# US 2019/0378291 A1 · Adaptive nonlinear optimization of shape parameters for object localization in 3d medical images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0378291 A1 - Document ID: US20190378291A1 - Status: Published application - Kind: A1 - Application number: 16/270,918 - Filed: 2019-02-08 - Published: 2019-12-12 - Earliest claimed: 2018-06-07 - Assignees: Siemens Healthcare GmbH - Applicants: Siemens Healthcare GmbH - Inventors: Mayalen Irene Catherine Etcheverry; Bogdan Georgescu; Sasa Grbic; Dorin Comaniciu; Benjamin L. Odry; Thomas Re; Shivam Kaushik; Bernhard Geiger; Mariappan S. Nadar - Web page: https://patentgrep.com/patent/US20190378291A1 ## CPC - G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80) - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06T 3/20: Linear translation of whole images or parts thereof, e.g. panning - G06T 3/40: Scaling of whole images or parts thereof, e.g. expanding or contracting - G06T 3/60: Rotation of whole images or parts thereof - G16H 30/40: for processing medical images, e.g. editing - G06T 2207/20021: Dividing image into blocks, subimages or windows - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30004: Biomedical image processing - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - 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 ## Abstract System and methods are provided for localizing a target object in a medical image. The medical image is discretized into a plurality of images having different resolutions. For each respective image of the plurality of images, starting from a first image and progressing to a last image with the progression increasing in resolution, a sequence of actions is performed for modifying parameters of a target object in the respective image. The parameters of the target object comprise nonlinear parameters of the target object. The sequence of actions is determined by an artificial intelligence agent trained for a resolution of the respective image to optimize a reward function. The target object is localized in the medical image based on the modified parameters of the target object in the last image. ## Claims ### Claim 1 A method for localizing a target object in a medical image, comprising: discretizing the medical image into a plurality of images having different resolutions; for each respective image of the plurality of images, starting from a first image and progressing to a last image with the progression increasing in resolution, performing a sequence of actions for modifying parameters of the target object in the respective image, the parameters of the target object comprising nonlinear parameters of the target object, wherein the sequence of actions comprises discrete actions determined by an artificial intelligence (AI) agent trained for a resolution of the respective image to optimize a reward function; and localizing the target object in the medical image based on the modified parameters of the target object in the last image. ### Claim 2 (depends on 1) The method of claim 1, wherein the parameters of the target object comprise translation, rotation, and scaling parameters defining a nine dimensional space. ### Claim 3 (depends on 1) The method of claim 1, wherein the AI agent is trained using deep reinforcement learning. ### Claim 4 (depends on 1) The method of claim 1, wherein the sequence of actions comprise a stop action in which the parameters of the target object are unchanged. ### Claim 5 (depends on 1) The method of claim 1, wherein the modified parameters of the target object in the respective image are used as initial parameters for the target object in a next image in the plurality of images. ### Claim 6 (depends on 1) The method of claim 1, wherein performing a sequence of actions for modifying parameters of the target object in the respective image comprises: repeatedly performing an action for modifying the parameters of the target object for a current state in the respective image that optimizes the reward function learned by the AI agent trained for the resolution of the respective image until a stopping condition is satisfied. ### Claim 7 (depends on 6) The method of claim 6, wherein the stopping condition comprises one of a stop action determined by the AI agent, a predetermined number of steps, and consecutive complementary actions. ### Claim 8 (depends on 1) The method of claim 1, wherein the target object is an anatomical landmark. ### Claim 9 An apparatus for localizing a target object in a medical image, comprising: means for discretizing the medical image into a plurality of images having different resolutions; means for, for each respective image of the plurality of images, starting from a first image and progressing to a last image with the progression increasing in resolution, performing a sequence of actions for modifying parameters of the target object in the respective image, the parameters of the target object comprising nonlinear parameters of the target object, wherein the sequence of actions comprises discrete actions determined by an artificial intelligence (AI) agent trained for a resolution of the respective image to optimize a reward function; and means for localizing the target object in the medical image based on the modified parameters of the target object in the last image. ### Claim 10 (depends on 9) The apparatus of claim 9, wherein the parameters of the target object comprise translation, rotation, and scaling parameters defining a nine dimensional space. ### Claim 11 (depends on 9) The apparatus of claim 9, wherein the AI agent is trained using deep reinforcement learning. ### Claim 12 (depends on 9) The apparatus of claim 9, wherein the modified parameters of the target object in the respective image are used as initial parameters for the target object in a next image in the plurality of images. ### Claim 13 (depends on 9) The apparatus of claim 9, wherein the means for performing a sequence of actions for modifying parameters of the target object in the respective image comprises: means for repeatedly performing an action for modifying the parameters of the target object for a current state in the respective image that optimizes the reward function learned by the AI agent trained for the resolution of the respective image until a stopping condition is satisfied. ### Claim 14 (depends on 13) The apparatus of claim 13, wherein the stopping condition comprises one of a stop action determined by the AI agent, a predetermined number of steps, and consecutive complementary actions. ### Claim 15 A non-transitory computer readable medium storing computer program instructions for localizing a target object in a medical image, the computer program instructions when executed by a processor cause the processor to perform operations comprising: discretizing the medical image into a plurality of images having different resolutions; for each respective image of the plurality of images, starting from a first image and progressing to a last image with the progression increasing in resolution, performing a sequence of actions for modifying parameters of the target object in the respective image, the parameters of the target object comprising nonlinear parameters of the target object, wherein the sequence of actions comprises discrete actions determined by an artificial intelligence (AI) agent trained for a resolution of the respective image to optimize a reward function; and localizing the target object in the medical image based on the modified parameters of the target object in the last image. ### Claim 16 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the parameters of the target object comprise translation, rotation, and scaling parameters defining a nine dimensional space. ### Claim 17 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the AI agent is trained using deep reinforcement learning. ### Claim 18 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the sequence of actions comprise a stop action in which the parameters of the target object are unchanged. ### Claim 19 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the modified parameters of the target object in the respective image are used as initial parameters for the target object in a next image in the plurality of images. ### Claim 20 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the target object is an anatomical landmark.