# US 12,183,065 B2 · Bone fracture detection and classification > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,183,065 B2 - Document ID: US12183065B2 - Status: Granted - Kind: B2 - Application number: 18/430,040 - Filed: 2024-02-01 - Published: 2024-05-30 - Granted: 2024-12-31 - Earliest claimed: 2018-11-26 - Assignees: metamorphosis GmbH - Applicants: metamorphosis GmbH - Inventors: Arno Blau - Web page: https://patentgrep.com/patent/US12183065B2 ## CPC - G06V 10/95: {structured as a network, e.g. client-server architectures} - G06T 7/0012: {Biomedical image inspection} - G06T 7/13: Edge detection - G06T 7/75: {involving models} - G06V 10/44: Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components - G06T 2207/10116: X-ray image - G06T 2207/30008: Bone - G06V 2201/033: of skeletal patterns - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Method and apparatus are provided for assisting with bone fracture detection. In particular, image data of a medical image is received in a processing unit from a device, which may be an imaging device, a data detection device or an image storage device. A bone structure is identified in the medical image. A fracture line in the identified bone structure is determined. A bone feature, which may include a portion of an outline of the identified bone structure, a point of the fracture line on an outline of the identified bone structure, a relative displacement of bone parts of the identified bone structure, or a combination thereof is detected. The bone feature may be classified and a corresponding output generated. ## Claims ### Claim 1 A device for assisting with bone fracture detection, the device comprising a processing unit configured to: receive image data of a medical image from a device selected from the group consisting of a C-arm based X-ray device, a diagnostic X-ray device, a computer tomography device, a magnetic resonance device, an ultrasound device, a data detection device or an image storage device; identify a bone structure in the medical image; determine a fracture line in the identified bone structure; and detect a bone feature selected from the group consisting of at least a portion of an outline of the identified bone structure, a point of the fracture line on an outline of the identified bone structure, a relative displacement of bone parts of the identified bone structure, or a combination thereof, wherein the bone feature is detected based on edge detection; and; implement a neural net to generate a corresponding output selected from a group consisting of bone structure identification, fracture line determination, bone outline detection, fracture line point detection, bone part displacement detection, bone fracture classification, or a combination thereof. ### Claim 2 (depends on 1) The device of claim 1, wherein the processing unit is further configured to classify the bone fracture and to generate a corresponding output. ### Claim 3 (depends on 1) The device of claim 1, wherein the neural net implemented in the processing unit comprises a deep neural net (DNN). ### Claim 4 (depends on 1) The device of claim 1, wherein the processing unit is further configured to receive information from a data base that is selected from a group consisting of a bone model, a bone fracture data set including a plurality of bone fracture lines and a likelihood thereof, a bone fragmentation data set include a plurality of bone fragmentations and a likelihood thereof, a classification data set including a plurality of bone fracture classifications, or a combination thereof. ### Claim 5 (depends on 1) The device of claim 1, further comprising a memory unit storing a set of instructions, wherein the memory unit is operably connected with the processing unit to execute the set of instructions for assisting with bone fracture detection. ### Claim 6 A method of assisting with bone fracture detection, the method comprising the steps of receiving image data of a medical image from a device selected from the group consisting of a C-arm based X-ray device, a diagnostic X-ray device, a computer tomography device, a magnetic resonance device, an ultrasound device, a data detection device or an image storage device; identifying a bone structure in the medical image; determining a fracture line at the identified bone structure; detecting a bone feature selected from the group consisting of at least a portion of an outline of the identified bone structure, a point of the fracture line on an outline of the identified bone structure, a relative displacement of bone parts of the identified bone structure, or a combination thereof, wherein the bone feature is detected based on edge detection; and implementing a neural net in a processing unit to generate a corresponding output selected from a group consisting of bone structure identification, fracture line determination, bone outline detection, fracture line point detection, bone part displacement detection, bone fracture classification, or a combination thereof. ### Claim 7 (depends on 6) The method of claim 6, further comprising classifying the bone fracture and generating a corresponding output. ### Claim 8 (depends on 6) The method of claim 6, wherein the neural net implemented in the processing unit further comprises a deep neural net (DNN). ### Claim 9 (depends on 6) The method of claim 6, further comprising receiving information from a data base that is selected from a group consisting of a bone model, a bone fracture data set including a plurality of bone fracture lines and a likelihood thereof, a bone fragmentation data set including a plurality of bone fragmentations and a likelihood thereof, a classification data set including a plurality of bone fracture classifications, or a combination thereof. ### Claim 10 A non-transitory, computer-readable memory storing a set of instruction that, when executed on a processing unit, performs a method of assisting with bone fracture detection comprising: receiving image data of a medical image from a device selected from the group consisting of a C-arm based X-ray device, a diagnostic X-ray device, a computer tomography device, a magnetic resonance device, an ultrasound device, a data detection device or an image storage device; identifying a bone structure in the medical image; determining a fracture line at the identified bone structure; and detecting a bone feature selected from the group consisting of at least a portion of an outline of the identified bone structure, a point of the fracture line on an outline of the identified bone structure, a relative displacement of bone parts of the identified bone structure, or a combination thereof, wherein the bone feature is detected based on edge detection; and implementing a neural net in a processing unit to generate a corresponding output selected from a group consisting of bone structure identification, fracture line determination, bone outline detection, fracture line point detection, bone part displacement detection, bone fracture classification, or a combination thereof. ### Claim 11 (depends on 10) The non-transitory, computer-readable memory of claim 10 wherein the set of instruction that, when executed on a processing unit, performs a method further comprising classifying the bone fracture and generating a corresponding output. ### Claim 12 (depends on 10) The non-transitory, computer-readable memory of claim 10 wherein the set of instruction that, when executed on a processing unit, performs a method further comprising receiving information from a data base that is selected from a group consisting of a bone model, a bone fracture data set including a plurality of bone fracture lines and a likelihood thereof, a bone fragmentation data set including a plurality of bone fragmentations and a likelihood thereof, a classification data set including a plurality of bone fracture classifications, or a combination thereof. ### Claim 13 (depends on 10) The non-transitory, computer-readable memory of claim 10 wherein the neural net implemented in the processing unit further comprises a deep neural net (DNN).