US-20260127745-A1 - SYSTEMS AND METHODS TO PROCESS ELECTRONIC IMAGES TO SELECTIVELY HIDE STRUCTURES AND ARTIFACTS FOR DIGITAL PATHOLOGY IMAGE REVIEW

US20260127745A1US 20260127745 A1US20260127745 A1US 20260127745A1US-20260127745-A1

Abstract

A method for filtering out artifacts from a digital pathology image of a tissue, the method comprising: determine a plurality of scores corresponding to a plurality of pixels in the digital pathology image of the tissue; group the plurality of pixels into a plurality of pixel clusters based on the plurality of scores corresponding to the plurality of pixels; identify, from the plurality of pixel clusters, one or more pixel clusters corresponding to one or more artifacts in the digital pathology image; and filter the digital pathology image by removing one or more regions in the digital pathology image corresponding to the one or more pixel clusters corresponding to the one or more artifacts.

Inventors

Navid Alemi
Christopher Kanan

Assignees

PAIGE.AI, Inc.

Dates

Publication Date: 20260507
Application Date: 20251229

Claims (20)

1 . A system for filtering out artifacts from a digital pathology image of a tissue, the system comprising one or more processors, memory, and one or more programs stored in the memory for execution by the one or more processors, the one or more programs including instructions that when executed by the one or more processors cause the system to: determine a plurality of scores corresponding to a plurality of pixels in the digital pathology image of the tissue; group the plurality of pixels into a plurality of pixel clusters based on the plurality of scores corresponding to the plurality of pixels; identify, from the plurality of pixel clusters, one or more pixel clusters corresponding to one or more artifacts in the digital pathology image; and filter the digital pathology image by removing one or more regions in the digital pathology image corresponding to the one or more pixel clusters corresponding to the one or more artifacts.
2 . The system of claim 1 , wherein the digital pathology image of the tissue comprises a whole slide image.
3 . The system of claim 1 , wherein the one or more artifacts comprise: a tissue fold, a pen marking, an air bubble, defocus, or any combination thereof.
4 . The system of claim 1 , wherein determining the plurality of scores corresponding to the plurality of pixels in the digital pathology image of the tissue comprises computing a Laplacian of the digital pathology image of the tissue.
5 . The system of claim 4 , wherein the one or more programs include instructions that when executed by the one or more processors cause the system to reduce noise in the plurality of pixels in the digital pathology image.
6 . The system of claim 1 , wherein grouping the plurality of pixels into the plurality of pixel clusters comprises identifying a plurality of initial pixel clusters via a K-means algorithm.
7 . The system of claim 1 , wherein identifying the one or more pixel clusters corresponding to the one or more artifacts in the digital pathology image comprises: identifying a foreground portion and a background portion of the digital pathology image, wherein the one or more artifacts are located in the background portion of the digital pathology image.
8 . The system of claim 7 , wherein the foreground portion and the background portion of the digital pathology image are identified via a binary thresholding algorithm.
9 . The system of claim 1 , wherein the one or more programs include instructions that when executed by the one or more processors cause the system to further filter the digital pathology image by removing a region in the digital pathology image corresponding to a pixel cluster below a predefined score.
10 . The system of claim 1 , wherein the one or more programs include instructions that when executed by the one or more processors cause the system to fill one or more holes in the filtered digital pathology image.
11 . The system of claim 1 , wherein the one or more programs include instructions that when executed by the one or more processors cause the system to input the filtered digital pathology image or a representation of the filtered digital pathology image into a trained machine learning model.
12 . The system of claim 11 , wherein the trained machine learning model is configured to provide an output indicative of a diagnosis, a treatment, an association between phenotypes, an outcome prediction, a subtype classification, an imputed value, or any combination thereof.
13 . A method for filtering out artifacts from a digital pathology image of a tissue, the method comprising: determine a plurality of scores corresponding to a plurality of pixels in the digital pathology image of the tissue; group the plurality of pixels into a plurality of pixel clusters based on the plurality of scores corresponding to the plurality of pixels; identify, from the plurality of pixel clusters, one or more pixel clusters corresponding to one or more artifacts in the digital pathology image; and filter the digital pathology image by removing one or more regions in the digital pathology image corresponding to the one or more pixel clusters corresponding to the one or more artifacts.
14 . The method of claim 13 , wherein the digital pathology image of the tissue comprises a whole slide image.
15 . The method of claim 13 , wherein the one or more artifacts comprise: a tissue fold, a pen marking, an air bubble, defocus, or any combination thereof.
16 . The method of claim 13 , wherein determining the plurality of scores corresponding to the plurality of pixels in the digital pathology image of the tissue comprises computing a Laplacian of the digital pathology image of the tissue.
17 . A non-transitory computer-readable medium storing instructions for filtering out artifacts from a digital pathology image of a tissue, wherein the instructions are executable by a system comprising one or more processors to cause the system to: determine a plurality of scores corresponding to a plurality of pixels in the digital pathology image of the tissue; group the plurality of pixels into a plurality of pixel clusters based on the plurality of scores corresponding to the plurality of pixels; identify, from the plurality of pixel clusters, one or more pixel clusters corresponding to one or more artifacts in the digital pathology image; and filter the digital pathology image by removing one or more regions in the digital pathology image corresponding to the one or more pixel clusters corresponding to the one or more artifacts.
18 . The non-transitory computer-readable medium of claim 17 , wherein the digital pathology image of the tissue comprises a whole slide image.
19 . The non-transitory computer-readable medium of claim 17 , wherein the one or more artifacts comprise: a tissue fold, a pen marking, an air bubble, defocus, or any combination thereof.
20 . The non-transitory computer-readable medium of claim 17 , wherein determining the plurality of scores corresponding to the plurality of pixels in the digital pathology image of the tissue comprises computing a Laplacian of the digital pathology image of the tissue.

Description

RELATED APPLICATION(S) This application is a continuation of and claims the benefit of priority to U.S. application Ser. No. 17/934,908, filed on Sep. 23, 2022, which claims priority to U.S. Provisional Application No. 63/261,706 filed Sep. 27, 2021, each of which are incorporated herein by reference in their entirety. FIELD OF THE DISCLOSURE Various embodiments of the present disclosure pertain generally to image processing methods. More specifically, particular embodiments of the present disclosure relate to systems and methods to selectively hide artifacts during digital review. BACKGROUND In human and animal pathology, visual examination of tissue under a microscope may be vital to diagnostic medicine, e.g., to diagnose cancer or in drug development (such as in assessing toxicity). With current pathology techniques, tissue samples may undergo multiple preparation steps so that different tissue structures may be differentiated visually by the human eye. These steps may consist of: (i) preserving the tissue using fixation; (ii) embedding the tissue in a paraffin block; (iii) cutting the paraffin block into thin sections (e.g., 3-5 micrometers or μm); (iv) mounting the sections on glass slides; and (v) staining mounted tissue sections to highlight important components or structures. With the use of stains and dyes, histology allows pathologists to visualize tissue structures and/or tissues, chemical elements within cells, and even microorganisms. However, some structures (e.g., hair, ink, bubbles, etc.) on a slide and/or appearing in an image of the slide may interfere with a visualization experience. The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art, or suggestions of the prior art, by inclusion in this section. SUMMARY According to certain aspects of the present disclosure, systems and methods are disclosed for processing electronic medical images, comprising: receiving a plurality of digital pathology images of at least one pathology specimen, the pathology specimen being associated with a patient; determining, using a machine learning system, whether artifacts or objects of interest are present on the digital pathology images; upon determining that an artifact or object of interest is present, determining one or more regions on the digital pathology images that contain artifacts or objects of interest; upon determining the regions on the digital pathology images that contain artifacts or objects of interest, using a machine learning system to inpaint or suppress the region; and outputting the digital pathology images with the artifacts or objects of interest inpatined or suppressed. A system for processing electronic medical images, the system including: at least one memory storing instructions; and at least one processor configured to execute the instructions to perform operations including: receiving a plurality of digital pathology images of at least one pathology specimen, the pathology specimen being associated with a patient; determining, using a machine learning system, whether artifacts or objects of interest are present on the digital pathology images; upon determining that an artifact or object of interest is present, determining one or more regions on the digital pathology images that contain artifacts or objects of interest; upon determining the regions on the digital pathology images that contain artifacts or objects of interest, using a machine learning system to inpaint or suppress the region; and outputting the digital pathology images with the artifacts or objects of interest inpatined or suppressed. A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform operations processing electronic medical images, the operations including: receiving a plurality of digital pathology images of at least one pathology specimen, the pathology specimen being associated with a patient; determining, using a machine learning system, whether artifacts or objects of interest are present on the digital pathology images; upon determining that an artifact or object of interest is present, determining one or more regions on the digital pathology images that contain artifacts or objects of interest; upon determining the regions on the digital pathology images that contain artifacts or objects of interest, using a machine learning system to inpaint or suppress the region; and outputting the digital pathology images with the artifacts or objects of interest inpatined or suppressed. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various exemplary embodiments and together with the description, serve to explain the principles of the disclosed emb