# US 2020/0402231 A1 · System and method for quantitative volumetric assessment and modeling of tumor lesions > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0402231 A1 - Document ID: US20200402231A1 - Status: Published application - Kind: A1 - Application number: 16/903,911 - Filed: 2020-06-17 - Published: 2020-12-24 - Earliest claimed: 2019-06-20 - Assignees: Ohio State Innovation Foundation - Applicants: Ohio State Innovation Foundation - Inventors: Sissy M. Jhiang; Chia-Hsiang Menq; Peng Cheng - Web page: https://patentgrep.com/patent/US20200402231A1 ## CPC - G06T 7/0012: {Biomedical image inspection} - A61B 5/4842: {Monitoring progression or stage of a disease} - G06T 7/0016: {involving temporal comparison} - G06T 7/11: Region-based segmentation - G06T 7/136: involving thresholding - G06T 7/155: involving morphological operators - G06T 7/30: Determination of transform parameters for the alignment of images, i.e. image registration - A61B 5/0033: {Features or image-related aspects of imaging apparatus, e.g. for MRI, optical tomography or impedance tomography apparatus; Arrangements of imaging apparatus in a room} - A61B 5/004: {adapted for image acquisition of a particular organ or body part (A61B5/0082 takes precedence; arrangements for optical scanning A61B5/0062)} - A61B 5/0073: {by tomography, i.e. reconstruction of 3D images from 2D projections (A61B5/0066 takes precedence)} - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/10104: Positron emission tomography [PET] - G06T 2207/10108: Single photon emission computed tomography [SPECT] - G06T 2207/30061: Lung - G06T 2207/30096: Tumor; Lesion - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract Disclosed are systems, methods, and computer program products for tumor lesion identification, segmentation, tracking and analysis, wherein a 3D spatial distribution of the tumor lesions in a target organ forms a unique 3D point structure for a patient. ## Claims ### Claim 1 A method for tumor lesion identification, segmentation, tracking and analysis comprising: obtaining a first set of one or more three-dimensional (3D) digital images, wherein at least a portion of the one or more 3D digital images comprises a target organ; segmenting the first set of one or more 3D digital images of the target organ into one or more first segment volumes of the target organ; identifying and segmenting a first set of one or more tumor lesions in each of the one or more first segment volumes of the target organ; determining for each identified tumor lesion in the first set of one or more tumor lesions a three-dimensional location and a volume; creating a 3D spatial distribution of the identified first set of tumor lesions, wherein the 3D spatial distribution forms a unique 3D point structure for a patient. ### Claim 2 (depends on 1) The method of claim 1, further comprising: obtaining a second set of one or more three-dimensional (3D) digital images of the target organ; segmenting the second set one or more 3D digital images target organ into one or more second segment volumes of the target organ; identifying and segmenting a second set of one or more tumor lesions in each of the one or more second segment volumes of the target organ; determining for each identified tumor lesion in the second set of one or more tumor lesions a three-dimensional location and a volume; creating a second 3D spatial distribution of the identified second set of lesions; and co-registering the second 3D spatial distribution with the first 3D spatial distribution. ### Claim 3 (depends on 2) The method of claim 2, further comprising: determining a first total tumor burden for each identified tumor lesion in the first set of one or more tumor lesions and determining a second total tumor burden for each identified tumor lesion in the second set of one or more tumor lesions; determining a change from the first total tumor burden to the second total tumor burden; determining a growth rate from the change in the first total tumor burden to the second total tumor burden; and making a clinical management decision based on the change in the first total tumor burden to the second total tumor burden and the growth rate from the change in the first total tumor burden to the second total tumor burden. ### Claim 4 (depends on 3) The method of claim 3, wherein the clinical management decision comprises one of: a disease is progressing rapidly and needs immediate intervention; progression of the disease is slowing down so the disease is becoming stable; a growth rate of lesions that have survived treatment is becoming faster, which is predictive of disease relapse; and the disease is in regression. ### Claim 5 (depends on 2) The method of claim 2, further comprising displaying a graphical user interface on a display, said graphical user interface comprised of at least four panels, said four panels including a user control panel, an information panel, and image display panel, and a lesion list panel. ### Claim 6 A system for tumor lesion identification, segmentation, tracking and analysis comprising: an image capture device; a memory; and a processor in communication with the memory, wherein the processor executes computer-readable instructions stored in the memory that cause the processor to: obtain a first set of one or more three-dimensional (3D) digital image that has been captured by the image capture device, wherein at least a portion of the one or more 3D digital images comprises a target organ; segment the first set of one or more 3D digital images of the target organ into one or more first segment volumes of the target organ; identify and segment a first set of one or more tumor lesions in each of the one or more first segment volumes of the target organ; determine for each identified tumor lesion in the first set of one or more tumor lesions a three-dimensional location and a volume; and create a 3D spatial distribution of the identified first set of tumor lesions, wherein the 3D spatial distribution forms a unique 3D point structure for a patient. ### Claim 7 (depends on 6) The system of claim 6, wherein the processor further executes computer-readable instructions stored in the memory that cause the processor to: obtain from the image capture device a second set of one or more three-dimensional (3D) digital images of the target organ; segment the second set one or more 3D digital images target organ into one or more second segment volumes of the target organ; identify and segment a second set of one or more tumor lesions in each of the one or more second segment volumes of the target organ; determine for each identified tumor lesion in the second set of one or more tumor lesions a three-dimensional location and a volume; create a second 3D spatial distribution of the identified second set of lesions; and co-register the second 3D spatial distribution with the first 3D spatial distribution. ### Claim 8 (depends on 7) The system of claim 7, wherein the processor further executes computer-readable instructions stored in the memory that cause the processor to: determine a first total tumor burden for each identified tumor lesion in the first set of one or more tumor lesions and determine a second total tumor burden for each identified tumor lesion in the second set of one or more tumor lesions; determine a change from the first total tumor burden to the second total tumor burden; and determine a growth rate from the change in the first total tumor burden to the second total tumor burden, wherein a clinical management decision is made based on the change in the first total tumor burden to the second total tumor burden and the growth rate from the change in the first total tumor burden to the second total tumor burden. ### Claim 9 (depends on 8) The system of claim 8, wherein the clinical management decision comprises one of: a disease is progressing rapidly and needs immediate intervention; progression of the disease is slowing down so the disease is becoming stable; a growth rate of lesions that have survived treatment is becoming faster, which is predictive of disease relapse; and the disease is in regression. ### Claim 10 (depends on 7) The system of claim 7, further comprising a display in communication with the processor, wherein the display displays a graphical user interface comprised of at least four panels, said four panels including a user control panel, an information panel, and image display panel, and a lesion list panel. ### Claim 11 A computer program product comprising computer-executable instructions for a method for tumor lesion identification, segmentation, tracking and analysis stored on a computer-readable media, said method comprising: obtaining a first set of one or more three-dimensional (3D) digital images, wherein at least a portion of the one or more 3D digital images comprises a target organ; segmenting the first set of one or more 3D digital images of the target organ into one or more first segment volumes of the target organ; identifying and segmenting a first set of one or more tumor lesions in each of the one or more first segment volumes of the target organ; determining for each identified tumor lesion in the first set of one or more tumor lesions a three-dimensional location and a volume; creating a 3D spatial distribution of the identified first set of tumor lesions, wherein the 3D spatial distribution forms a unique 3D point structure for a patient. ### Claim 12 (depends on 11) The computer program product of claim 11, further comprising: obtaining a second set of one or more three-dimensional (3D) digital images of the target organ; segmenting the second set one or more 3D digital images target organ into one or more second segment volumes of the target organ; identifying and segmenting a second set of one or more tumor lesions in each of the one or more second segment volumes of the target organ; determining for each identified tumor lesion in the second set of one or more tumor lesions a three-dimensional location and a volume; creating a second 3D spatial distribution of the identified second set of lesions; and co-registering the second 3D spatial distribution with the first 3D spatial distribution. ### Claim 13 (depends on 12) The computer program product of claim 12, wherein the method further comprises: determining a first total tumor burden for each identified tumor lesion in the first set of one or more tumor lesions and determining a second total tumor burden for each identified tumor lesion in the second set of one or more tumor lesions; determining a change from the first total tumor burden to the second total tumor burden; determining a growth rate from the change in the first total tumor burden to the second total tumor burden; and making a clinical management decision based on the change in the first total tumor burden to the second total tumor burden and the growth rate from the change in the first total tumor burden to the second total tumor burden. ### Claim 14 (depends on 13) The computer program product of claim 13, wherein the clinical management decision comprises one of: a disease is progressing rapidly and needs immediate intervention; progression of the disease is slowing down so the disease is becoming stable; a growth rate of lesions that have survived treatment is becoming faster, which is predictive of disease relapse; and the disease is in regression. ### Claim 15 (depends on 12) The computer program product of claim 12, wherein the method further comprises displaying a graphical user interface on a display, said graphical user interface comprised of at least four panels, said four panels including a user control panel, an information panel, and image display panel, and a lesion list panel.