CN111528889A - Analysis method and device for craniomaxillary surface state and electronic equipment - Google Patents
Analysis method and device for craniomaxillary surface state and electronic equipment Download PDFInfo
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Abstract
The invention discloses a method and a device for analyzing a craniomaxillary surface state and electronic equipment. Wherein, the analysis method comprises the following steps: acquiring a target sequence image of the head craniomaxillofacial of a target user; synthesizing the target sequence image into a Computed Tomography (CT) image to obtain a synthesized CT image; establishing a reference coordinate system in the target sequence image and the synthesized CT image, wherein the reference coordinate system accords with the head integration and modularization characteristics of a user; three-dimensional analysis is carried out on the craniomaxillofacial soft tissue in the target sequence image by taking the reference coordinate system as reference so as to obtain the craniomaxillofacial soft tissue information of the head; performing three-dimensional analysis on the craniomaxillofacial hard tissue in the synthetic CT image by taking the reference coordinate system as reference so as to obtain the head craniomaxillofacial hard tissue information; and analyzing whether the head craniomaxillofacial of the target user is abnormally deformed or not based on the soft tissue information and the hard tissue information of the head craniomaxillofacial.
Description
Technical Field
The invention relates to the technical field of head shadow analysis, in particular to a method and a device for analyzing a craniomaxillofacial state and electronic equipment.
Background
In the related art, in terms of clinical images, especially in terms of CBCT (Cone beam computed tomography), the technology of easily acquiring three-dimensional data reflecting cranio-maxillofacial information of a user through a computer terminal is continuously advanced, and in the stereo cephalogram measurement process, establishment of a three-dimensional coordinate system and a reference plane is important. In the current technical scheme, when determining the median sagittal plane of the craniomaxillofacial surface, 3 anatomical marking points located on the midline of the craniomaxillofacial surface are usually used for constructing the median sagittal plane, namely, the orbital-ear plane is firstly constructed as an axial plane (horizontal plane), and then the anatomical marking points on the midline of two faces are selected to be the median sagittal plane vertical to the orbital-ear plane, but the method has a great defect, namely, because of the defect of CBCT in soft tissue display, most of the positioning of the craniomaxillofacial median sagittal plane is completed on the skull, because the skull is in an irregular shape, the error and randomness exist in constructing the median sagittal plane by using the 3 anatomical marking points located on the midline of the craniomaxillofacial surface, and in addition, the stability of the marking points is poor in serious craniomaxillofacial deformity.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a craniomaxillofacial state analysis method and device and electronic equipment, and aims to at least solve the technical problem that in the related technology, due to the fact that the irregularity of a skull causes the defect that the error is large in the determination process of the midsagittal plane of the craniomaxillofacial state, the complete craniomaxillofacial state cannot be acquired.
According to an aspect of an embodiment of the present invention, there is provided a craniomaxillary surface condition analysis method including: acquiring a target sequence image of the head craniomaxillofacial of a target user, wherein the target sequence image is a soft tissue sequence image or a black bone sequence image, and the image type of the target sequence image is a Magnetic Resonance Image (MRI); synthesizing the target sequence image into a Computed Tomography (CT) image to obtain a synthesized CT image; establishing a reference coordinate system in the target sequence image and the synthesized CT image, wherein the reference coordinate system accords with the head integration and modularization characteristics of a user; performing three-dimensional analysis on the craniomaxillofacial soft tissue in the target sequence image by taking the reference coordinate system as a reference so as to obtain the craniomaxillofacial soft tissue information of the head; performing three-dimensional analysis on the craniomaxillofacial hard tissue in the synthetic CT image by taking the reference coordinate system as a reference so as to obtain the head craniomaxillofacial hard tissue information; and analyzing whether the head craniomaxillofacial of the target user is abnormally deformed or not based on the soft tissue information and the hard tissue information of the head craniomaxillofacial.
Optionally, the step of establishing a reference coordinate system in the target sequence image and the synthesized CT image includes: selecting a brain midline meeting the symmetrical structure of the two sides of the head of the user, or positioning the brain midline of the head structure of the virtual anatomic user; defining a median sagittal plane from the brain midline; determining a projection point of a user head target identification point on the median sagittal plane according to predefined user head modular classification data, and taking the projection point as a coordinate origin; taking the coordinate origin and the nasal root point of the head of the user as a plane perpendicular to a median sagittal plane as a horizontal plane; and constructing the reference coordinate system in the target sequence image and the synthetic CT image based on the coordinate origin and the horizontal plane.
Optionally, the step of obtaining a target sequence of images of a head craniomaxillofacial of the target user comprises: after detecting that the supine position of the target user meets a preset supine condition, detecting the head direction and the head position of the target user; analyzing whether the oral cavity opening and closing state of the target user meets a preset opening and closing state or not based on the head direction and the head position of the target user; under the condition that the oral cavity opening and closing state of the target user is determined to meet the preset opening and closing state, performing MRI (magnetic resonance imaging) soft tissue conventional sequence scanning on the head of the target user by adopting a gradient echo sequence to obtain a head craniomaxillofacial soft tissue sequence image; or, under the condition that the oral cavity opening and closing state of the target user meets the preset opening and closing state, performing MRI scanning on the head of the target user by adopting a low flip angle black bone sequence to obtain a head craniomaxillofacial black bone sequence image.
Optionally, the step of synthesizing the target sequence images into computed tomography CT images includes: converting the target sequence image into a head sequence image by adopting a deep learning unpaired data mode; and automatically coding the head sequence image to obtain a CT image.
Optionally, after synthesizing the target sequence images into a computed tomography CT image, the analysis method further comprises: converting the synthesized CT image into an MRI image by adopting a deep learning unpaired data mode; and analyzing the difference between the synthetic CT and the actual CT through a preset image analysis model.
Optionally, three-dimensional analysis of craniomaxillofacial soft tissue in the target sequence image to obtain information of craniomaxillofacial soft tissue includes: segmenting muscle morphology of the face of the user and determining muscle volume of the face of the user; analyzing muscle symmetry of the user's face based on the muscle morphology, muscle volume, and facial fat distribution range; determining soft tissue information of a cranio-maxillofacial area of a head based on muscle symmetry, muscle morphology, muscle volume, and joint parameters of the user's face.
According to another aspect of the embodiments of the present invention, there is also provided an analysis apparatus of craniomaxillofacial conditions, including: the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a target sequence image of the head craniomaxillofacial of a target user, the target sequence image is a soft tissue sequence image or a black bone sequence image, and the image type of the target sequence image is MRI (magnetic resonance imaging); the synthesizing unit is used for synthesizing the target sequence image into a Computed Tomography (CT) image to obtain a synthesized CT image; the system comprises an establishing unit, a calculating unit and a calculating unit, wherein the establishing unit is used for establishing a reference coordinate system in a target sequence image and a synthesized CT image, and the reference coordinate system accords with the head integration and modularization characteristics of a user; the first analysis unit is used for carrying out three-dimensional analysis on the craniomaxillofacial soft tissue in the target sequence image by taking the reference coordinate system as a reference so as to obtain the craniomaxillofacial soft tissue information of the head; the second analysis unit is used for carrying out three-dimensional analysis on the craniomaxillofacial hard tissue in the synthesized CT image by taking the reference coordinate system as a reference so as to obtain the head craniomaxillofacial hard tissue information; and the third analysis unit is used for analyzing whether the head craniomaxillofacial of the target user is abnormally deformed or not based on the soft tissue information and the hard tissue information of the head craniomaxillofacial.
Optionally, the establishing unit includes: the selection module is used for selecting the brain midline which meets the symmetrical structure of the two sides of the head of the user, or positioning the brain midline of the head structure of the virtual anatomic user; a first determination module to define a median sagittal plane from the brain midline; the second determination module is used for determining a projection point of the user head target identification point on the median sagittal plane according to predefined user head modularization classification data, and taking the projection point as a coordinate origin; a third determining module, configured to use the coordinate origin and a plane perpendicular to a midsagittal plane and at the nasion point of the head of the user as a horizontal plane; and the construction module is used for constructing the reference coordinate system in the target sequence image and the synthetic CT image based on the coordinate origin and the horizontal plane.
Optionally, the obtaining unit includes: the first detection module is used for detecting the head direction and the head position of the target user after detecting that the supine position of the target user meets a preset supine condition; the first analysis module is used for analyzing whether the oral cavity opening and closing state of the target user meets a preset opening and closing state or not based on the head direction and the head position of the target user; the first scanning module is used for carrying out MRI (magnetic resonance imaging) soft tissue conventional sequence scanning on the head of the target user by adopting a gradient echo sequence under the condition that the oral cavity opening and closing state of the target user is determined to meet a preset opening and closing state so as to obtain a head craniomaxillofacial soft tissue sequence image; or, the second scanning module is configured to perform MRI scanning on the head of the target user by using a low flip angle black bone sequence under the condition that it is determined that the oral cavity opening and closing state of the target user meets a preset opening and closing state, so as to obtain a head craniomaxillofacial black bone sequence image.
Optionally, the synthesis unit comprises: the first conversion module is used for converting the target sequence image into a head sequence image in a deep learning unpaired data mode; and the coding module is used for automatically coding the head sequence image to obtain a CT image.
Optionally, the analyzing device for craniomaxillofacial conditions further comprises: the second conversion module is used for converting the synthesized CT image into an MRI image by adopting a depth learning unpaired data mode after the target sequence image is synthesized into a computed tomography CT image; and the first analysis module is used for analyzing the difference between the synthetic CT and the actual CT through a preset image analysis model.
Optionally, the first analysis unit comprises: the segmentation module is used for segmenting the muscle form of the face of the user and determining the muscle volume of the face of the user; a second analysis module for analyzing muscle symmetry of the user's face based on the muscle morphology, muscle volume, and facial fat distribution range; a fourth determination module for determining soft tissue information of the craniomaxillofacial area of the head based on the muscle symmetry, the muscle morphology, the muscle volume, and the joint parameters of the face of the user.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the method of analyzing craniomaxillary conditions of any of the above via execution of the executable instructions.
According to another aspect of the embodiments of the present invention, there is also provided a storage medium including a stored program, wherein the apparatus on which the storage medium is stored is controlled to perform the analysis method of craniomaxillary surface condition according to any one of the above-mentioned methods when the program is executed.
In the embodiment of the invention, when whether the craniomaxillofacial deformation of a user is abnormal is analyzed, a target sequence image of the head craniomaxillofacial surface of the target user is firstly obtained, the target sequence image is synthesized into a computed tomography CT image to obtain a synthesized CT image, a reference coordinate system is established in the target sequence image and the synthesized CT image, the craniomaxillofacial soft tissue is subjected to three-dimensional analysis in the target sequence image by taking the reference coordinate system as reference to obtain the head craniomaxillofacial soft tissue information, the craniomaxillofacial hard tissue is subjected to three-dimensional analysis in the synthesized CT image by taking the reference coordinate system as reference to obtain the head craniomaxillofacial hard tissue information, and whether the head craniomaxillofacial abnormal deformation of the target user is generated is analyzed based on the head craniomaxillofacial soft tissue information and the hard tissue information. In the embodiment, a reference coordinate system (based on human head integration and modularization characteristics) can be established through a sequence image of the head craniomaxillofacial of a user, the soft and hard tissues of the craniomaxillofacial are analyzed by taking the reference coordinate system as a reference, whether the head craniomaxillofacial is abnormally deformed or not is determined, the craniomaxillofacial soft and hard tissues are analyzed comprehensively and accurately, the craniomaxillofacial analysis work is automatically completed, the craniomaxillofacial state analysis efficiency is improved, and the technical problem that the comprehensive craniomaxillofacial state cannot be obtained due to the fact that the error is large in the determination process of the central sagittal plane of the craniomaxillofacial caused by the irregularity of the craniomaxillofacial is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of an alternative method of analyzing craniomaxillary surface condition in accordance with an embodiment of the present invention;
FIG. 2 is a schematic view of an alternative craniomaxillary surface condition analysis device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
To facilitate understanding of the invention by those skilled in the art, some terms or nouns referred to in the embodiments of the invention are explained below:
CT, Computed Tomography, and Computed Tomography utilize precisely collimated beams, rays, ultrasound, etc. to scan one section after another around a certain part of the body along with a highly sensitive detector.
CBCT, Cone beam computed tomography, also called oral CT, is a Cone beam projection computerized tomography apparatus for realizing data tomographic reconstruction and obtaining three-dimensional oral images.
MRI, magnetic Resonance Imaging, uses multi-sequence, direct multi-aspect display of images of the site under examination.
MRI-only, single nuclear magnetism, uses MRI as the only original data source, does not need CT scanning, but utilizes original MRI and its synthetic virtual CT image, analyzes the user's brain craniomaxillofacial state.
In the prior art, in the three-dimensional cephalogram measurement based on CBCT, the determination scheme of the median sagittal plane has the problems of large error, strong randomness and poor stability (poor stability of a mark point in severe craniomaxillofacial deformity). Aiming at the problems, the embodiment of the invention adopts a large number of experiments to determine that the brain-cranium-jaw-face and nerve-muscle-skeleton of a human body are an integrated evolutionary development system and an interaction system, and the development of the head of the human body is an integrated complex process in which multiple genes participate. The induction and regulation of facial morphology by signal molecules from the human brain is variously confirmed, and the brain is synchronized with the development of a nerve cranium or a facial cranium and is indirectly connected with facial muscles. In this application, it was found that there is an objective, unique modular partition inside the human head that is distinct from other primates, with closer intra-modular connections and relatively loose connections between different modules, e.g., the skull originating from different germ layers along the anterior-posterior axis (cranio-caudal axis), with cells of the neural crest (ectoderm) in the anterior (head) of the skull, the mesoderm in the posterior (tail), with the boundaries between the two located at the posterior sagittal suture, and at the ventral side near the pituitary fossa. The presence of a module-specific partition of the human head can be determined using anatomical network analysis methods: the anterior skull and the middle upper face form the same evolutionary development module, the mandible and the posterior skull form another anatomical and functional module, the face above the eyebrow forms a single neuromuscular module, and the middle face and the lower part are muscle modules on the left side and the right side.
Aiming at the problems in the process of cephalometric image measurement and structural characteristics of human brain in the prior art, a breakthrough is searched from the two aspects of head structure and image technology, the biological law of derivation of human head structure is deeply learned, and the method is inspired by new ideas in other fields, and on the basis of advanced research and pre-experiment, a cranio-maxillofacial state analysis scheme is provided. The present invention will be described in detail with reference to examples.
Example one
In accordance with an embodiment of the present invention, there is provided an embodiment of a method for analyzing craniomaxillofacial conditions, wherein the steps illustrated in the flowchart of the figures may be performed in a computer system, such as a set of computer-executable instructions, and wherein, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than that illustrated.
The analysis method of the craniomaxillofacial state provided by the embodiment of the invention can be applied to a mononuclear sequence three-dimensional head shadow measurement system based on head structure integration and modularization, and can realize three-dimensional analysis of the head shadow.
FIG. 1 is a flow chart of an alternative method of analyzing craniomaxillary surface condition according to an embodiment of the invention, as shown in FIG. 1, comprising the steps of:
step S102, acquiring a target sequence image of the head craniomaxillofacial of a target user, wherein the target sequence image is a soft tissue sequence image or a black bone sequence image, and the image type of the target sequence image is a Magnetic Resonance Image (MRI);
step S104, synthesizing the target sequence image into a Computed Tomography (CT) image to obtain a synthesized CT image;
step S106, establishing a reference coordinate system in the target sequence image and the synthetic CT image, wherein the reference coordinate system accords with the head integration and modularization characteristics of a user;
step S108, performing three-dimensional analysis on the craniomaxillofacial soft tissue in the target sequence image by taking the reference coordinate system as a reference to obtain the craniomaxillofacial soft tissue information of the head;
step S110, performing three-dimensional analysis on the craniomaxillofacial hard tissue in the synthetic CT image by taking the reference coordinate system as a reference to obtain the head craniomaxillofacial hard tissue information;
and S112, analyzing whether the head craniomaxillofacial of the target user is abnormally deformed or not based on the soft tissue information and the hard tissue information of the head craniomaxillofacial.
Through the steps, when whether the craniomaxillofacial deformation of the user is abnormal or not is analyzed, a target sequence image of the head craniomaxillofacial surface of the target user is obtained firstly, the target sequence image is synthesized into a computed tomography CT image to obtain a synthesized CT image, a reference coordinate system is established in the target sequence image and the synthesized CT image, the craniomaxillofacial soft tissue is subjected to three-dimensional analysis in the target sequence image by taking the reference coordinate system as reference to obtain the craniomaxillofacial soft tissue information, the craniomaxillofacial hard tissue is subjected to three-dimensional analysis in the synthesized CT image by taking the reference coordinate system as reference to obtain the head craniomaxillofacial hard tissue information, and whether the head craniomaxillofacial deformation of the target user is abnormal or not is analyzed based on the head craniomaxillofacial soft tissue information and the hard tissue information. In the embodiment, a reference coordinate system (based on human head integration and modularization characteristics) can be established through a sequence image of the head craniomaxillofacial of a user, the soft and hard tissues of the craniomaxillofacial are analyzed by taking the reference coordinate system as a reference, whether the head craniomaxillofacial is abnormally deformed or not is determined, the craniomaxillofacial soft and hard tissues are analyzed comprehensively and accurately, the craniomaxillofacial analysis work is automatically completed, the craniomaxillofacial state analysis efficiency is improved, and the technical problem that the comprehensive craniomaxillofacial state cannot be obtained due to the fact that the error is large in the determination process of the central sagittal plane of the craniomaxillofacial caused by the irregularity of the craniomaxillofacial is solved.
The present invention will be described in detail with reference to the above steps.
Step S102, acquiring a target sequence image of the head craniomaxillofacial of a target user, wherein the target sequence image is a soft tissue sequence image or a black bone sequence image, and the image type of the target sequence image is MRI.
A craniomaxillofacial surface of a user comprising a plurality of hard tissues and surrounding external soft tissues, wherein the hard tissues include, but are not limited to: mandible, maxilla, teeth, etc.
Optionally, the step of obtaining a target sequence image of a head craniomaxillofacial area of the target user includes: after the supine position of the target user meets a preset supine condition, detecting the head direction and the head position of the target user; analyzing whether the oral cavity opening and closing state of the target user meets a preset opening and closing state or not based on the head direction and the head position of the target user; under the condition that the oral cavity opening and closing state of the target user is determined to meet the preset opening and closing state, performing MRI (magnetic resonance imaging) soft tissue routine sequence scanning on the head of the target user by adopting a gradient echo sequence (for example, a 3D (three-dimensional) gradient echo sequence) to obtain a head craniomaxillofacial soft tissue sequence image; or, under the condition that the oral cavity opening and closing state of the target user meets the preset opening and closing state, the low-flip-angle black bone sequence is adopted to carry out MRI scanning on the head of the target user so as to obtain a black bone sequence image of the craniomaxillofacial surface of the head.
The sequence image related to the present application is an image obtained by nuclear magnetic scanning, and the obtained sequence image includes: soft tissue sequence images and black bone sequence images. For black bone sequence images, fat and water are suppressed by using a low flip angle to obtain a uniform soft tissue background (currently, bone tissue is shown less clearly by nuclear magnetic scan sequences, black bone sequences can effectively suppress signals from fat and water, cortical bone is rendered black and recognizable, soft tissue is rendered uniformly gray, and such MRI sequences render bone tissue in a discernible black by improving image contrast between bone and other soft tissue, reducing contrast between different soft tissues, and are therefore referred to as "black bone" sequences.
And step S104, synthesizing the target sequence image into a Computed Tomography (CT) image to obtain a synthesized CT image.
In the present invention, the step of synthesizing the target sequence image into the computed tomography CT image to obtain a synthesized CT image includes: converting the target sequence image into a head sequence image by adopting a deep learning unpaired data mode; and automatically coding the head sequence image to obtain a CT image.
Alternatively, after synthesizing the target sequence image into the CT image of the computed tomography scan to obtain a synthesized CT image, the analysis method further includes: converting the synthesized CT image into an MRI image by adopting a deep learning unpaired data mode; and analyzing the difference between the synthetic CT and the actual CT through a preset image analysis model.
In the process of synthesizing CT images, deep learning or a scheme for generating a antagonistic network (GaN) CNN is adopted, and the application illustrates a deep learning unpaired data mode. Compared with other methods for training matched data, the method for deeply learning the unpaired data is more real in CT images and less in artifact and fuzzy spots, and the method for deeply learning the unpaired data comprises the following three steps: in the first step, the MRI soft tissue sequence images are combined into CT images by learning. Second, learning converts the synthesized CT image back to an MRI image. And thirdly, finding out the difference between the synthesized CT and the actual CT through training.
And step S106, establishing a reference coordinate system in the target sequence image and the synthesized CT image, wherein the reference coordinate system accords with the head integration and modularization characteristics of the user.
As an alternative embodiment of the present invention, the step of establishing a reference coordinate system in the target sequence image and the synthesized CT image includes: selecting a brain midline meeting the symmetrical structure of the two sides of the head of the user, or positioning the brain midline of the head structure of the virtual anatomic user; defining a median sagittal plane from the midline of the brain; determining a projection point of a user head target identification point on a median sagittal plane according to predefined user head modular classification data, and taking the projection point as a coordinate origin; taking a plane which is perpendicular to a median sagittal plane and has a coordinate origin and a nose root point of the head of the user as a horizontal plane; and constructing a reference coordinate system in the target sequence image and the synthesized CT image based on the coordinate origin and the horizontal plane.
In the embodiment of the invention, when the brain midline is determined, a structure which meets bilateral symmetry to the maximum extent can be selected as the brain midline in two ways; second, the midline of the brain is defined by automatically locating the anatomy that actually exists.
After the brain midline is defined, a three-dimensional reference coordinate system can be constructed according to the incidence relation between the brain midline and the median sagittal plane of the human head by the characteristics of human head integration and modularization.
The modular structure of the human head is present, and the high correlation of the midline structures of the forebrain, the forecranium and the mid-upper face of the human body is fully embodied under physiological and pathological conditions, wherein the midline is the first established structural boundary in the neural plate, and the spinal cord foreplate sets the midline of the face by generating, for example, Shh (Sonic hedgehog, Shh) signal molecules, and simultaneously induces the division of the forebrain into two hemispheres. This midline structure is the axis of self-development of the vertebrate embryo. Abnormalities in facial midline structures often co-exist with abnormalities in brain midline structures. According to the embodiment of the invention, according to the consistency of the brain and the facial midline, the craniomaxillofacial state is analyzed by replacing the fused facial midline with the brain midline in the anatomy, an analysis result is obtained, and the brain midline and the facial midline are connected by using MRI, so that the process of three-dimensional cephalometric measurement research is greatly accelerated, and the craniomaxillofacial deformity can be deeply explored.
The head target identification point may be a certain identification point of the head of the user, taking a sphenoid saddle point at a junction of the anterior and posterior craniums as an example, determining a projection of the sphenoid saddle point at the junction of the anterior and posterior craniums on a median sagittal plane as an origin of coordinates, and constructing a three-dimensional coordinate system by taking the origin of coordinates and a plane perpendicular to the median sagittal plane as a horizontal plane through a nasion point.
And S108, performing three-dimensional analysis on the craniomaxillofacial soft tissue in the target sequence image by taking the reference coordinate system as reference so as to obtain the craniomaxillofacial soft tissue information of the head.
Before analyzing soft tissue information, the position of the head of a user needs to be positioned and scanned, the user is ensured to be in a supine position, the direction and the position of the head are the same, teeth are in a median occlusal position, and therefore the best target sequence image of the head of the user can be obtained through scanning. The functional states of the displayed muscles of the target sequence images are the same and comparable.
Optionally, performing three-dimensional analysis on the craniomaxillofacial soft tissue in the target sequence image to obtain the craniomaxillofacial soft tissue information of the head includes: segmenting muscle morphology of the face of the user and determining muscle volume of the face of the user; analyzing muscle symmetry of the user's face based on muscle morphology, muscle volume, and facial fat distribution range; soft tissue information of the cranio-maxillofacial area of the head is determined based on muscle symmetry, muscle morphology, muscle volume and joint parameters of the face of the user. The analysis of the soft tissue information includes the analysis of muscle morphology, muscle volume, muscle position, muscle orientation, muscle symmetry, and symmetry of facial fat of the user's face.
Wherein, when analyzing the muscle form and the muscle volume of the user's face, include: measuring the muscle shape and volume of the face of the user (for example, using MRI to perform shape and volume measurement on the facial muscle by segmenting and measuring the facial muscle), and selecting the part with higher measurement priority (for example, segmenting and measuring the temporal muscle and the masseter muscle which are most easily segmented in all samples) because the facial muscle is more; obtaining the normal value range of the average form and volume of each muscle by using the normal control group data; the morphological and volume changes of the muscles of the craniomaxillofacial surfaces of each head are analyzed with reference to the normal values of the muscle morphology and the normal values of the volume.
When analyzing the position, the direction and the symmetry of the muscle of the face of the user, the method comprises the following steps: evaluating the positions and directions of the temporalis muscles and the masseter muscles in each group of samples relative to a global coordinate system; in the users of the craniomaxillofacial asymmetric group, the shapes, the volumes, the positions and the directions of the temporalis muscles and the masseter muscles on the left and the right sides are compared, and the difference between the temporalis muscles and the masseter muscles is statistically analyzed. And comparing with a normal control group, and judging the characteristic changes of muscles on two side parts of the craniomaxillofacial asymmetric deformity.
When analyzing the symmetry of the facial fat of the user, the method comprises the following steps: and (3) performing three-dimensional direction delineation on fat distribution ranges on two sides of the sample face of the craniomaxillofacial asymmetric group, then quantitatively measuring and comparing, and analyzing the symmetry of fat on two sides of the craniomaxillofacial asymmetric malformed face.
And step S110, performing three-dimensional analysis on the craniomaxillofacial hard tissue in the synthetic CT image by taking the reference coordinate system as a reference so as to obtain the head craniomaxillofacial hard tissue information.
When the quantitative three-dimensional analysis is carried out on the craniomaxillofacial soft and hard tissues by taking the reference coordinate system as reference, the involved analysis of the head hard tissues comprises the following steps: and (4) carrying out quantitative analysis on hard tissues of all samples, obtaining a normal value range by using a normal control group, and analyzing the craniomaxillofacial state.
In analyzing hard tissue information, the quantitative analysis of hard tissue elements includes the size, position, orientation, shape and symmetry of the maxilla and mandible, which consists of two parts, self-symmetry and systematic symmetry. Self-symmetry is the symmetry of the facial hard tissue elements with respect to a local coordinate system. System symmetry is the alignment of the local coordinate system with respect to the reference coordinate system. For example, for the sizes of the maxilla and mandible, the length, width and height of the maxilla need to be determined; when analyzing jaw orientation, jaw rotation (about the vertical axis), torsion (about the anteroposterior axis), and inclination (about the lateral axis) can be analyzed.
When hard tissue information is analyzed, analysis is carried out on the articular disc, the articular space, the joint cavity effusion, the condylar bone and the like of the temporomandibular joint on all images, the reference image of the temporomandibular joint is combined with the temporomandibular joint hard tissue image reconstructed by the synthetic CT, and the joint condition of the craniomaxillofacial head is fully evaluated.
And S112, analyzing whether the head craniomaxillofacial of the target user is abnormally deformed or not based on the soft tissue information and the hard tissue information of the head craniomaxillofacial.
According to the embodiment of the invention, a reference coordinate system which accords with the head integration and modularization characteristics of a human body is established through automatic positioning, the reference coordinate system is used as a reference, the three-dimensional analysis is carried out on the cranio-maxillofacial soft tissue and the hard tissue in an MRI image and a synthesized CT image, the basic information of the brain, the skull, the jaw and the face of the human body can be analyzed more accurately, and after comparison and reference, the state of the cranio-maxillofacial surface of the head of a user can be determined, and whether the cranio-maxillofacial surface of the head is abnormally deformed or not can be.
Example two
The invention is illustrated below by means of a further alternative embodiment.
FIG. 2 is a schematic view of an alternative craniomaxillary surface condition analysis device according to an embodiment of the present invention, which may include, as shown in FIG. 2: an acquisition unit 21, a synthesis unit 22, a creation unit 23, a first analysis unit 24, a second analysis unit 25, a third analysis unit 26, wherein,
the acquiring unit 21 is configured to acquire a target sequence image of a craniomaxillofacial surface of a head of a target user, where the target sequence image is a soft tissue sequence image or a black bone sequence image, and the image type of the target sequence image is a magnetic resonance image MRI;
a synthesizing unit 22, configured to synthesize the target sequence image into a computed tomography CT image to obtain a synthesized CT image;
the establishing unit 23 is configured to establish a reference coordinate system in the target sequence image and the synthesized CT image, where the reference coordinate system conforms to the head integration and modularization characteristics of the user;
the first analysis unit 24 is used for performing three-dimensional analysis on the craniomaxillofacial soft tissue in the target sequence image by taking the reference coordinate system as a reference so as to obtain the craniomaxillofacial soft tissue information of the head;
a second analysis unit 25, configured to perform three-dimensional analysis on the craniomaxillofacial hard tissue in the synthesized CT image with reference to the reference coordinate system to obtain information of the head craniomaxillofacial hard tissue;
and a third analyzing unit 26, configured to analyze whether the head craniomaxillofacial of the target user is abnormally deformed based on the soft tissue information and the hard tissue information of the head craniomaxillofacial.
The craniomaxillofacial state analysis device can firstly obtain a target sequence image of the head craniomaxillofacial of a target user through the obtaining unit 21 when analyzing whether the craniomaxillofacial of the user has deformation abnormality or not, synthesize the target sequence image into a computed tomography CT image through the synthesizing unit 22 to obtain a synthesized CT image, establish a reference coordinate system in the target sequence image and the synthesized CT image through the establishing unit 23, perform three-dimensional analysis on the craniomaxillofacial soft tissue in the target sequence image by taking the reference coordinate system as reference through the first analyzing unit 24 to obtain the head craniomaxillofacial soft tissue information, perform three-dimensional analysis on the craniomaxillofacial hard tissue in the synthesized CT image by taking the reference coordinate system as reference through the second analyzing unit 25 to obtain the head craniomaxillofacial hard tissue information, and perform three-dimensional analysis on the basis of the head craniomaxillofacial soft tissue information and the head craniomaxillofacial soft tissue information through the third analyzing unit, and analyzing whether the head craniomaxillofacial of the target user is abnormally deformed or not. In the embodiment, a reference coordinate system (based on human head integration and modularization characteristics) can be established through a sequence image of the head craniomaxillofacial of a user, the soft and hard tissues of the craniomaxillofacial are analyzed by taking the reference coordinate system as a reference, whether the head craniomaxillofacial is abnormally deformed or not is determined, the craniomaxillofacial soft and hard tissues are analyzed comprehensively and accurately, the craniomaxillofacial analysis work is automatically completed, the craniomaxillofacial state analysis efficiency is improved, and the technical problem that the comprehensive craniomaxillofacial state cannot be obtained due to the fact that the error is large in the determination process of the central sagittal plane of the craniomaxillofacial caused by the irregularity of the craniomaxillofacial is solved.
Optionally, the establishing unit includes: the selection module is used for selecting the brain midline which meets the symmetrical structure of the two sides of the head of the user, or positioning the brain midline of the head structure of the virtual anatomic user; a first determination module for defining a median sagittal plane from the brain midline; the second determination module is used for determining a projection point of the user head target identification point on the median sagittal plane according to the predefined user head modularization classification data, and taking the projection point as a coordinate origin; a third determining module, configured to use the coordinate origin and a plane perpendicular to the midsagittal plane and at the nasion point of the head of the user as a horizontal plane; and the construction module is used for constructing a reference coordinate system in the target sequence image and the synthesized CT image based on the coordinate origin and the horizontal plane.
In an embodiment of the present invention, the obtaining unit includes: the first detection module is used for detecting the head direction and the head position of a target user after the supine position of the target user meets a preset supine condition; the first analysis module is used for analyzing whether the oral cavity opening and closing state of the target user meets a preset opening and closing state or not based on the head direction and the head position of the target user; the system comprises a first scanning module, a second scanning module and a third scanning module, wherein the first scanning module is used for carrying out MRI (magnetic resonance imaging) soft tissue conventional sequence scanning on the head of a target user by adopting a gradient echo sequence under the condition that the oral cavity opening and closing state of the target user meets a preset opening and closing state so as to obtain a head craniomaxillofacial soft tissue sequence image; or the second scanning module is used for carrying out MRI scanning on the head of the target user by adopting the low flip angle black bone sequence under the condition that the oral cavity opening and closing state of the target user is determined to meet the preset opening and closing state, so as to obtain a head craniomaxillofacial black bone sequence image.
Alternatively, the synthesis unit comprises: the first conversion module is used for converting the target sequence image into a head sequence image in a deep learning unpaired data mode; and the coding module is used for automatically coding the head sequence image to obtain a CT image.
Optionally, the craniomaxillofacial condition analysis apparatus further comprises: the second conversion module is used for converting the synthesized CT image back to the MRI image by adopting a depth learning unpaired data mode after synthesizing the target sequence image into the computed tomography CT image to obtain a synthesized CT image; and the first analysis module is used for analyzing the difference between the synthetic CT and the actual CT through a preset image analysis model.
In an embodiment of the present invention, the first analysis unit includes: the segmentation module is used for segmenting the muscle form of the face of the user and determining the muscle volume of the face of the user; the second analysis module is used for analyzing the muscle symmetry of the face of the user based on the muscle form, the muscle volume and the face fat distribution range; and the fourth determination module is used for determining the soft tissue information of the craniomaxillofacial surface of the head based on the muscle symmetry, the muscle form, the muscle volume and the joint parameters of the face of the user.
The analysis device for craniomaxillofacial conditions may further include a processor and a memory, the acquiring unit 21, the synthesizing unit 22, the establishing unit 23, the first analyzing unit 24, the second analyzing unit 25, the third analyzing unit 26, and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls a corresponding program unit from the memory. The kernel can be set to be one or more, and whether the head craniomaxillofacial surface of the target user is abnormally deformed or not is analyzed on the basis of the soft tissue information and the hard tissue information of the head craniomaxillofacial surface by adjusting the kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including: a processor; and a memory for storing executable instructions for the processor; wherein the processor is configured to perform the method of analyzing a craniomaxillary surface condition of any of the above via execution of executable instructions.
According to another aspect of the embodiments of the present invention, there is also provided a storage medium including a stored program, wherein the apparatus on which the storage medium is located is controlled to perform the analysis method of craniomaxillary surface condition according to any one of the above-mentioned methods when the program is executed.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device: acquiring a target sequence image of the head craniomaxillofacial of a target user, wherein the target sequence image is a soft tissue sequence image or a black bone sequence image, and the image type of the target sequence image is a Magnetic Resonance Image (MRI); synthesizing the target sequence image into a Computed Tomography (CT) image to obtain a synthesized CT image; establishing a reference coordinate system in the target sequence image and the synthesized CT image, wherein the reference coordinate system accords with the head integration and modularization characteristics of a user; three-dimensional analysis is carried out on the craniomaxillofacial soft tissue in the target sequence image by taking the reference coordinate system as reference so as to obtain the craniomaxillofacial soft tissue information of the head; performing three-dimensional analysis on the craniomaxillofacial hard tissue in the synthetic CT image by taking the reference coordinate system as reference so as to obtain the head craniomaxillofacial hard tissue information; and analyzing whether the head craniomaxillofacial of the target user is abnormally deformed or not based on the soft tissue information and the hard tissue information of the head craniomaxillofacial.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method of analyzing craniomaxillofacial conditions, comprising:
acquiring a target sequence image of the head craniomaxillofacial of a target user, wherein the target sequence image is a soft tissue sequence image or a black bone sequence image, and the image type of the target sequence image is a Magnetic Resonance Image (MRI);
synthesizing the target sequence image into a Computed Tomography (CT) image to obtain a synthesized CT image;
establishing a reference coordinate system in the target sequence image and the synthesized CT image, wherein the reference coordinate system accords with the head integration and modularization characteristics of a user;
performing three-dimensional analysis on the craniomaxillofacial soft tissue in the target sequence image by taking the reference coordinate system as a reference so as to obtain the craniomaxillofacial soft tissue information of the head;
performing three-dimensional analysis on the craniomaxillofacial hard tissue in the synthetic CT image by taking the reference coordinate system as a reference so as to obtain the head craniomaxillofacial hard tissue information;
and analyzing whether the head craniomaxillofacial of the target user is abnormally deformed or not based on the soft tissue information and the hard tissue information of the head craniomaxillofacial.
2. The analysis method according to claim 1, wherein the step of establishing a reference coordinate system in the target sequence image and the composite CT image comprises:
selecting a brain midline meeting the symmetrical structure of the two sides of the head of the user, or positioning the brain midline of the head structure of the virtual anatomic user;
defining a median sagittal plane from the brain midline;
determining a projection point of a user head target identification point on the median sagittal plane according to predefined user head modular classification data, and taking the projection point as a coordinate origin;
taking the coordinate origin and the nasal root point of the head of the user as a plane perpendicular to a median sagittal plane as a horizontal plane;
and constructing the reference coordinate system in the target sequence image and the synthetic CT image based on the coordinate origin and the horizontal plane.
3. The analysis method according to claim 1, wherein the step of obtaining a target sequence of images of the craniomaxillofacial of the head of the target user comprises:
after detecting that the supine position of the target user meets a preset supine condition, detecting the head direction and the head position of the target user;
analyzing whether the oral cavity opening and closing state of the target user meets a preset opening and closing state or not based on the head direction and the head position of the target user;
under the condition that the oral cavity opening and closing state of the target user is determined to meet the preset opening and closing state, performing MRI (magnetic resonance imaging) soft tissue conventional sequence scanning on the head of the target user by adopting a gradient echo sequence to obtain a head craniomaxillofacial soft tissue sequence image; or,
and under the condition that the oral cavity opening and closing state of the target user meets the preset opening and closing state, carrying out MRI scanning on the head of the target user by adopting a low flip angle black bone sequence to obtain a black bone sequence image of the craniomaxillofacial surface of the head.
4. The analysis method according to claim 1, wherein the step of synthesizing the target sequence images into Computed Tomography (CT) images comprises:
converting the target sequence image into a head sequence image by adopting a deep learning unpaired data mode;
and automatically coding the head sequence image to obtain a CT image.
5. The analysis method according to claim 4, wherein after synthesizing the target sequence images into computed tomography CT images, the analysis method further comprises:
converting the synthesized CT image into an MRI image by adopting a deep learning unpaired data mode;
and analyzing the difference between the synthetic CT and the actual CT through a preset image analysis model.
6. The analysis method according to claim 1, wherein performing a three-dimensional analysis of craniomaxillofacial soft tissue in the target sequence image to obtain craniomaxillofacial soft tissue information comprises:
segmenting muscle morphology of the face of the user and determining muscle volume of the face of the user;
analyzing muscle symmetry of the user's face based on the muscle morphology, muscle volume, and facial fat distribution range;
determining soft tissue information of a cranio-maxillofacial area of a head based on muscle symmetry, muscle morphology, muscle volume, and joint parameters of the user's face.
7. An apparatus for analyzing craniomaxillofacial conditions, comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a target sequence image of the head craniomaxillofacial of a target user, the target sequence image is a soft tissue sequence image or a black bone sequence image, and the image type of the target sequence image is MRI (magnetic resonance imaging);
the synthesizing unit is used for synthesizing the target sequence image into a Computed Tomography (CT) image to obtain a synthesized CT image;
the system comprises an establishing unit, a calculating unit and a calculating unit, wherein the establishing unit is used for establishing a reference coordinate system in a target sequence image and a synthesized CT image, and the reference coordinate system accords with the head integration and modularization characteristics of a user;
the first analysis unit is used for carrying out three-dimensional analysis on the craniomaxillofacial soft tissue in the target sequence image by taking the reference coordinate system as a reference so as to obtain the craniomaxillofacial soft tissue information of the head;
the second analysis unit is used for carrying out three-dimensional analysis on the craniomaxillofacial hard tissue in the synthesized CT image by taking the reference coordinate system as a reference so as to obtain the head craniomaxillofacial hard tissue information;
and the third analysis unit is used for analyzing whether the head craniomaxillofacial of the target user is abnormally deformed or not based on the soft tissue information and the hard tissue information of the head craniomaxillofacial.
8. The analysis device according to claim 7, wherein the establishing unit includes:
the selection module is used for selecting the brain midline which meets the symmetrical structure of the two sides of the head of the user, or positioning the brain midline of the head structure of the virtual anatomic user;
a first determination module to define a median sagittal plane from the brain midline;
the second determination module is used for determining a projection point of the user head target identification point on the median sagittal plane according to predefined user head modularization classification data, and taking the projection point as a coordinate origin;
a third determining module, configured to use the coordinate origin and a plane perpendicular to a midsagittal plane and at the nasion point of the head of the user as a horizontal plane;
and the construction module is used for constructing the reference coordinate system in the target sequence image and the synthetic CT image based on the coordinate origin and the horizontal plane.
9. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of analyzing craniomaxillary surface condition of any one of claims 1-6 via execution of the executable instructions.
10. A storage medium characterized in that it comprises a stored program, wherein the apparatus on which the storage medium is controlled when the program is run performs the craniomaxillary surface condition analysis method of any one of claims 1 to 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112258492A (en) * | 2020-10-30 | 2021-01-22 | 李艳 | Skull asymmetric information acquisition method, storage medium and electronic device |
| CN117392735A (en) * | 2023-12-12 | 2024-01-12 | 深圳市宗匠科技有限公司 | Face data processing method, device, computer equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015175848A1 (en) * | 2014-05-14 | 2015-11-19 | The Johns Hopkins University | System and method for automatic localization of structures in projection images |
| CN107865658A (en) * | 2016-09-23 | 2018-04-03 | 西门子保健有限责任公司 | Method and apparatus for correcting synthesis electron-density map |
| EP3550515A1 (en) * | 2018-04-05 | 2019-10-09 | Siemens Healthcare GmbH | Cross-modality image synthesis |
| CN110464353A (en) * | 2019-08-21 | 2019-11-19 | 南方医科大学 | A kind of pseudo- CT synthetic method and application based on depth convolutional neural networks |
| EP3572822A1 (en) * | 2018-05-23 | 2019-11-27 | RaySearch Laboratories AB | Medical image conversion |
-
2020
- 2020-04-30 CN CN202010366369.XA patent/CN111528889B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015175848A1 (en) * | 2014-05-14 | 2015-11-19 | The Johns Hopkins University | System and method for automatic localization of structures in projection images |
| CN107865658A (en) * | 2016-09-23 | 2018-04-03 | 西门子保健有限责任公司 | Method and apparatus for correcting synthesis electron-density map |
| EP3550515A1 (en) * | 2018-04-05 | 2019-10-09 | Siemens Healthcare GmbH | Cross-modality image synthesis |
| EP3572822A1 (en) * | 2018-05-23 | 2019-11-27 | RaySearch Laboratories AB | Medical image conversion |
| CN110464353A (en) * | 2019-08-21 | 2019-11-19 | 南方医科大学 | A kind of pseudo- CT synthetic method and application based on depth convolutional neural networks |
Non-Patent Citations (3)
| Title |
|---|
| JELMER M. WOLTERINK: "Deep MR to CT Synthesis Using Unpaired Data", 《SASHIMI 2017: SIMULATION AND SYNTHESIS IN MEDICAL IMAGING》 * |
| 姜喜玲: "基于头部一体化与模块化结构特点的颅面对称性CT与MRI三维分析", 《万方学位论文》 * |
| 萨仁: "MRI结合伪彩色处理技术研究面部肌肉对称性", 《中国优秀硕士学位论文全文数据库医药卫生科技辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112258492A (en) * | 2020-10-30 | 2021-01-22 | 李艳 | Skull asymmetric information acquisition method, storage medium and electronic device |
| CN117392735A (en) * | 2023-12-12 | 2024-01-12 | 深圳市宗匠科技有限公司 | Face data processing method, device, computer equipment and storage medium |
| CN117392735B (en) * | 2023-12-12 | 2024-03-22 | 深圳市宗匠科技有限公司 | Face data processing method, device, computer equipment and storage medium |
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