# US 12,217,423 B2 · Systems and methods for processing electronic images to detect contamination in specimen preparations > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,217,423 B2 - Document ID: US12217423B2 - Status: Granted - Kind: B2 - Application number: 18/487,264 - Filed: 2023-10-16 - Published: 2024-02-22 - Granted: 2025-02-04 - Earliest claimed: 2019-12-20 - Assignees: Paige.AI, Inc. - Applicants: PAIGE.AI, Inc. - Inventors: Patricia Raciti; Christopher Kanan; Thomas Fuchs; Leo Grady - Web page: https://patentgrep.com/patent/US12217423B2 ## CPC - G06T 7/0012: {Biomedical image inspection} - G06T 7/11: Region-based segmentation - G06T 7/194: involving foreground-background segmentation - G06V 10/764: using classification, e.g. of video objects - G06V 10/776: Validation; Performance evaluation - G06V 10/82: using neural networks - G06V 10/993: {Evaluation of the quality of the acquired pattern} - G06V 20/69: Microscopic objects, e.g. biological cells or cellular parts - G06T 2207/10024: Color image - G06T 2207/10056: Microscopic image - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro - G06V 2201/03: Recognition of patterns in medical or anatomical images - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Systems and methods are disclosed for 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, of whether at least one first image region of the one or more image regions comprises at least one external contaminant, the machine learning system having been trained using a plurality of training images to predict a presence of external contaminants and/or a location of any external contaminants present in the tissue specimen, and determining, based on the prediction of whether a first image region comprises an external contaminant, whether to process the image region using an processing algorithm. ## Claims ### Claim 1 A computer-implemented method for processing digital images of a tissue specimen, the method comprising: receiving, by a floater detection platform, the digital images of the tissue specimen; receiving, by the floater detection platform, data associated with a tissue type of the tissue specimen, the data including a tissue class; detecting, by the floater detection platform, one or more contiguous tissue pieces of the tissue specimen using an analysis of the digital images; extracting, by the floater detection platform, a plurality of features from each contiguous tissue piece of the one or more contiguous tissue pieces of the tissue specimen; providing, by the floater detection platform, the plurality of features from each contiguous tissue piece to a machine-learning model, wherein the machine-learning model has been trained, using one or more gathered and/or simulated sets of digital images of a plurality of tissue specimens, to identify a sample tissue class of each contiguous tissue piece based on the plurality of features; outputting, by the machine-learning model, a first sample tissue class of a first contiguous tissue piece; comparing, by the floater detection platform, the first sample tissue class of the first contiguous tissue piece with the tissue class of the tissue specimen to determine a tissue class mismatch; and generating, by the floater detection platform, an alert based on the tissue class mismatch. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the analysis of the digital images is implemented by one or more of Faster R-CNN, selective search, EdgeBoxes, DeepBox, Mask R-CNN, or U-Net. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the plurality of features from each contiguous tissue piece comprise one or more of convolutional neural network (CNN) features, SIFT features, or SURF features. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the alert comprises one or more of an output transmitted to a computing device or a marker indicating a location of an external contaminant. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the analysis of the digital images comprises: detecting, by the floater detection platform, a first image region from a background of a digital image of the digital images. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, further comprising: providing, by the floater detection platform, the digital images to a database for storage, each digital image associated with a timestamp. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the machine-learning model 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. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein the machine-learning model is implemented by feature extraction using hand-engineered features and at least one of random forest, support vector machine (SVM), and/or deep learning. ### Claim 9 (depends on 8) The computer-implemented method of claim 8, wherein the hand-engineered features comprise at least one of 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). ### Claim 10 (depends on 1) The computer-implemented method of claim 1, wherein the floater detection platform includes a slide analysis tool. ### Claim 11 A computer-implemented method for training a machine-learning model for processing digital images of a tissue specimen, the method comprising: providing, by one or more processors, one or more gathered and/or simulated sets of digital images of a plurality of tissue specimens to one or more machine-learning algorithms as one or more sets of training data; determining, by the one or more machine-learning algorithms, associations between the one or more gathered and/or simulated sets of digital images and one or more tissue features; modifying one or more of a layer, a weight, a synapse, or a node of the machine-learning model based on the associations between the one or more gathered and/or simulated sets of digital images and the one or more tissue features; and outputting, by the one or more processors, the machine-learning model, wherein the machine-learning model is trained to identify a sample tissue class of a contiguous tissue piece based on the one or more tissue features and the modified one or more of the layer, the weight, the synapse, or the node of the machine-learning model. ### Claim 12 (depends on 11) The computer-implemented method of claim 11, wherein the machine-learning model 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. ### Claim 13 (depends on 11) The computer-implemented method of claim 11, wherein the machine-learning model is implemented by feature extraction using hand-engineered features and at least one of random forest, support vector machine (SVM), and/or deep learning. ### Claim 14 (depends on 11) The computer-implemented method of claim 11, further comprising: providing, by the one or more processors, data associated with a tissue type of a tissue specimen, the data including a tissue class. ### Claim 15 A system for processing digital images of a tissue specimen, the system comprising: a memory storing instruction and a trained machine-learning model trained to identify a presence of one or more external contaminants and/or a location of the one or more external contaminants present and output an external contaminant prediction; and a processor operatively connected to the memory and configured to execute the instructions to perform operations including: receiving, by a floater detection platform, the digital images of the tissue specimen; receiving, by the floater detection platform, data associated with a tissue type of the tissue specimen, the data including a tissue class; detecting, by the floater detection platform, one or more contiguous tissue pieces of the tissue specimen using an analysis of the digital images; extracting, by the floater detection platform, a plurality of features from each contiguous tissue piece of the one or more contiguous tissue pieces of the tissue specimen; providing, by the floater detection platform, the plurality of features from each contiguous tissue piece to a machine-learning model, wherein the machine-learning model has been trained, using one or more gathered and/or simulated sets of digital images of a plurality of tissue specimens, to identify a sample tissue class of each contiguous tissue piece based on the plurality of features; outputting, by the machine-learning model, a first sample tissue class of a first contiguous tissue piece; comparing, by the floater detection platform, the first sample tissue class of the first contiguous tissue piece with the tissue class of the tissue specimen to determine a tissue class mismatch; and generating, by the floater detection platform, an alert based on the tissue class mismatch. ### Claim 16 (depends on 15) The system of claim 15, wherein the analysis of the digital images is implemented by one or more of Faster R-CNN, selective search, EdgeBoxes, DeepBox, Mask R-CNN, or U-Net. ### Claim 17 (depends on 15) The system of claim 15, wherein the plurality of features from each contiguous tissue piece comprise one or more of convolutional neural network (CNN) features, SIFT features, or SURF features. ### Claim 18 (depends on 15) The system of claim 15, wherein the alert comprises one or more of an output transmitted to a computing device or a marker indicating a location of an external contaminant. ### Claim 19 (depends on 15) The system of claim 15, wherein the analysis of the digital images comprises: detecting, by the floater detection platform, a first image region from a background of a digital image of the digital images. ### Claim 20 (depends on 15) The system of claim 15, further comprising: providing, by the floater detection platform, the digital images to a database for storage, each digital image associated with a timestamp.