Robotic Microtool Control in an Intelligent Automated In Vitro Fertilization and Intracytoplasmic Sperm Injection Platform
US-2024426856-A1 · Dec 26, 2024 · US
US9495743B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-9495743-B2 |
| Application number | US-201414450587-A |
| Country | US |
| Kind code | B2 |
| Filing date | Aug 4, 2014 |
| Priority date | Jan 22, 2010 |
| Publication date | Nov 15, 2016 |
| Grant date | Nov 15, 2016 |
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Systems and methods are described for cataract intervention. In one embodiment a system comprises a laser source configured to produce a treatment beam comprising a plurality of laser pulses; an integrated optical system comprising an imaging assembly operatively coupled to a treatment laser delivery assembly such that they share at least one common optical element, the integrated optical system being configured to acquire image information pertinent to one or more targeted tissue structures and direct the treatment beam in a 3-dimensional pattern to cause breakdown in at least one of the targeted tissue structures; and a controller operatively coupled to the laser source and integrated optical system, and configured to adjust the laser beam and treatment pattern based upon the image information, and distinguish two or more anatomical structures of the eye based at least in part upon a robust least squares fit analysis of the image information.
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What is claimed is: 1. A method for locating a boundary of at least one anatomical structure of an eye of a patient comprising: providing image information pertinent to at least one anatomical structure of the eye; processing the image information to identify a first set of candidate edge locations for the at least one anatomical structure of the eye, the first set of candidate edge locations being disposed in a first boundary surface portion of the at least one anatomical structure, using a least-squares technique to fit a first mathematical surface model to the first set of the candidate edge locations, processing the image information based on proximity to the first mathematical surface model to identify a second set of candidate edge locations for the at least one anatomical structure, the second set of candidate locations being disposed in a second boundary surface portion of the at least one anatomical structure that is different from the first boundary surface portion, and using a least-squares technique to fit a second mathematical surface model to the combined first and second sets of candidate edge locations; and locating a boundary of the at least one anatomical feature based on the fit of the least-squares technique. 2. The method of claim 1 , wherein the at least one anatomical structure comprises a cornea. 3. The method of claim 1 , wherein the at least one anatomical structure comprises a sclera. 4. The method of claim 1 , wherein the at least one anatomical structure comprises a limbus. 5. The method of claim 1 , wherein the at least one anatomical structure comprises an iris. 6. The method of claim 1 , wherein the at least one anatomical structure comprises a lens. 7. The method of claim 1 , wherein the at least one anatomical structure comprises a lens capsule. 8. The method of claim 1 , further comprising performing a series of three or more least squares least-squares fit analyses, and separately processing the image information to identify additional candidate edge locations so that a greater number of candidate edge locations are used in each successive least squares least-squares fit analysis. 9. The method of claim 1 , wherein at least one of the first and second mathematical surface models is a spherical surface. 10. The method of claim 1 , wherein at least one of the first and second mathematical surface models is an aspherical surface. 11. The method of claim 1 , further comprising locating two or more boundaries between a corresponding three or more anatomical structures of the eye. 12. The method of claim 11 , wherein the boundaries include the intersection between a cornea of the eye and a sclera of the eye. 13. The method of claim 11 , wherein the boundaries include the intersection between a cornea of the eye and an iris of the eye. 14. The method of claim 11 , wherein the boundaries include the intersection between a lens of the eye and an iris of the eye. 15. The method of claim 1 , further comprising utilize one or more weighted candidate edge locations of a least squares least-squares fit analysis to locate an anatomical structure of the eye. 16. The method of claim 1 , wherein the second boundary surface portion completely surrounds the first boundary surface portion. 17. The method of claim 1 , wherein the image information comprises an A-Scan comprising at least one of a line, sequence, and column of a plurality of pixels. 18. The method of claim 17 , wherein the candidate edge locations are a set of XYZ triples comprising an X coordinate, a Y coordinate, and a Z coordinate representing the location of an edge pixel from amongst the plurality of pixels. 19. The method of claim 18 , further comprising performing a total of three or more least squares fit analyses, and separately processing the image information with additional candidate edge locations so that a greater number of candidate edge locations are used in each successive least squares least-squares fit analysis. 20. The method of claim 19 , wherein a boundary is located based on a weighting obtained from the least-squares technique.
Biomedical image inspection · CPC title
for optical coherence tomography [OCT] · CPC title
for photodisruption · CPC title
Lens · CPC title
Eye; Retina; Ophthalmic · CPC title
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