# US 12,224,066 B2 · Deep learning models for region of interest determination > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,224,066 B2 - Document ID: US12224066B2 - Status: Granted - Kind: B2 - Application number: 18/431,151 - Filed: 2024-02-02 - Published: 2024-05-23 - Granted: 2025-02-11 - Earliest claimed: 2021-05-24 - Assignees: NantOmics, LLC; Nanthealth, Inc.; NantCell, Inc. - Applicants: NantOmics, LLC; NantHealth, Inc.; NantCell, Inc. - Inventors: Mustafa I. Jaber; Bing Song; Christopher W. Szeto; Stephen Charles Benz; Shahrooz Rabizadeh; Liudmila A. Beziaeva - Web page: https://patentgrep.com/patent/US12224066B2 ## CPC - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning - G06N 3/09: Supervised learning - G06N 3/091: Active learning - G06N 3/096: Transfer learning - G06T 7/0012: {Biomedical image inspection} - G06T 7/11: Region-based segmentation - G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI] - G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]} - G16H 30/40: for processing medical images, e.g. editing - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 20/20: Ensemble learning - G06T 2207/10056: Microscopic image - G06T 2207/20021: Dividing image into blocks, subimages or windows - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro - G06T 2207/30096: Tumor; Lesion - G06V 2201/03: Recognition of patterns in medical or anatomical images - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A method of determining a region of interest in an image of tissue of an individual by an apparatus including processing circuitry may include executing, by the processing circuitry, instructions that cause the apparatus to partition an image of tissue of an individual into a set of areas, identify a tissue type of each area of the image, and apply a classifier to the image to determine a region of interest, the classifier being configured to determine regions of interest based on the tissue types of the set of areas of the image. ## Claims ### Claim 1 An image processing system comprising: at least one computer readable non-transitory memory storing software instructions, a first description of a structure type of a first region type, and a second description of a structure type of a second region type that is different than the first region type; and at least one processing circuitry coupled with the memory and that performs, upon execution of the software instructions, operations of: partitioning a digital image into a set of areas; identifying a structure type of each area of the digital image; determining regions of interest in the digital image by applying a classifier able to determine regions of interest based on structure types of the set of areas in the digital image, including determining a first region of interest in the image by matching the set of areas of the image with the first description of the first region type, and determining a second region of interest in the image by matching the set of areas of the image with the second description of the second region type; and presenting the regions of interest with different visual appearances indicating respective region types. ### Claim 2 (depends on 1) The image processing system of claim 1, wherein the different visual appearances include at least one of the following: a color difference, a shading difference, a highlighting difference, and a pattern difference. ### Claim 3 (depends on 1) The image processing system of claim 1, wherein the structure types comprise anatomic structures. ### Claim 4 (depends on 1) The image processing system of claim 1, wherein the structure types comprise at least one of the following: a lesion, a plaque, a clot, an infection, an inflammation, an anomaly, an implant, a blood vessel, a bone, a muscle, a tendon, and a foreign body. ### Claim 5 (depends on 1) The image processing system of claim 1, wherein the structure types comprise tissue types. ### Claim 6 (depends on 5) The image processing system of claim 5, wherein the tissue types include at least one of the following: a tumor type, a cancer type, and a noncancerous type. ### Claim 7 (depends on 5) The image processing system of claim 5, wherein the operations further include determining an outcome associated with an individual related to the tissue types. ### Claim 8 (depends on 7) The image processing system of claim 7, wherein the outcome relates to at least one of the following: a prognosis, a diagnosis, and a treatment. ### Claim 9 (depends on 1) The image processing system of claim 1, wherein the classifier comprises a neural network. ### Claim 10 (depends on 9) The image processing system of claim 9, wherein the classifier comprises a convolutional neural network. ### Claim 11 (depends on 1) The image processing system of claim 1, wherein the regions of interest include a boundary. ### Claim 12 (depends on 1) The image processing system of claim 1, wherein visual appearances include at least one mask. ### Claim 13 (depends on 12) The image processing system of claim 12, wherein the at least one mask comprises a label. ### Claim 14 (depends on 1) The image processing system of claim 1, wherein the first description and the second description each include at least one of the following related to the regions of interest: a number of a subset of areas, a size of the subset of areas, a density of the subset of areas, a moment of the subset of areas, a shape of the subset of areas, a symmetry of the subset of areas, a spatial distribution of the subset of areas, a visual appearance of the subset of areas, and a distance between the subset of areas. ### Claim 15 (depends on 1) The image processing system of claim 1, wherein the regions of interest comprise at least one similarity measure. ### Claim 16 (depends on 15) The image processing system of claim 15, wherein the at least one similarity measure comprises a degree of confidence for an identified region of interest. ### Claim 17 (depends on 15) The image processing system of claim 15, wherein the at least one similarity measure represents at least one of the following: a similarity between feature measurements, a difference between feature measurements, and an aggregated feature measurement. ### Claim 18 (depends on 1) The image processing system of claim 1, wherein regions of interest comprise a label. ### Claim 19 (depends on 18) The image processing system of claim 18, wherein the label comprises a text label. ### Claim 20 (depends on 18) The image processing system of claim 18, wherein the digital image comprises an x-ray image.