CN111839570B - Device for predicting femoral head collapse risk and use method and application thereof - Google Patents
Device for predicting femoral head collapse risk and use method and application thereof Download PDFInfo
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Abstract
The invention provides a device for predicting femoral head collapse risk, and a use method and application thereof. The device comprises an image processing unit, an analysis unit, a separation unit, a calculation unit and an output unit; the device can obtain a plurality of data of the femoral head of the case according to the CT image of the hip joint of the case, including the volume of the hardened bone, the soft tissue volume and the total skull density, simultaneously obtain the relation between each data and the time of the course of the case, calculate the collapse risk of the femoral head of the case according to the total skull density and the hardened bone volume of the case, and provide reliable medical data for the clinical treatment of the femoral head necrosis.
Description
Technical Field
The invention relates to the technical field of medical image recognition, in particular to a device for predicting femoral head collapse risk, and a use method and application thereof.
Background
Femur head necrosis (Osteonecrosis of the femoral head, ONFH), also known as ischemic necrosis of the femoral head (Avascular necrosis, AVN), refers to a disorder of local blood circulation in the femoral head caused by various causes, resulting in hypoxia, shrinkage and death of bone cells. The femoral head necrosis is a pathological evolution process, and occurs initially in a loading area of the femoral head, and the necrotic bone trabecular structure is damaged under the action of stress, namely, the microscopic fracture and the repair process aiming at damaged bone tissues are performed later. The cause of osteonecrosis is not eliminated, the repair is imperfect, and the damage and repair process continues, resulting in structural changes of the femoral head, collapse, deformation of the femoral head, arthritis, and dysfunction.
Collapse of the femoral head refers to the beginning of a decrease in the ability of the femoral head to withstand pressure after necrosis of the femoral head to a certain extent, and the total of numerous fine compression fractures of the femoral head. Collapse of the femoral head necrosis means that the mechanical properties of the subchondral bone plate fail, eventually leading to compromised hip dysfunction. The most common symptom of femoral head necrosis is pain, the pain is at the proximal side of the hip joint and thigh, and the pain can radiate to the knee. Pain may be caused by necrotic tissue-repaired inflammatory lesions or high pressure within inflammatory lesions, and may be manifested as persistent pain, resting pain. The osteochondral collapse and deformation causes secondary arthritis, or chronic injury pain in the attachment site of the muscle and ligament around the hip joint. Hip mobility is limited, in particular rotational mobility is limited, or painful and contractile lameness.
However, femoral head necrosis has a number of different symptoms and signs in addition to pain, and the time of occurrence of the pain and the degree of onset are different, but are all based on pathological evolution. While various clinical manifestations are not specific to femoral head necrosis, many hip joint disorders can occur, in other words, diagnosis of femoral head necrosis is difficult to make by subjective symptoms and clinical examination of patients.
At present, the femoral head necrosis is clinically diagnosed mainly by means of imaging, the imaging performance of the femoral head necrosis is related to the severity of pathological changes and pathological processes, and the pathological changes determine imaging diversification. The image segmentation plays an important role in quantitative and qualitative analysis of medical images, and directly influences the subsequent analysis and processing work of a computer-aided diagnosis system. The segmentation method of the femoral head CT image mainly comprises manual segmentation by an expert, computer interactive segmentation and full-automatic segmentation.
In order to further treat femoral head necrosis, a better diagnosis and treatment scheme is provided for patients, and researchers develop different diagnosis and treatment systems according to computers. For example, CN110444293a discloses a femoral head necrosis diagnosis and treatment system and a cloud service system, the system comprising: the detection data acquisition module is used for acquiring detection data of a patient from the detection equipment; the diagnosis information acquisition module is used for acquiring the diagnosis information of the patient; the diagnosis and treatment information module is used for determining and storing diagnosis and treatment information of a user according to the diagnosis and treatment information and the detection data; the query module is used for querying corresponding case information from the stored diagnosis and treatment information according to a query instruction of a user; and the communication module is used for providing an online communication platform of the case information for the target crowd and solving the technical problem that the information system of the existing hospital can not meet the diagnosis and communication requirements of users. However, the invention mainly focuses on the aspect of acquiring diagnosis and treatment information, provides long-term diagnosis and treatment for patients, has no obvious help on the aspects of predicting the degree of femoral head necrosis and collapse risk, and can better help a doctor to put forward a more proper treatment scheme and also help the patients to recover earlier if the condition development of the patients can be predicted according to CT images.
Therefore, the device for predicting the collapse risk of the femoral head has high clinical significance.
Disclosure of Invention
In view of the problems in the prior art, the present invention provides a device for predicting the risk of collapse of the femoral head, and methods of use and application thereof. The device can help doctors predict the condition development of patients and give the probability of the collapse risk of femoral heads of patients.
To achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides an apparatus for predicting risk of collapse of a femoral head comprising:
an image processing unit: the method comprises the steps of processing hip joint images of cases to obtain a hardened bone mask, a cancellous bone mask and a soft tissue mask of the hip joint, reconstructing a femoral head three-dimensional model, and then performing surface meshing and body meshing;
analysis unit: calculating Polyline of the femoral head three-dimensional model, and reconstructing a femoral head mask based on Polyline;
a separation unit: separating out the hardened bone mask, the cancellous bone mask and the soft tissue mask inside the femoral head and reconstructing a three-dimensional model based on the hardened bone mask, the cancellous bone mask and the soft tissue mask of the hip joint and the femoral head mask by using an intersection algorithm of Boolean operation;
a calculation unit: calculating the volume of the hardened bone and the volume of the soft tissue by using the three-dimensional model, reading the grid gray value of the three-dimensional model of the femoral head to obtain the total skull density, and then calculating the relation between the volume of the hardened bone, the volume of the soft tissue and the total skull density and the disease course time of the case;
an output unit: and taking the hardened bone volume and the total skull density as input variables to input a Logistic regression model, and outputting the probability of femoral head collapse risk.
The device for predicting the collapse risk of the femoral head can reconstruct a three-dimensional model of the femoral head of a patient according to the hip joint image of the patient, and can also obtain a hardened bone mask, a cancellous bone mask and a soft tissue mask of the femoral head and reconstruct the three-dimensional model; the calculation unit can also obtain the total skull density of the femoral head, give the connection between the disease course time of the case and various data, help doctors to know the disease condition of the patient on one hand, and also help doctors to estimate the development trend of the disease condition of the patient and give a reasonable treatment scheme on the other hand. The device can avoid subjectivity of doctors in experience judgment, can reduce workload of the doctors, and provides reliable medical data for clinic treatment of femoral head necrosis.
As a preferable technical scheme of the invention, the Logistic regression model is expressed by the following equation:
logit (P) =5.137+0.001×sclerotic bone volume-9.674 ×total skull density.
Preferably, the probability of risk of collapse of the femoral head is calculated as follows:
preferably, the total skull density is calculated as follows:
the medical image processing technology and the three-dimensional reconstruction implementation mode can be used for image segmentation and three-dimensional modeling by using the existing commercial software, and can also be identified and processed based on a computer language. As a preferable technical scheme of the invention, the image processing unit comprises a hybrid medical image processing software and/or a 3-matic medical image processing software.
The image processed by the image processing unit may be a clinically usual imaging device such as a CT (Computed Tomography, CT for short, i.e., computed tomography) image, an MRI (Magnetic Resonance Imaging, MRI for short, i.e., magnetic resonance imaging) image, and a DR device (digital X-ray imaging system) image. For example, after a CT image is acquired, the image processing unit processes the CT image as image data to obtain a femoral head three-dimensional model and masks for each part.
Preferably, the separation unit separates out the hardened bone mask, the cancellous bone mask, and the soft tissue mask inside the femoral head based on an intersection algorithm of boolean operations.
Preferably, the method for acquiring the grid gray value comprises the following steps: and after carrying out surface meshing and body meshing on the femoral head three-dimensional model, importing the Mimics medical image processing software, and reading the mesh gray value.
Preferably, the three-dimensional model of the femoral head is obtained by reconstructing a proximal three-dimensional model of the femur, and the specific method is as follows: based on gray value segmentation, separating a mask of the proximal femur from the hip joint image, reconstructing the proximal femur three-dimensional model, and separating a three-dimensional femur model from the proximal femur three-dimensional model by taking a plane of the base of the femur as a boundary.
The device provided in the present invention also predicts the relationship between the time of the disease course of the case and the sclerotic bone volume, soft tissue density and total skull density.
Wherein, the relationship between the volume of the hardened bone and the time of the disease process can be expressed as:
scleroste = 126.424 x time of disease (month) +206.061;
the relationship between soft tissue volume and time of disease can be expressed as:
soft tissue volume = 0.005 x time of disease +0.565
The relationship between total skull density and time of disease can be expressed as:
full skull density = -144.674 x time of disease (month) +11538.205
In other words, after obtaining the hip joint image of the patient, the doctor can calculate the disease course time of the patient according to the image, obtain the relation between the disease course time of the case and each parameter, and simultaneously predict the risk of collapse or necrosis of the femoral head of the case to remind the patient to pay attention to protection.
In order to facilitate the acquisition of patient information, the device for predicting the collapse risk of the femoral head provided by the invention further comprises a terminal management module, wherein the terminal management module is used for acquiring the request information of the terminal and outputting the information content corresponding to the request information. The terminal can be a doctor workstation, so that a doctor can conveniently acquire treatment information of a patient in real time, and an authorized user can acquire the requested information.
In a second aspect, the present invention provides a method of using the device of the first aspect, the method comprising the steps of:
(1) Guiding the hip joint image of the case into an image processing unit, separating out a mask at the proximal end of the femur based on gray value segmentation, reconstructing a three-dimensional model at the proximal end of the femur, and simultaneously separating out a hardened bone mask, a cancellous bone mask and a soft tissue mask in the hip joint image;
(2) Separating a femoral head three-dimensional model from a femoral head proximal three-dimensional model by taking a femoral head basal plane as a boundary, and carrying out surface mesh division and body mesh division;
(3) Introducing the three-dimensional model of the femoral head obtained in the step (2) into an analysis unit, calculating Polyline of the three-dimensional model of the femoral head, and reconstructing a femoral head mask based on the Polyline;
(4) Introducing the hardened bone mask, the cancellous bone mask and the soft tissue mask obtained in the step (1) and the femoral head mask obtained in the step (2) into a separation unit to separate the hardened bone mask, the cancellous bone mask and the soft tissue mask in the femoral head and reconstructing a three-dimensional model;
(5) Introducing the three-dimensional model obtained in the step (4) into a calculation unit, calculating the volume of the hardened bone and the volume of the soft tissue, obtaining the full skull density by using the grid gray value of the three-dimensional model of the femoral head in the step (1), and obtaining the relation between the volume of the hardened bone, the volume of the soft tissue and the full skull density and the disease course time of the case;
(6) And (5) introducing the volume of the hardened bone and the total skull density in the step (5) into an output unit, and outputting the probability of the collapse risk of the femoral head.
In a third aspect, the present invention provides the use of a device according to the first aspect for predicting the risk of necrosis or collapse of the femoral head.
It should be noted that the foregoing units are only divided according to functional logic, but are not limited to the above-described division, so long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Compared with the prior art, the invention has at least the following beneficial effects:
the device for predicting the femoral head collapse risk mainly comprises an image processing unit, an analysis unit, a separation unit, a calculation unit and an output unit, can obtain multiple data of the femoral head of a case according to a CT image of the hip joint of the case, including the hardened bone volume, the soft tissue volume and the total skull density, simultaneously obtain the relation between each data and the case disease course time, calculate the femoral head collapse risk of the case according to the total skull density and the hardened bone volume of the case, provide reliable medical data for the clinical treatment of femoral head necrosis, not only help doctors know the illness state of patients, but also help doctors estimate the development trend of the illness state of the patients, and provide reasonable treatment schemes.
Drawings
Fig. 1 is a schematic view of the three-dimensional model reconstructed in example 2, wherein fig. a is a proximal femur three-dimensional model, fig. B is a proximal femur three-dimensional model bounded by a femoral head base plane, and fig. C is a femoral head three-dimensional model.
Fig. 2 is a diagram of a hardened bone mask, a soft tissue mask and a cancellous bone mask obtained by the separation unit in example 2, wherein a is the hardened bone mask, B is the soft tissue mask and C is the cancellous bone mask.
FIG. 3 is a model diagram of the separation measurement of soft tissue volume and hardened bone volume using the intersection Boolean operation in example 2.
Fig. 4 is a graph of ROC curve analysis results obtained for different parameters.
Detailed Description
The following embodiments are further described with reference to the accompanying drawings, but the following examples are merely simple examples of the present invention and do not represent or limit the scope of the invention, which is defined by the claims.
Example 1
Embodiment 1 provides a method of using a device for predicting risk of femoral head collapse, comprising the steps of:
(1) Importing a case hip joint CT file (di com format) into an image separation unit, processing by using a Mimics medical image processing software, dividing based on gray values, separating out a mask at the proximal femur, and reconstructing a proximal femur three-dimensional model;
(2) Separating out a femoral head three-dimensional model from the proximal femur three-dimensional model by taking the femoral head basal plane as a boundary;
(3) In an analysis unit, calculating a Polyline of the femoral head three-dimensional model, and reconstructing a femoral head mask based on the Polyline;
(4) Using an image separation unit to separate a hardened bone mask, a cancellous bone mask and a soft tissue mask in an original hip joint CT file based on gray value segmentation;
(5) In the separation unit, a Boolean operation intersection algorithm is applied to separate a hardened bone mask, a cancellous bone mask and a soft tissue mask in the femoral head, and a three-dimensional model is respectively reconstructed;
(6) After the femoral head three-dimensional model is obtained, 3-matic medical image processing software is imported to conduct surface mesh division and body mesh division.
(7) And in the computing unit, the obtained femoral head mesh is imported into the Mimics medical image processing software to carry out material assignment. Automatically reading a grid gray value (Hounsfiled unit), calculating the total skull density according to the following formula, and obtaining the relation between the total skull density and the disease course time;
(8) In the computing unit, measuring the volume of each part according to the reconstructed hardened bone three-dimensional model, cancellous bone three-dimensional model and soft tissue three-dimensional model, and obtaining the relation between the hardened bone volume and the soft tissue volume and the disease course time;
(9) And inputting a Logistic regression model by taking the hardened bone volume and the total skull density as input variables through an output unit, and outputting the probability of the femoral head collapse risk.
Example 2
The original CT images of 50 cases (28 men and 22 women, average 38.12 + -10.14 years old, total 50 hips) were reviewed in this example according to the method provided in example 1.
Wherein the 50 cases are divided into a collapsed group and a non-collapsed group of 25 hips each; as a healthy control, 5 healthy volunteers (5 hips) were also recruited.
(1) The original CT images of each case are imported into a Mimics medical image processing software for morphological analysis, a mask at the proximal femur is separated based on gray value segmentation, and a three-dimensional model at the proximal femur is reconstructed; separating out a femoral head three-dimensional model from the proximal femur three-dimensional model by taking the femoral head basal plane as a boundary;
a reconstructed three-dimensional model of a case is given in fig. 1, where a is the proximal femoral three-dimensional model, and the plane of the femoral head base is shown in B, and C is the femoral head three-dimensional model.
(2) Dividing a femoral head model according to gray values, separating masking plates of the above 3 structures in the femoral head according to gray value intervals of hardened bones, cancellous bones and soft tissues, reconstructing a three-dimensional model, and measuring volume;
as shown in fig. 2, a case of hardened bone mask (a), soft tissue mask (B) and cancellous bone mask (C) are given;
as shown in fig. 3, where the intersection algorithm of boolean operations was applied, the soft tissue volume and the hardened bone volume were measured separately.
(3) Performing grid division on the whole skull, and calculating the whole skull density through material assignment, wherein the whole skull density is calculated according to the following equation:
(4) Analyzing the linear correlation relationship between the different bone structure volumes, the total skull density and the disease course time and the correlation with collapse ending;
wherein, the relationship between the volume of the hardened bone and the time of the disease process can be expressed as:
scleroste = 126.424 x time of disease (month) +206.061;
the relationship between soft tissue volume and time of disease can be expressed as:
soft tissue volume = 0.005 x disease course time +0.565;
the relationship between total skull density and time of disease can be expressed as:
full skull density = -144.674 x time of disease (month) +11538.205.
And measuring and calculating the femoral head collapse risk according to a Logistic regression model, wherein the Logistic regression model is expressed by the following equation:
logit (P) =5.137+0.001×sclerotic bone volume-9.674 ×total skull density;
the probability of the femoral head collapse risk is calculated as follows:
the accuracy of the above index in predicting collapse was analyzed using ROC curve, as shown in fig. 4, where AUC of the full skull density curve was 0.323; AUC of the hardened bone volume curve was 0.600; AUC of soft tissue volume was 0.573; AUC of hardened bone and soft tissue volume curve is 0.654; AUC of Logistic regression model was 0.765; from the AUC values, it can be seen that the accuracy of the Logistic regression model is highest.
Analysis of results:
by analyzing the morphology of the femoral head of 50 cases and 5 healthy controls, the femoral head necrosis is found to have discontinuous hardened bone boundaries, and meanwhile, the soft tissue volume is reduced;
at the same time, the volume of hardened bone and the total skull density increase with the course of the disease, while the volume of soft tissue decreases (P < 0.05).
In summary, the device for predicting the collapse risk of the femoral head provided by the invention can obtain multiple data of the femoral head of a case according to the CT image of the hip joint of the case, including the hardened bone volume, the soft tissue volume and the total skull density, simultaneously obtain the relation between each data and the time of the case, calculate the collapse risk of the femoral head of the case according to the total skull density and the hardened bone volume of the case, and provide reliable medical data for clinical treatment of femoral head necrosis.
The applicant declares that the above is only a specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and it should be apparent to those skilled in the art that any changes or substitutions that are easily conceivable within the technical scope of the present invention disclosed by the present invention fall within the scope of the present invention and the disclosure.
Claims (7)
1. An apparatus for predicting risk of collapse of a femoral head, the apparatus comprising:
an image processing unit: the method comprises the steps of processing hip joint images of cases to obtain a hardened bone mask, a cancellous bone mask and a soft tissue mask of the hip joint, reconstructing a femoral head three-dimensional model, and then performing surface meshing and body meshing;
analysis unit: calculating a multi-section line of the three-dimensional model of the femoral head, and reconstructing a femoral head mask based on the multi-section line;
a separation unit: separating out the hardened bone mask, the cancellous bone mask and the soft tissue mask inside the femoral head and reconstructing a three-dimensional model based on the hardened bone mask, the cancellous bone mask and the soft tissue mask of the hip joint and the femoral head mask by using an intersection algorithm of Boolean operation;
a calculation unit: calculating the volume of the hardened bone and the volume of the soft tissue by using the three-dimensional model reconstructed by the separation unit, reading the grid gray value of the three-dimensional model of the femoral head to obtain the total skull density, and then calculating the relation between the volume of the hardened bone, the volume of the soft tissue and the total skull density and the disease course time of the case;
an output unit: inputting a Logistic regression model by taking the hardened bone volume and the total skull density as input variables, and outputting the probability of femoral head collapse risk;
the Logistic regression model is expressed in the following equation: logit (P) =5.137+0.001×sclerotic bone volume-9.674 ×total skull density;
the probability of the femoral head collapse risk is calculated as follows:
2. the apparatus of claim 1, wherein the total skull density is calculated as:
3. the apparatus of claim 1, wherein the method for obtaining the grid gray value comprises:
and after carrying out surface meshing and body meshing on the femoral head three-dimensional model, importing the Mimics medical image processing software, and reading the mesh gray value.
4. The apparatus of claim 1, wherein the image processing unit comprises a hybrid medical image processing software and a 3-matic medical image processing software.
5. The apparatus according to claim 1, wherein the separation unit separates the hardened bone mask, the cancellous bone mask, and the soft tissue mask inside the femoral head based on an intersection algorithm of boolean operations.
6. The device according to claim 1, wherein the three-dimensional model of the femoral head is obtained by reconstructing a three-dimensional model of the proximal femur by:
based on gray value segmentation, separating a mask of the proximal femur from the hip joint image, reconstructing the proximal femur three-dimensional model, and separating a three-dimensional femur model from the proximal femur three-dimensional model by taking a plane of the base of the femur as a boundary.
7. A method of using the device of any one of claims 1 to 6, comprising the steps of:
(1) Guiding the hip joint image of the case into an image processing unit, separating out a mask at the proximal end of the femur based on gray value segmentation, reconstructing a three-dimensional model at the proximal end of the femur, and simultaneously separating out a hardened bone mask, a cancellous bone mask and a soft tissue mask in the hip joint image;
(2) Separating a femoral head three-dimensional model from a femoral head proximal three-dimensional model by taking a femoral head basal plane as a boundary, and carrying out surface mesh division and body mesh division;
(3) Introducing the three-dimensional model of the femoral head obtained in the step (2) into an analysis unit, calculating a multi-section line of the three-dimensional model of the femoral head, and reconstructing a femoral head mask based on the multi-section line;
(4) Introducing the hardened bone mask, the cancellous bone mask and the soft tissue mask obtained in the step (1) and the femoral head mask obtained in the step (3) into a separation unit to separate the hardened bone mask, the cancellous bone mask and the soft tissue mask in the femoral head and reconstructing a three-dimensional model;
(5) Introducing the three-dimensional model obtained in the step (4) into a calculation unit, calculating the volume of the hardened bone and the volume of the soft tissue, reading the grid gray value of the three-dimensional model of the femoral head to obtain the total skull density, and then obtaining the relation between the volume of the hardened bone, the volume of the soft tissue and the total skull density and the disease course time of the case;
(6) And (5) introducing the volume of the hardened bone and the total skull density in the step (5) into an output unit, and outputting the probability of the collapse risk of the femoral head.
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| CN108921832A (en) * | 2018-06-26 | 2018-11-30 | 陈卫衡 | Femoral head image analysis method, device, server and medium |
| CN110444293A (en) * | 2019-07-30 | 2019-11-12 | 中国中医科学院望京医院 | Caput femoris necrosis diagnosis and therapy system and cloud service system |
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| CN108921832A (en) * | 2018-06-26 | 2018-11-30 | 陈卫衡 | Femoral head image analysis method, device, server and medium |
| CN110444293A (en) * | 2019-07-30 | 2019-11-12 | 中国中医科学院望京医院 | Caput femoris necrosis diagnosis and therapy system and cloud service system |
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