CN113610809A - Fracture detection method, fracture detection device, electronic device, and storage medium - Google Patents
Fracture detection method, fracture detection device, electronic device, and storage medium Download PDFInfo
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Abstract
The disclosure relates to the technical field of artificial intelligence, in particular to the technical field of computer vision and deep learning, and can be applied to scenes such as fracture detection. The specific implementation scheme is as follows: acquiring a bone image to be detected; outputting a preliminary detection result indicating whether the bone image has a fracture part by inputting the bone image into the fracture detection model; when the initial detection result indicates that the fracture part exists, marking the outline of the fracture part in the skeleton image by inputting the skeleton image into the outline identification model; name information of the fracture part is specified based on the outline of the fracture part in the bone image, and a detection report including the name information of the fracture part is generated. The specific information of the fracture part can be intuitively acquired based on the detection report, and the diagnosis efficiency and accuracy of a doctor can be greatly improved.
Description
Technical Field
The disclosure relates to the technical field of artificial intelligence, in particular to the technical field of computer vision and deep learning, and can be applied to scenes such as fracture detection.
Background
The existing method for detecting the fracture condition through the bone image can only judge whether the bone structure shown by the bone image has fracture or not, or can only locate the bone structure to the approximate area of the fracture part, and can not output more detailed information of the fracture part, so that the diagnosis efficiency can not be effectively improved.
Disclosure of Invention
The disclosure provides a fracture detection method, a fracture detection device, an electronic device and a storage medium.
According to a first aspect of the present disclosure, there is provided a fracture detection method, including:
acquiring a bone image to be detected;
outputting a preliminary detection result indicating whether the bone image has a fracture part by inputting the bone image into the fracture detection model;
when the initial detection result indicates that the fracture part exists, marking the outline of the fracture part in the skeleton image by inputting the skeleton image into the outline identification model;
name information of the fracture part is specified based on the outline of the fracture part in the bone image, and a detection report including the name information of the fracture part is generated.
According to a second aspect of the present disclosure, there is provided a fracture detection apparatus comprising:
the image acquisition module is used for acquiring a bone image to be detected;
the preliminary detection module is used for outputting a preliminary detection result indicating whether the bone image has a fracture part or not by inputting the bone image into the fracture detection model;
the outline marking module is used for marking the outline of the fracture part in the skeleton image by inputting the skeleton image into the outline identification model when the initial detection result indicates that the fracture part exists;
and the result output module is used for determining the name information of the fracture part based on the outline of the fracture part in the bone image and generating a detection report containing the name information of the fracture part.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the fracture detection method described above.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to execute the above-described fracture detection method.
According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the fracture detection method described above.
According to a sixth aspect of the present disclosure, a fracture detection device is provided, which includes the electronic device provided by the embodiment of the present disclosure.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
The technical scheme provided by the disclosure has the following beneficial effects:
according to the technical scheme, after the fracture part exists in the skeleton image, the specific position of the fracture part and the name information of the fracture part can be further accurately identified, the detection report containing the name information of the fracture part is generated, the specific information of the fracture part can be intuitively acquired based on the detection report, and the diagnosis efficiency and accuracy of a doctor can be greatly improved.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a diagram illustrating a model architecture to which a fracture detection method can be applied according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart illustrating a fracture detection method provided by an embodiment of the present disclosure;
FIG. 3 illustrates an exemplary structural diagram of a fast-RCNN model provided by an embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating an exemplary structure of a Mask-RCNN model provided by an embodiment of the present disclosure;
FIG. 5 is a schematic flow chart diagram illustrating another fracture detection method provided by the embodiments of the present disclosure;
FIG. 6 illustrates a hand skeleton image provided by an embodiment of the present disclosure;
FIG. 7 is a schematic diagram illustrating a hand skeleton image after removing a type identifier provided by an embodiment of the present disclosure;
FIG. 8 illustrates a schematic hand bone image outlining a fracture site provided by embodiments of the present disclosure;
FIG. 9 is a schematic view of a fracture detection apparatus provided in an embodiment of the present disclosure;
fig. 10 is a second schematic view of a fracture detection apparatus provided in the embodiment of the present disclosure;
fig. 11 shows a schematic block diagram of an example electronic device that may be used to implement the fracture detection methods provided by embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The existing method for detecting the fracture condition through the bone image can only judge whether the bone structure shown by the bone image has fracture or not, or can only locate the bone structure to the approximate area of the fracture part, and can not output more detailed information of the fracture part, so that the diagnosis efficiency can not be effectively improved.
The embodiment of the present disclosure provides a fracture detection method, a fracture detection device, an electronic device, and a storage medium, which aim to solve at least one of the above technical problems in the prior art.
Fig. 1 illustrates a model architecture diagram capable of applying a fracture detection method according to an embodiment of the present disclosure, where the architecture includes a fracture detection model and a contour recognition model, as shown in fig. 1. When a bone image to be detected is obtained, a primary detection result indicating whether the bone image has a fracture part or not can be output by inputting the bone image into a fracture detection model; when the initial detection result indicates that the fracture part exists, marking the outline of the fracture part in the skeleton image by inputting the skeleton image into the outline identification model; name information of the fracture part is specified based on the outline of the fracture part in the bone image, and a detection report including the name information of the fracture part is generated.
Optionally, as shown in fig. 1, the above-mentioned architecture may further include a classification model. The embodiment of the present disclosure may output name information of the fracture site by inputting the bone image labeled with the outline of the fracture site into a classification model.
It can be understood that the models are obtained by training the initial model with a training set related to the fracture image on the basis of the corresponding initial model. The type of the initial model corresponding to each model may be determined according to actual design requirements, for example, the base model of the fracture detection model may be a fast-RCNN model, the base model of the contour recognition model may be a Mask-RCNN model, and the base model of the classification model may be a support vector machine.
Fig. 2 shows a schematic flow chart of a fracture detection method provided by an embodiment of the present disclosure, and as shown in fig. 2, the method mainly includes the following steps:
s210: and acquiring a bone image to be detected.
The bone image is an image of a bone structure including a body part obtained by scanning the body part with a scanning device. For example, the scanning device may be an X-Ray (X-Ray) scanning device, and after a body part is scanned by the X-Ray scanning device, an X-Ray image including an image of a bone structure of the body part may be obtained, and the X-Ray image is a bone image.
In the embodiment of the disclosure, the bone image to be detected can be directly obtained from the scanning device; alternatively, the bone image scanned by the scanning device may be stored in a designated device (e.g., a server), and the bone image to be detected may be acquired from the designated device when the detection step needs to be performed. Of course, the bone image to be detected may be obtained by other ways, which are not listed here.
S220: by inputting the bone image to the fracture detection model, a preliminary detection result indicating whether the bone image has a fracture site is output.
In embodiments of the present disclosure, the fracture detection model may be a binary model. The bone image is input into a fracture detection model, and the fracture detection model can output two primary detection results of the existence of the fracture part and the absence of the fracture part.
In the disclosed embodiment, when the initial detection result indicates that the fracture site exists, the step S230 may be continuously performed; when the preliminary detection result indicates that no fracture part exists, a detection report indicating that no fracture part exists can be continuously generated, and the fracture detection process is ended.
S230: when the initial detection result indicates the presence of a fracture site, the outline of the fracture site is marked in the bone image by inputting the bone image to the outline recognition model.
In the embodiment of the present disclosure, the contour recognition model may be any model capable of recognizing the contour of the fracture site based on the preset characteristic fracture image, and the bone image is input into the contour recognition model, and the contour recognition model may determine the fracture site from the bone structure included in the bone image and mark the contour of the fracture site.
S240: name information of the fracture part is determined based on the outline of the fracture part in the bone image.
In the embodiment of the present disclosure, a mapping relationship between the outline of each part in the bone structure and the corresponding name information may be established in advance, the outline of the fracture part is marked in the bone image, and the name information of the fracture part may be determined based on the feature of the outline.
Optionally, the bone image labeled with the outline of the fracture part may be input into the classification model, and the name information of the fracture part may be determined by using the classification model. The classification model may be a Support Vector Machine (SVM) or other types of classification models, which is not limited in this disclosure.
S250: a detection report including name information of the fracture site is generated.
The embodiment of the disclosure can generate a detection report based on a preset report template, and the detection report includes name information of the fracture part. Here, the detection report may be a visual text report, or may also be other forms of reports, such as a video report, a graph report, and the like, which is not limited in this disclosure.
According to the fracture detection method provided by the embodiment of the disclosure, after the fracture part exists in the skeleton image, the specific position of the fracture part and the name information of the fracture part can be further accurately identified, the detection report containing the name information of the fracture part is generated, the specific information of the fracture part can be intuitively acquired based on the detection report, and the diagnosis efficiency and accuracy of a doctor can be greatly improved.
In the embodiment of the present disclosure, the bone image contains the type identifier of the bone. Prior to step S220, a bone type in the bone image may be determined based on the type identification. When determining name information of a fracture site based on an outline of the fracture site in a bone image, at least one candidate name information may be determined based on a bone type; name information of the fracture site is determined from the at least one candidate name information based on an outline of the fracture site in the bone image.
In the embodiment of the disclosure, after determining the bone type in the bone image based on the type identifier, the type identifier in the bone image may be removed, and a preliminary detection result indicating whether the bone image has a fracture part may be output by inputting the bone image with the removed type identifier to the fracture detection model.
In the embodiment of the present disclosure, the bone type may also be used as one item of information in the detection report, so that in the case that the bone type in the bone image has been determined based on the type identifier, a detection report including the bone type and the name information of the fracture part may be generated, and the information in the detection report is enriched, which helps to improve the diagnosis efficiency and accuracy.
In the embodiment of the present disclosure, the specific model category of the above-mentioned two classification models may depend on the actual design requirement, for example, the fracture detection model may be a fast-RCNN model. Fig. 3 illustrates an exemplary structural diagram of a fast-RCNN model provided by an embodiment of the present disclosure, and as shown in fig. 3, the fast-RCNN model includes convolutional layers (Conv layers), Region extraction Networks (RPNs), Region of interest Pooling layers (ROI Pooling), and classifiers (classifiers). The convolutional layer may be composed of a set of underlying conv + relu + posing for extracting feature maps (feature maps) of bone images, which may be shared for subsequent region extraction networks and classifiers. The region extraction network can determine a plurality of candidate regions (regions) based on the feature map, judge that anchors belong to positive or negative through a softmax function, and then correct the anchors by utilizing bounding box regression to obtain accurate target regions (regions). The region-of-interest pooling layer can collect the input feature map and the target region, determine a target region feature map (positive feature maps) based on the feature map and the target region, input the target region feature map into the classifier, and the classifier calculates the category of the target region based on the target region feature map, namely, judges whether the target region has a fracture part, so as to determine whether the bone image has the fracture part.
Optionally, the contour recognition model may be a Mask-RCNN model, fig. 4 illustrates an exemplary structural schematic diagram of the Mask-RCNN model provided by the embodiment of the present disclosure, and as illustrated in fig. 4, the Mask-RCNN model includes a region of interest aggregate (ROI Align) and a plurality of convolution layers (Conv), a bone image is input into the region of interest aggregate of the Mask-RCNN model, and then the bone image is sequentially processed by the plurality of convolution layers, so that a fracture site may be determined from a bone structure included in the bone image, and a contour of the fracture site is marked. It should be noted that this step is marked to make the contour of the frontal fracture substantially identical to the external shape of the fracture, and the contour can achieve the precision of pixel level.
Fig. 5 is a schematic flow chart of another fracture detection method provided in the embodiment of the present disclosure, and as shown in fig. 5, the method may mainly include the following steps:
s510: and acquiring a bone image to be detected.
The bone image is an image of a bone structure including a body part obtained by scanning the body part with a scanning device. Taking the scanned body part as an example of a hand, fig. 6 shows a hand skeleton image provided by the embodiment of the present disclosure, and after the hand is scanned by the scanning device, the hand skeleton image shown in fig. 6 and including the hand skeleton structure can be obtained.
It should be noted that, for the specific description of acquiring the bone image to be detected in step S510, reference may be made to the description in step S210, and details are not repeated here.
S520: the bone type in the bone image is determined based on the type identification.
In an embodiment of the present disclosure, the type identifier in the bone image may be generated by the scanning device. In particular, when scanning a body part by a scanning device, a user may enter information of the scanned body part at the scanning device, which generates a corresponding type identification based on the information of the scanned body part.
S530: the type identification in the bone image is cleared.
The disclosed embodiments may remove the type identifier in the bone image through a responsive algorithm. Optionally, the embodiment of the present disclosure may use a gaussian fuzzy mathematical model to remove the type identifier in the bone image, specifically, obtain a pixel color masking the icon according to a difference between the type identifier and a peripheral color thereof, and adjust the pixel color of the type identifier to make the color of the type identifier consistent with the peripheral color thereof, thereby achieving an effect of removing the type identifier.
In the disclosed embodiment, the size of the bone image may also be adjusted, for example, the size of the bone image may be adjusted to 224x 224. The adjusted size of the skeleton image can be determined based on the size of a sample image in a training set used in the training process of the fracture detection model, and the purpose is to adjust the size of the skeleton image to be detected to be uniform with the size of the sample image in the training process of the fracture detection model and enhance the robustness of the fracture detection model.
S540: and outputting a preliminary detection result indicating whether the bone image has the fracture part or not by inputting the bone image with the removed type identifier into the fracture detection model.
After the type identifier in the bone image is removed, part of noise in the bone image is removed, adverse effects of the type identifier on the judgment process of the fracture detection model are avoided, and the accuracy of a primary detection result output by the fracture detection model is improved. It should be noted that, for the specific description of acquiring the bone image to be detected in step S540, reference may be made to the description in step S220, and details are not repeated here.
S550: when the initial detection result indicates the presence of a fracture site, the outline of the fracture site is marked in the bone image by inputting the bone image to the outline recognition model.
In the embodiment of the present disclosure, for specific description of acquiring the bone image to be detected in step S550, reference may be made to the description in step S230, and details are not repeated here.
S560: at least one candidate name information is determined based on the bone type.
It can be understood that the at least one candidate name information is determined based on the bone type, so that the search range of the name information of the fracture part in the subsequent step can be narrowed, and the search efficiency can be improved. Embodiments of the present disclosure may pre-store name information for various parts in each bone type. Taking the left-hand skeleton as an example, each part of the left-hand skeleton includes a first joint of the thumb, a second joint of the thumb, a first joint of the index finger, a first joint of the wrist and the like.
S570: name information of the fracture site is determined from the at least one candidate name information based on an outline of the fracture site in the bone image.
Optionally, the embodiment of the present disclosure may determine a matching probability between the fracture site and each candidate name information in the at least one candidate name information by inputting the bone image labeled with the outline of the fracture site into the classification model; and determining the candidate name information with the highest matching probability as the name information of the fracture part. The classification model may be a Support Vector Machine (SVM) or other types of classification models, which is not limited in this disclosure.
Here, the matching probability may represent an accuracy rate of determining the name information of the fracture site, and the greater the matching probability, the higher the accuracy rate of determining the name information of the fracture site.
S580: a detection report including the bone type, the name information of the fracture part, and the matching probability corresponding to the name information is generated.
In the embodiment of the present disclosure, the matching probability corresponding to the bone type and the name information may also be used as one item of information in the detection report, and therefore, in the case where the bone type in the bone image has been determined based on the type identifier and the matching probability corresponding to the name information has been determined, a detection report including the bone type, the name information of the fracture site, and the matching probability corresponding to the name information may be generated, thereby enriching the information in the detection report, contributing to improving the diagnosis efficiency and accuracy, and intuitively representing the reliability of the name information when the detection report shows the matching probability corresponding to the name information.
In embodiments of the present disclosure, the fracture detection model may be a three-classification model. The bone image is input into a fracture detection model, the fracture detection model can output two primary detection results of the existence of the fracture part and the nonexistence of the fracture part, and when the primary detection result indicates that the fracture part exists, the primary detection result also comprises the fracture type of the fracture part, wherein the fracture type can comprise implantation, contusion and other types. The fracture type can also be used as one item of information in the detection report, and under the condition that the fracture type is determined, the detection report containing the fracture type and the name information of the fracture part can be generated, so that the information in the detection report is enriched, and the diagnosis efficiency and the diagnosis accuracy are improved.
Optionally, the type identifier in step S520 may be in the form of characters, letters, or patterns. As shown in fig. 6, the letter "L" in fig. 6 is a type identifier, and the type identifier indicates that the bone structure in the bone image is a left-hand bone. The embodiment of the disclosure can input the determined bone image with the type identifier into the fracture detection model, and the fracture detection model can identify the type identifier in the bone image, so as to determine the bone type in the bone image.
Fig. 7 shows a hand skeleton image schematic diagram after removing a type identifier according to an embodiment of the present disclosure. The letter "L" in FIG. 6 can be removed in removing the type identifier in the bone image, and it can be seen that FIG. 7 has removed the letter "L" as compared to FIG. 6.
Fig. 8 is a schematic diagram illustrating a hand skeleton image outlining a fracture site according to an embodiment of the present disclosure. When the outline of the fracture site is marked in the bone image, the outline of the fracture site may be marked with a designated color, as shown in fig. 8, the fracture site is the first joint of the thumb, and the outline recognition model may mark the outline of the first joint of the thumb with a dark color.
Based on the same principle as the fracture detection method described above, fig. 9 shows one of the schematic diagrams of a fracture detection apparatus provided by the embodiment of the present disclosure. As shown in fig. 9, the fracture detection apparatus 900 includes an image acquisition module 910, a preliminary detection module 920, a contour labeling module 930, and a result output module 940.
The image acquiring module 910 is used for acquiring an image of a bone to be detected.
The preliminary detection module 920 is configured to output a preliminary detection result indicating whether a fracture site exists in the bone image by inputting the bone image into the fracture detection model.
The contour labeling module 930 is configured to label a contour of the fracture site in the bone image by inputting the bone image into the contour recognition model when the initial detection result indicates that the fracture site exists.
The result output module 940 is configured to determine name information of the fracture site based on the contour of the fracture site in the bone image, and generate a detection report including the name information of the fracture site.
According to the fracture detection device provided by the embodiment of the disclosure, after the fracture part exists in the skeleton image, the specific position of the fracture part and the name information of the fracture part can be further accurately identified, the detection report containing the name information of the fracture part is generated, the specific information of the fracture part can be intuitively acquired based on the detection report, and the diagnosis efficiency and accuracy of a doctor can be greatly improved.
In the embodiment of the present disclosure, the bone image contains the type identifier of the bone. Fig. 10 shows a second schematic diagram of a fracture detection apparatus provided in an embodiment of the present disclosure, and as shown in fig. 10, the fracture detection apparatus 900 further includes a type identification module 950, where the type identification module 950 is configured to: determining a bone type in the bone image based on the type identification before outputting a preliminary detection result indicating whether the bone image has a fracture part by inputting the bone image to the fracture detection model.
When the result output module 940 is configured to determine the name information of the fracture part based on the contour of the fracture part in the bone image, specifically: determining at least one candidate name information based on the bone type; name information of the fracture site is determined from the at least one candidate name information based on an outline of the fracture site in the bone image.
In an embodiment of the present disclosure, the preliminary detection module 920 is further configured to: removing the type identification in the bone image; and outputting a preliminary detection result indicating whether the bone image has the fracture part or not by inputting the bone image with the removed type identifier into the fracture detection model.
In the embodiment of the present disclosure, the result output module 940, when being used to generate a detection report containing name information of a fracture site, is specifically configured to: a detection report is generated that includes information on the type of bone and the name of the fracture site.
In an embodiment of the present disclosure, the result output module 940, when configured to determine the name information of the fracture part from the at least one candidate name information based on the contour of the fracture part in the bone image, is specifically configured to: determining the matching probability of the fracture part and each candidate name information in at least one candidate name information by inputting the skeleton image marked with the outline of the fracture part into a classification model; and determining the candidate name information with the highest matching probability as the name information of the fracture part.
In the embodiment of the present disclosure, the result output module 940, when being used to generate a detection report containing name information of a fracture site, is specifically configured to: a detection report including the bone type, the name information of the fracture part, and the matching probability corresponding to the name information is generated.
In the disclosed embodiment, when the preliminary test result indicates the presence of a fracture site, the preliminary test result further includes a fracture type of the fracture site; when the result output module 940 is used to generate a detection report including name information of the fracture site, it is specifically configured to: a detection report is generated that includes information on the fracture type and name of the fracture site.
It can be understood that the above modules of the fracture detection device in the embodiment of the present disclosure have functions of implementing the corresponding steps of the above fracture detection method. The function can be realized by hardware, and can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the functions described above. The modules can be software and/or hardware, and each module can be implemented independently or by integrating a plurality of modules. For the functional description of each module of the fracture detection device, reference may be made to the corresponding description of the fracture detection method, which is not described herein again.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
Fig. 11 shows a schematic block diagram of an example electronic device that may be used to implement the fracture detection methods provided by embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 11, the electronic device 1100 includes a computing unit 1101, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)1102 or a computer program loaded from a storage unit 1108 into a Random Access Memory (RAM) 1103. In the RAM1103, various programs and data necessary for the operation of the electronic device 1100 may also be stored. The calculation unit 1101, the ROM1102, and the RAM1103 are connected to each other by a bus 1104. An input/output (I/O) interface 1105 is also connected to bus 1104.
A number of components in electronic device 1100 connect to I/O interface 1105, including: an input unit 1106 such as a keyboard, a mouse, and the like; an output unit 1107 such as various types of displays, speakers, and the like; a storage unit 1108 such as a magnetic disk, optical disk, or the like; and a communication unit 1109 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 1109 allows the electronic device 1100 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 1101 can be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The calculation unit 1101 performs the respective methods and processes described above, such as the fracture detection method. For example, in some embodiments, the fracture detection method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 1108. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 1100 via the ROM1102 and/or the communication unit 1109. When the computer program is loaded into RAM1103 and executed by the computing unit 1101, one or more steps of the fracture detection method described above may be performed. Alternatively, in other embodiments, the computing unit 1101 may be configured to perform the fracture detection method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
The present disclosure also provides a fracture detection device including the electronic device 1100 shown in fig. 11, according to an embodiment of the present disclosure.
Optionally, the fracture detection device further comprises a scanning device, which is communicatively coupled to the electronic device 1100. After the scanning device scans the body part, the obtained image containing the bone structure image of the body part is input to the electronic device 1100 as a bone image to be detected, and the electronic device 1100 outputs a detection report corresponding to the bone image.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (18)
1. A method of detecting a fracture, comprising:
acquiring a bone image to be detected;
outputting a preliminary detection result indicating whether a fracture site exists in the bone image by inputting the bone image to a fracture detection model;
when the preliminary detection result indicates that a fracture part exists, marking the outline of the fracture part in the bone image by inputting the bone image to an outline recognition model;
and determining the name information of the fracture part based on the outline of the fracture part in the bone image, and generating a detection report containing the name information of the fracture part.
2. The method of claim 1, wherein the bone image comprises a type identifier of a bone;
before the outputting a preliminary detection result indicating whether the bone image has a fracture part by inputting the bone image to a fracture detection model, the method further comprises:
determining a bone type in the bone image based on the type identification;
the determining the name information of the fracture part based on the outline of the fracture part in the bone image comprises:
determining at least one candidate name information based on the bone type;
determining name information of the fracture site from the at least one candidate name information based on an outline of the fracture site in the bone image.
3. The method of claim 2, wherein after determining a bone type in the bone image based on the type identification, further comprising:
clearing the type identification in the bone image;
outputting a preliminary detection result indicating whether the bone image has a fracture part by inputting the bone image from which the type identifier has been removed to a fracture detection model.
4. The method of claim 2, wherein the generating a detection report containing name information of the fracture site comprises: generating a detection report including the bone type and name information of the fracture site.
5. The method of claim 2, wherein said determining name information for the fracture site from the at least one candidate name information based on the contour of the fracture site in the bone image comprises:
determining a matching probability of the fracture part and each candidate name information in the at least one candidate name information by inputting the bone image marked with the outline of the fracture part into a classification model;
and determining the candidate name information with the highest matching probability as the name information of the fracture part.
6. The method of claim 5, wherein the generating a detection report containing name information of the fracture site comprises: and generating a detection report containing the bone type, the name information of the fracture part and the matching probability corresponding to the name information.
7. The method of claim 1, when the preliminary test result indicates the presence of a fracture site, the preliminary test result further including a fracture type of the fracture site;
the generating of the detection report including the name information of the fracture part includes: generating a detection report containing information on the fracture type and the name of the fracture part.
8. A fracture detection apparatus, comprising:
the image acquisition module is used for acquiring a bone image to be detected;
the preliminary detection module is used for outputting a preliminary detection result indicating whether the bone image has a fracture part or not by inputting the bone image into a fracture detection model;
a contour labeling module for labeling a contour of the fracture part in the bone image by inputting the bone image to a contour recognition model when the preliminary detection result indicates the existence of the fracture part;
and the result output module is used for determining the name information of the fracture part based on the outline of the fracture part in the bone image and generating a detection report containing the name information of the fracture part.
9. The apparatus of claim 8, wherein the bone image comprises a type identifier of a bone;
the apparatus further comprises a type identification module to: determining a bone type in the bone image based on the type identification before outputting a preliminary detection result indicating whether the bone image has a fracture part or not by inputting the bone image to a fracture detection model;
the result output module, when configured to determine the name information of the fracture site based on the contour of the fracture site in the bone image, is specifically configured to: determining at least one candidate name information based on the bone type; determining name information of the fracture site from the at least one candidate name information based on an outline of the fracture site in the bone image.
10. The apparatus of claim 9, wherein the preliminary detection module is further configured to:
clearing the type identification in the bone image;
outputting a preliminary detection result indicating whether the bone image has a fracture part by inputting the bone image from which the type identifier has been removed to a fracture detection model.
11. The apparatus according to claim 9, wherein the result output module, when configured to generate a detection report containing name information of the fracture site, is specifically configured to: generating a detection report including the bone type and name information of the fracture site.
12. The apparatus of claim 9, wherein the result output module, when configured to determine the name information of the fracture site from the at least one candidate name information based on the contour of the fracture site in the bone image, is specifically configured to:
determining a matching probability of the fracture part and each candidate name information in the at least one candidate name information by inputting the bone image marked with the outline of the fracture part into a classification model;
and determining the candidate name information with the highest matching probability as the name information of the fracture part.
13. The apparatus according to claim 12, wherein the result output module, when configured to generate a detection report containing name information of the fracture site, is specifically configured to: and generating a detection report containing the bone type, the name information of the fracture part and the matching probability corresponding to the name information.
14. The device of claim 8, when the preliminary test result indicates the presence of a fracture site, the preliminary test result further including a fracture type of the fracture site;
when the result output module is used for generating a detection report containing the name information of the fracture part, the result output module is specifically used for: generating a detection report containing information on the fracture type and the name of the fracture part.
15. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
17. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-7.
18. A fracture detection apparatus comprising the electronic apparatus of claim 15.
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