Systems and methods for processing electronic images to detect contamination in specimen preparations

1. A computer-implemented method for processing an electronic image corresponding to a tissue specimen, the method comprising:

receiving one or more digital images associated with a tissue specimen;

detecting one or more image regions from a background of the one or more digital images;

determining a prediction, using a machine learning system, that at least a first image region of the one or more image regions comprises at least one foreign tissue contaminant, the machine learning system having been trained using a plurality of training images associated with a plurality of tissue specimens to predict a presence of one or more foreign tissue contaminants and/or a location of the one or more foreign tissue contaminants present in the plurality of training images; and

determining, based on the prediction that the first image region comprises the at least one foreign tissue contaminant, to not process the first image region using a processing algorithm.

2. The computer implemented method of claim 1 , wherein the background of the one or more digital images comprises a portion of the one or more digital images that is not the tissue specimen.

3. The computer implemented method of claim 1 , wherein the detecting of the one or more image regions from the background of the one or more digital images comprises:

thresholding based on color intensity, texture features, and/or Otsu's method.

4. The computer implemented method of claim 1 , wherein the detecting of the one or more image regions from the background of the one or more digital images comprises using one or more segmentation algorithms.

5. The computer implemented method of claim 1 , wherein the detecting of the one or more image regions from the background of the one or more digital images comprises using bounding box detection.

6. The computer implemented method of claim 1 , further comprising:

storing the one or more digital images in at least one database along with a timestamp associated with the one or more digital images.

7. The computer implemented method of claim 1 , wherein the machine learning system is implemented by a convolutional neural network (CNN), Region CNN (R-CNN), Faster R-CNN, Mask R-CNN, Convolutional Graph Neural Network, and/or Relationship Neural Network.

8. The computer implemented method of claim 1 , wherein the machine learning system is implemented by feature extraction using hand-engineered features and at least one from among: a random forest, support vector machine (SVM), and/or deep learning.

9. The computer implemented method of claim 8 , wherein the hand-engineered features comprise at least one from Scale-Invariant Feature Transform (SIFT), Speeded up robust features (SURF), Rotation Invariant Feature Transform (RIFT), Oriented feature from accelerated segment test (FAST) and/or Rotated Binary Robust Independent Elementary Features (BRIEF) (ORB).

10. The computer implemented method of claim 1 , wherein the machine learning system is trained using the plurality of training images by:

receiving the plurality of training images, wherein at least one training image of the plurality of training images includes at least one foreign tissue contaminant in a background of the at least one training image;

receiving information about a localization of the at least one foreign tissue contaminant included in the at least one training image of the plurality of training images;

receiving information about a tissue type of the tissue specimen depicted in the at least one training image;

detecting one or more image regions from the background of the at least one training image; and

training the machine learning system to indicate a presence of the at least one foreign tissue contaminant in the at least one training image and/or a specific location of the at least one foreign tissue contaminant present in the at least one training image.

11. The computer implemented method of claim 1 , further comprising:

receiving information about a tissue type of the tissue specimen shown in the one or more digital images of the received one or more digital images; and

determining the prediction at least in part based on the tissue type.

12. A system for processing an electronic image corresponding to a tissue specimen, the system comprising:

at least one memory storing instructions; and

at least one processor configured to execute the instructions to perform operations comprising:

receiving one or more digital images associated with a tissue specimen;

detecting one or more image regions from a background of the one or more digital images;

determining a prediction, using a machine learning system, that at least a first image region of the one or more image regions comprises at least one foreign tissue contaminant, the machine learning system having been trained using a plurality of training images associated with a plurality of tissue specimens to predict a presence of one or more foreign tissue contaminants and/or a location of the one or more foreign tissue contaminants present in the plurality of training images; and

determining, based on the prediction that the first image region comprises the at least one foreign tissue contaminant, to not process the first image region using a processing algorithm.

13. The system of claim 12 , wherein the background of the one or more digital images comprises a portion of the one or more digital images that is not the tissue specimen.

14. The system of claim 12 , wherein the detecting of the one or more image regions from the background of the one or more digital images comprises:

thresholding based on color intensity, texture features, and/or Otsu's method.

15. The system of claim 12 , wherein the detecting of the one or more image regions from the background of the one or more digital images comprises using one or more segmentation algorithms.

16. The system of claim 12 , wherein the detecting of the one or more image regions from the background of the one or more digital images comprises using bounding box detection.

17. The system of claim 12 , wherein the machine learning system is trained using the plurality of training images by:

receiving the plurality of training images, wherein at least one training image of the plurality of training images includes at least one foreign tissue contaminant in a background of the at least one training image;

receiving information about a localization of the at least one foreign tissue contaminant included in the at least one training image of the plurality of training images;

receiving information about a tissue type of the tissue specimen depicted in the at least one training image;

detecting one or more image regions from the background of the at least one training image; and

training a machine learning system to indicate a presence of the at least one foreign tissue contaminant in the at least one training image and/or a specific location of the at least one foreign tissue contaminant present in the at least one training image.

18. The system of claim 12 , wherein the machine learning system is implemented by CNN, R-CNN, Faster R-CNN, Mask R-CNN, Convolutional Graph Neural Network, and/or Relationship Neural Network.

19. At least one non-transitory computer readable medium for processing an electronic image corresponding to a tissue specimen, the at least one non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

receiving one or more digital images associated with a tissue specimen;

detecting one or more image regions from a background of the one or more digital images;

determining a prediction, using a machine learning system, that at least a first image region of the one or more image regions comprises at least one foreign tissue contaminant, the machine learning system having been trained using a plurality of training images associated with a plurality of tissue specimens to predict a presence of one or more foreign tissue contaminants and/or a location of the one or more foreign tissue contaminants present in the plurality of training images; and

determining, based on the prediction that the first image region comprises the at least one foreign tissue contaminant, to not process the first image region using a processing algorithm.