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1. WO2019199392 - MICROSCOPE À RÉALITÉ AUGMENTÉE POUR PATHOLOGIE AVEC SUPERPOSITION DE DONNÉES QUANTITATIVES DE BIOMARQUEURS

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CLAIMS

What is claimed is:

1. A method for assisting a user in review of a slide containing a biological sample with a microscope having an eyepiece comprising the steps of:

(a) capturing, with a camera, a digital image of a view of the sample as seen through the eyepiece of the microscope,

(b) using a first machine learning pattern recognizer to identify one or more areas of interest in the sample from the image captured by the camera, and a second machine pattern recognizer trained to identify individual cells and

(c) superimposing an enhancement to the view of the sample as seen through the eyepiece of the microscope as an overlay, wherein the enhancement is based upon the identified areas of interest in the sample and further comprises quantitative data associated with the areas of interest,

(d) wherein, when the sample is moved relative to the microscope optics or when a magnification or focus of the microscope changes, a new digital image of a new view of the sample is captured by the camera and supplied to the machine learning pattern recognizer, and a new enhancement is superimposed onto the new view of the sample as seen through the eyepiece in substantial real time.

2. The method of claim 1, wherein the one or more areas of interest comprise cells positive for expression of a protein and wherein the quantitative data comprises a percent of the cells in the view as being positive for such protein expression.

3. The method of claim 2, wherein the protein comprises Ki-67, P53, Estrogen Receptor (ER) or Progesterone Receptor (PR).

4, The method of claim 1, wherein the one or more areas of interest comprise individual microorganism cells and the quantitative data comprises a count of the number of microorganism cells in the view.

5. The method of claim 1, wherein the one or more areas of interest comprise individual cells undergoing mitosis and wherein the quantitative data comprises a count of the number of cells in the view undergoing mitosis.

6. The method of claim 1, wherein the areas of interest comprise tumor cells and wherein the quantitative data comprises an area measurement of the tumor cells, either absolute or relative area within a defined region in the sample.

7. The method of any of claims 1-6, further comprising the step of providing on a workstation associated with the microscope a graphical display providing access to tools to customize the presentation of the enhancement on the field of view.

8. The method of claim 1, wherein the quantitative data comprises a measurement.

9. The method of claim 8, wherein the measurement comprises an area measurement and wherein the areas of interest comprise prostate tissue with specific Gleason grades.

10. The method of claim 1, wherein the quantitative data comprises a count of the number of areas of interest in the view.

11. A system assisting a user in review of a slide containing a biological sample, comprising:

a microscope having a stage for holding a slide containing a biological sample, at least one objective lens, and an eyepiece,

a digital camera configured to capture digital images of a view of the sample as seen through the eyepiece of the microscope,

a compute unit comprising a machine learning pattern recognizer configured to receive the digital images from the digital camera, wherein the pattern recognizer is trained to identify regions of interest in biological samples of the type currently placed on the stage, and wherein the pattern recognizer recognizes regions of interest on a digital image captured by the camera and wherein the compute unit generates data representing an enhancement to the view of the sample as seen through the eyepiece of the microscope, wherein the enhancement is based upon the regions of interest in the sample; and

one or more optical components coupled to the eyepiece for superimposing the enhancement on the field of view;

wherein the compute unit implements a first machine learning pattern recognizer trained to identify individual cells within the view and a second machine learning pattern recognizer trained to identify individual cells within the view which are positive for expression of a protein.

and wherein the enhancement further comprises a display of quantitative data relating to the ceils which are positive for the expression of the protein.

12. The system of claim 10, wherein the protein comprises Ki-67, P53, Estrogen Receptor or Progesterone Receptor (PR).

13. A system assisting a user in review of a slide containing a biological sample, comprising:

a microscope having a stage for holding a slide containing a biological sample, at least one objective lens, and an eyepiece,

a digital camera configured to capture digital images of a view of the sample as seen through the eyepiece of the microscope,

a compute unit comprising a machine learning pattern recognizer configured to receive the digital images from the digital camera, wherein the pattern recognizer is trained to identify regions of interest in biological samples of the type currently placed on the stage, and wherein the pattern recognizer recognizes regions of interest on a digital image captured by the camera and wherein the compute unit generates data representing an enhancement to the view of the sample as seen through the eyepiece of the microscope, wherein the enhancement is based upon the regions of interest in the sample; and one or more optical components coupled to the eyepiece for superimposing the enhancement on the field of view:

wherein the compute unit implements a machine learning pattern recognizer trained to identify individual cells which are undergoing mitosis;

and wherein the enhancement further comprises a display of quantitative data relating to the ceils which are undergoing mitosis.

14. A system assisting a user in review of a slide containing a biological sample, comprising:

a microscope having a stage for holding a slide containing a biological sample, at least one objective lens, and an eyepiece,

a digital camera configured to capture digital images of a view of the sample as seen through the eyepiece of the microscope,

a compute unit comprising a machine learning pattern recognizer configured to receive the digital images from the digital camera, wherein the pattern recognizer is trained to identify regions of interest in biological samples of the type currently placed on the stage, and wherein the pattern recognizer recognizes regions of interest on a digital image captured by the camera and wherein the compute unit generates data representing an

enhancement to the view of the sample as seen through the eyepiece of the microscope, wherein the enhancement is based upon the regions of interest in the sample; and one or more optical components coupled to the eyepiece for superimposing the enhancement on the field of view;

wherein the compute unit implements one or more machine learning pattern recognizers trained to identify individual tumor cells or areas of tumor cells which are classified in accordance with specific Gleason grades,

and wherein the enhancement further comprises a display of quantitative area data relating to the tumor cells or areas of tumor cells which are classified in accordance with specific Gleason grades.

15. The system of any of claims 11-14, further comprising a workstation associated with the microscope having a display providing tools for a user of the workstation to draw an annotation on an image of the view, and wherein the annotation is saved along with the image of the view in a computer memory.

16. The system of any of claims 11-14, further comprising a workstation associated with the microscope having a display, wherein the display providing access to tools to customize the presentation of the enhancement on the field of view.