cell.
- 38. A system for automatically analyzing a biological sample with a microscope, comprising:
a digital microscope for generating a first digital image of a first field of view of the biological sample; a processor connected to the digital microscope; and a computer readable medium storing one or more sequences of instructions which, when executed by the processor, cause the processor to carry out the steps of:
receiving the first digital image; determining cell data that indicates an area co-located in the first digital image with a cell set of one or more cells of a particular type; determining anomalous data that indicates an area co-located in the first digital image with a anomalous set of zero or more particular objects that are anomalous to normal cells of the particular type; combining the cell data and the anomalous data to determine the particular objects inside the cell set of one or more cells of the particular type in the first digital image; and generating an analytical result for the biological sample based on the particular objects inside the cell.
- 39. The system as recited in claim 38, wherein the analytical result is used to assist in a clinical diagnosis of a medical condition of a donor of the biological sample.
- 40. The system as recited in claim 38, wherein the analytical result provides a clinical diagnosis of a medical condition of a donor of the biological sample.
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims benefit of Provisional Appln. 60/384,323, filed May 30, 2002, the entire contents of which are hereby incorporated by reference as if fully set forth herein, under 35 U.S.C. §119(e).
PCT Information
Filing Document |
Filing Date |
Country |
Kind |
PCT/US03/16692 |
5/29/2003 |
WO |
|