# US 12,002,216 B2 · Automatic quality checks for radiotherapy contouring > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,002,216 B2 - Document ID: US12002216B2 - Status: Granted - Kind: B2 - Application number: 18/120,552 - Filed: 2023-03-13 - Published: 2023-07-20 - Granted: 2024-06-04 - Earliest claimed: 2015-09-22 - Assignees: Siemens Healthineers International AG - Applicants: Siemens Healthineers International AG - Inventors: Thomas Albrecht; Peter Hess; Nawal Houhou - Web page: https://patentgrep.com/patent/US12002216B2 ## CPC - G06T 7/143: involving probabilistic approaches, e.g. Markov random field [MRF] modelling - G06F 16/54: Browsing; Visualisation therefor - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06T 7/0002: {Inspection of images, e.g. flaw detection} - G06T 7/001: {using an image reference approach} - G06T 7/0012: {Biomedical image inspection} - G06T 7/12: Edge-based segmentation - G06T 7/13: Edge detection - G06T 7/50: Depth or shape recovery - G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS - G16H 30/40: for processing medical images, e.g. editing - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/10116: X-ray image - G06T 2207/20076: Probabilistic image processing - G06T 2207/20212: Image combination - G06T 2207/30008: Bone - G06T 2207/30048: Heart; Cardiac - G06T 2207/30096: Tumor; Lesion - G06T 2207/30168: Image quality inspection - G06T 2207/30242: Counting objects in image - G06V 2201/03: Recognition of patterns in medical or anatomical images - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Systems, devices, methods, and computer processing products for automatically checking for errors in segmentation (contouring) using heuristic and/or statistical evaluation methods. ## Claims ### Claim 1 A method for evaluating quality of segmentation of an anatomical structure in a radiological image, comprising: receiving a set of rules to be used to evaluate the segmented structure; determining the rules that are appropriate to be used to evaluate the segmented structure; selecting the appropriate rules for execution; determining order of execution of the selected rules; and applying the selected rules in the determined order to check the quality of the segmentation in the image, wherein the segmentation is determined to fail quality check when one or more of the selected rules are violated, wherein the rules include heuristic rules and statistical rules, the heuristic rules include rules that require that the structure be inside the segmentation in an image, that the segmented structure not overlap with another structure in the image, that the segmentation in the image be a single connected contour, that a length, volume, or shape of the structure be in a predefined range, that gross tumor volume structures be completely covered by at least one planning target volume structures, and that clinical target volume structures be completely covered by at least one planning target volume structures, and wherein Boolean operators and pixel/voxel counting or a 3D connectivity labeling process is applied to determine whether the heuristic rules are violated. ### Claim 2 (depends on 1) The method of claim 1, wherein the determining and selection of the rules is based on predefined radiotherapy atlases. ### Claim 3 (depends on 1) The method of claim 1, wherein the determining, selection, and order of execution of the selected rules are done automatically using a determination engine or manually by a user. ### Claim 4 (depends on 1) The method of claim 1, wherein the determining whether the heuristic rules are violated comprises: generating binary images for the segmented structure; combine the binary images using at least one of a plurality of Boolean operators to obtain a resulting binary image; and count pixels/voxels in the resulting binary image, wherein a heuristic rule is determined to be violated if the number of pixels/voxels is not within a predetermined range. ### Claim 5 (depends on 1) The method of claim 1, wherein the applying of the 3D connectivity labeling process includes identifying disconnections in the segmented structure by: determining how many segment components does the segmented structure have; and comparing the number of the segment components with a previously determined number representing the number of connected components delineating the structure, wherein a heuristic rule is violated if the number of segment components is different from the previously determined number of connected components. ### Claim 6 (depends on 1) The method of claim 1, wherein the statistical rules evaluate whether the shape of the segmented structure is within a predetermined range of known shapes for the structure by: generating a statistical shape model for the segmented structure; and determining whether the shape of the segmented structure is within a predetermined range of known shapes for the segmented structure, wherein a segmentation is determined to fail the quality check when the shape of the segment is not within the predetermined range. ### Claim 7 (depends on 6) The method of claim 6, further comprising: generating the shape model based on a probabilistic distribution of shape variations found in the range of known shapes for the segmented structure; and performing a statistical test to check if the shape of the segmented structure is within the predetermined range. ### Claim 8 (depends on 1) The method of claim 1, further comprising: generating an alert signal when it is determined that the segmentation failed the quality check; determining and displaying where and why the segmentation failed the quality check; and allowing a user to correct errors in the segmentation based on the determination. ### Claim 9 (depends on 8) The method of claim 8, further comprising: generating an evaluation report providing an overview of the failed quality check; and allowing a user to respond to the failed quality check in real-time. ### Claim 10 A system for evaluating quality of segmentation of an anatomical structure in a radiological image, comprising: a computer processing system including: a database configured to store a plurality of segmentation evaluation rules; a user input device; and a display device, the computer processing device configured to: determine the evaluation rules appropriate to be used to evaluate the segmented structure; select the evaluation rules for execution; determine order of execution of the selected evaluation rules; and apply the selected evaluation rules in the determined order to evaluate a quality of the segmentation in the image, wherein the segmentation is determined to fail quality check when one or more of the selected evaluation rules are violated, wherein the rules include heuristic rules and statistical rules, the heuristic rules include rules that require that the structure be inside the segmentation in an image, that the segmented structure not overlap with another structure in the image, that the segmentation in the image be a single connected contour, that a length, volume, or shape of the structure be in a predefined range, that gross tumor volume structures be completely covered by at least one planning target volume structures, and that clinical target volume structures be completely covered by at least one planning target volume structures, and wherein Boolean operators and pixel/voxel counting or a 3D connectivity labeling process is applied to determine whether the heuristic rules are violated. ### Claim 11 (depends on 10) The system of claim 10, wherein the computer processing system is configured to automatically determine and select the evaluation rules and the order of execution of the selected evaluation rules using a determination engine. ### Claim 12 (depends on 10) The system of claim 10, wherein the determining, selection, and order of execution of the selected evaluation rules is done manually by a user via the user input device. ### Claim 13 (depends on 10) The system of claim 10, wherein the computer processing system is further configured to: generate an alert signal when it is determined that the segmentation failed the quality check; determine and display where and why the segmentation failed the quality check; and allow the user to correct errors in the segmentation based on the determination. ### Claim 14 (depends on 13) The system of claim 13, wherein the computer processing system is further configured to: generate an evaluation report providing an overview of the failed quality check; and allow the user to respond to the failed quality check in real-time. ### Claim 15 (depends on 10) The system of claim 10, wherein the statistical rules evaluate whether the shape of the segmented structure is within a predetermined range of known shapes for the structure by: generating a statistical shape model for the segmented structure; and determining whether the shape of the segmented structure is within a predetermined range of known shapes for the segmented structure, wherein a segmentation is determined to fail the quality check when the shape of the segment is not within the predetermined range.