SYSTEM AND METHOD FOR THRESHOLDING FOR RESIDUAL CANCER CELL DETECTION

1 . A system for determining a patient-specific threshold used to detect abnormal cells, the system comprising:

a medical imaging device configured to produce a set of images of an anatomy of a patient;

an image analysis system comprising one or more processors configured to:

receive the set of images; and

analyze the set of images to determine a patient-specific threshold to use to detect abnormal tissue of the patient.

2 . The system of claim 1 , wherein the image analysis system is configured to analyze the set of images by calculating, using a pixel comparator, a representative intensity value for each image in the set of images that represents one or more pixel intensity values of the associated image to generate a set of representative intensity values.

3 . The system of claim 2 , wherein the image analysis system is configured to:

select a subset of the set of representative intensity values based on a predetermined criterion that is used to filter out one or more of the representative intensity values in the set of representative intensity values;

average the selected subset of representative intensity values to calculate an averaged intensity value; and

calculate the patient-specific threshold based on the averaged intensity value and an adjustment factor associated with the pixel comparator.

4 . The system of claim 1 , wherein the images are surgical site images of a surgical site of the patient.

5 . The system of claim 4 , wherein:

the medical imaging device is configured to produce a new surgical site image;

the image analysis system is further configured to identify one or more groups of abnormal cells in the new surgical site image based on the calculated patient-specific threshold; and

the system comprises a display configured to indicate one or more locations of at least one of the one or more groups of identified abnormal cells.

6 . The system of claim 2 , wherein the pixel comparator is selected from the group consisting of:

a maximums comparator that calculates the representative intensity value for each image in the set of images by calculating a maximum pixel intensity value of each image;

a minimums comparator that calculates the representative intensity value for each image in the set of images by calculating a minimum pixel intensity value of each image;

a means comparator that calculates the representative intensity value for each image in the set of images by calculating a mean of pixel intensity values of each image;

a twenty-fifth percentile comparator that calculates the representative intensity value for each image in the set of images by calculating a 25% percentile of pixel intensity values of each image; and

a medians comparator that calculates the representative intensity value for each image in the set of images by calculating a median pixel intensity value for each image.

7 . The system of claim 2 , wherein the image analysis system is configured to select the pixel comparator from a plurality of pixel comparators, comprising:

running the plurality of pixel comparators on a training set of images to calculate a set of results values for each of the pixel comparators in the plurality of pixel comparators;

analyzing the set of results values for each pixel comparator in the plurality of pixel comparators; and

selecting the pixel comparator from the plurality of pixel comparators based on the analysis.

8 . A computer-implemented method for determining a patient-specific threshold used to detect abnormal cells, the method comprising:

receiving a set of images of an anatomy of a patient; and

analyzing the set of images to determine a patient-specific threshold to use to detect abnormal tissue of the patient.

9 . The method of claim 8 , comprising:

analyzing the set of images by calculating, using a pixel comparator, a representative intensity value for each image in the set of images that represents one or more pixel intensity values of the associated image to generate a set of representative intensity values.

10 . The method of claim 9 , further comprising:

selecting a subset of the set of representative intensity values based on a predetermined criterion that is used to filter out one or more of the representative intensity values in the set of representative intensity values;

averaging the selected subset of representative intensity values to calculate an averaged intensity value; and

calculating the patient-specific threshold based on the averaged intensity value and an adjustment factor associated with the pixel comparator.

11 . The method of claim 8 , wherein the images are surgical site images of a surgical site of the patient.

12 . The method of claim 11 , further comprising:

receiving a new surgical site image;

identifying one or more groups of abnormal cells in the new surgical site image based on the calculated patient-specific threshold; and

indicating, via a display, one or more locations of at least one of the one or more groups of identified abnormal cells.

13 . The method of claim 9 , wherein the pixel comparator is selected from the group consisting of:

a maximums comparator that calculates the representative intensity value for each image in the set of images by calculating a maximum pixel intensity value of each image;

a minimums comparator that calculates the representative intensity value for each image in the set of images by calculating a minimum pixel intensity value of each image;

a means comparator that calculates the representative intensity value for each image in the set of images by calculating a mean of pixel intensity values of each image;

a twenty-fifth percentile comparator that calculates the representative intensity value for each image in the set of images by calculating a 25% percentile of pixel intensity values of each image; and

a medians comparator that calculates the representative intensity value for each image in the set of images by calculating a median pixel intensity value for each image.

14 . The method of claim 9 , further comprising selecting the pixel comparator from a plurality of pixel comparators, comprising:

running the plurality of pixel comparators on a training set of images to calculate a set of results values for each of the pixel comparators in the plurality of pixel comparators;

analyzing the set of results values for each pixel comparator in the plurality of pixel comparators; and

selecting the pixel comparator from the plurality of pixel comparators based on the analysis.

15 . At least one non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed by at least one processor of an image analysis system configured to collect one or more images, perform a method to calculate a threshold used to detect abnormal cells, the method comprising:

receiving a set of images of an anatomy of a patient; and

analyzing the set of images to determine a patient-specific threshold to use to detect abnormal tissue of the patient.

16 . The at least one non-transitory computer-readable storage medium of claim 15 , the method further comprising:

analyzing the set of images by calculating, using a pixel comparator, a representative intensity value for each image in the set of images that represents one or more pixel intensity values of the associated image to generate a set of representative intensity values.

17 . The at least one non-transitory computer-readable storage medium of claim 16 , the method further comprising:

selecting a subset of the set of representative intensity values based on a predetermined criterion that is used to filter out one or more of the representative intensity values in the set of representative intensity values;

averaging the selected subset of representative intensity values to calculate an averaged intensity value; and

calculating the patient-specific threshold based on the averaged intensity value and an adjustment factor associated with the pixel comparator.

18 . The at least one non-transitory computer-readable storage medium of claim 15 , wherein the images are surgical site images of a surgical site of the patient.

19 . The at least one non-transitory computer-readable storage medium of claim 18 , the method further comprising:

receiving a new surgical site image;

identifying one or more groups of abnormal cells in the new surgical site image based on the calculated patient-specific threshold; and

indicating, via a display, one or more locations of at least one of the one or more groups of identified abnormal cells.

20 . The at least one non-transitory computer-readable storage medium of claim 16 , wherein the pixel comparator is selected from the group consisting of:

a maximums comparator that calculates the representative intensity value for each image in the set of images by calculating a maximum pixel intensity value of each image;

a minimums comparator that calculates the representative intensity value for each image in the set of images by calculating a minimum pixel intensity value of each image;

a means comparator that calculates the representative intensity value for each image in the set of images by calculating a mean of pixel intensity values of each image;

a twenty-fifth percentile comparator that calculates the representative intensity value for each image in the set of images by calculating a 25% percentile of pixel intensity values of each image; and

a medians comparator that calculates the representative intensity value for each image in the set of images by calculating a median pixel intensity value for each image.

21 . The at least one non-transitory computer-readable storage medium of claim 16 , the method further comprising selecting the pixel comparator from a plurality of pixel comparators, comprising:

running the plurality of pixel comparators on a training set of images to calculate a set of results values for each of the pixel comparators in the plurality of pixel comparators;

analyzing the set of results values for each pixel comparator in the plurality of pixel comparators; and

selecting the pixel comparator from the plurality of pixel comparators based on the analysis.