CN111420271A - Electrode patch positioning method based on head tumor treatment - Google Patents
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Abstract
The invention relates to an electrode patch positioning method based on head tumor treatment, belonging to the technical field of medical treatment. By adopting the technical scheme of the invention, the intelligent electrode patch positioning system is more intelligent, the positioning of the electrode patch is more accurate, the optimal electric field scheme is formed at the tumor part, the electrode array wearing scheme of a patient is generated, and the obtained electromagnetic curative effect is better.
Description
Technical Field
The invention belongs to the technical field of medical treatment, and particularly relates to an electrode patch positioning method based on head tumor treatment.
Background
Brain gliomas are malignant tumors derived from neuroepithelial tissues, commonly known as "brain cancers". Brain glioma is the most common intracranial primary tumor, and foreign clinical statistics show that the incidence rate of intracranial primary tumors is 21/10 ten thousand, and glioma accounts for about 60%. Domestic literature reports that brain glioma accounts for about 35.26% -60.96% of intracranial tumors. The most common treatment method at present is surgery and radiotherapy and chemotherapy, and the brain glioma grows infiltratively, so the surgery is often difficult to completely cut. And because the tumor is a radiation-resistant tumor and is resistant to most chemotherapeutics, the overall curative effect is poor, especially high-grade glioma has the growth characteristics of high degree anaplasia, the postoperative recurrence is fast, the prognosis is poor, and the health of human beings is seriously threatened.
The use of electric fields and currents to treat neurological disorders and brain diseases is becoming widespread. Examples of such treatments include, but are not limited to: transcranial Direct Current Stimulation (TDCS), Transcranial Magnetic Stimulation (TMS), and tumor therapy field (TTField). These treatments rely on the delivery of low frequency electromagnetic fields to targeted areas within the brain. For example, Woods et al, clinical neurophysiology, 1271031-1048 (2016), review technical aspects of TDCS; and Thielscher et al, "proceedings of conference," Institute of Electrical and Electronics Engineers (IEEE), institute of medical and biological engineers, 222-225 (2015), which teaches methods for simulating TMS. As another example, Miranda et al, Physics in medicine and biology, 594137-4147 (2014), teaches the creation of a computational head model of a healthy individual that simulates the delivery of TTField using a Magnetic Resonance Imaging (MRI) dataset, where the model creation is performed in a semi-automated manner. Furthermore, Wenger et al, Physics in medicine and biology, 607339-7357 (2015), teaches a method for creating a computational head model of a healthy individual to simulate the delivery of TTField, where the model is created from an MRI dataset of a healthy individual.
However, the position of the head electrode patch array is inaccurate, so that the curative effect of treating head tumors by using electric fields and electric currents is influenced.
Disclosure of Invention
The invention aims to provide an electrode patch positioning method based on head tumor treatment, which is determined by utilizing tumor coordinates and a mode of simulating the maximum electric field generated at a tumor part.
The technical scheme adopted by the invention is as follows:
An electrode patch positioning method based on head tumor treatment is characterized by comprising the following steps:
(1) Constructing a head tumor image training sample set: extracting image texture features and shape features of the obtained head CT image containing the tumor; the tumor identification BP neural network identifies whether the human organ CT image contains suspected tumor or not according to the image texture characteristics; marking each CT image pixel by pixel, and packaging the CT images and the corresponding marked CT images to form a training sample; selecting M training samples from the obtained total tumor target samples, requiring the training samples to contain tumors of different types as much as possible, and manually judging and marking the attributes of the training samples; training a tumor recognition BP neural network by adopting a CT image training sample set to obtain a head tumor image training sample set;
(2) Acquiring a three-dimensional image of the head of a patient by utilizing CT (computed tomography), and extracting image texture characteristics and shape characteristics;
(3) the identification of the head tumor comprises the steps of intercepting a rectangular area containing a tumor target according to the textural features of the tumor target image, rotating the tumor target by- α degrees based on the orientation angle α of the tumor target obtained by detecting the tumor target to obtain a horizontal tumor target sample, enveloping the horizontal tumor target by using a minimum rectangle, intercepting the minimum rectangle to obtain a horizontal sample target slice of the tumor target, and marking the horizontal sample target slice as a test sample;
(4) Determining the coordinates of the tumor: establishing head characteristics of a patient for modeling to obtain a head model of the patient, and forming digital coordinates of the head model of the patient in a three-dimensional space; inputting the head tumor target identified in the step (3) into a head model of the patient, so as to obtain a coordinate position of the tumor in the head model of the patient;
(5) According to the principle that the conductivity and the resistivity of a human body and the electromagnetic action center position of an electrode patch array coincide with the center position of a head tumor, a neural network algorithm is adopted, the electrode patch array is input into a head model of a patient to carry out finite element analysis, a maximum electric field generated at the tumor part is simulated and formed, and then the specific coordinate position of each electrode patch in the electrode patch array at the head is obtained according to the distribution of the electric field; the electromagnetic direction of each electrode patch in the electrode patch array is orthogonally arranged;
(6) Converting the specific coordinate position of each electrode patch on the head into the head position of the patient;
And completing electrode patch positioning based on head tumor treatment.
Further, the tumor identification BP neural network comprises 1 input layer, 1 hidden layer and 1 output layer, the texture feature vector and the shape feature parameter of the area where the suspected focus is located are used as neurons of the input layer, the number of nodes of the hidden layer is set to be 2 times of the number of the neurons of the input layer according to the prior experience, and the output layer comprises 1 node and three output values; the three output values respectively represent the lesion in the CT image as benign tumor, malignant tumor and non-tumor.
Further, in the step (4), the brain is subdivided into 5 large divisions according to parietal lobe, frontal lobe, occipital lobe, temporal lobe and islet lobe of a human brain structure, and then the brain is subdivided into left parietal lobe, right parietal lobe, left frontal lobe, right frontal lobe, left occipital lobe, right occipital lobe, left temporal lobe, right temporal lobe, left islet lobe and right islet lobe according to tumor positions; A3D head model is generated from the patient's brain structure.
Furthermore, a three-dimensional image is formed in the software by the brain model of the patient, a highlight display area is formed in the model according to the size of the tumor part of the patient, the tumor of the patient is generated in a color with obvious contrast, and the position of the tumor on the head is displayed in a three-dimensional mode.
Further, a plurality of pulse electrodes of the electrode patch output pulse voltages in a cyclic single or multiple manner.
Compared with the prior art, the invention has the following beneficial effects:
1. According to the invention, the head tumor image training sample set and the tumor recognition BP neural network are adopted, so that the collected samples are more intelligent and the accuracy is higher;
2. The invention adopts a neural network algorithm, inputs the electrode patch array into a head model of a patient for finite element analysis, simulates and forms a maximum electric field generated at a tumor part, and then obtains the specific coordinate position of each electrode patch in the electrode patch array at the head according to the distribution of the electric field, which is more in line with the human body and the electromagnetic principle, so that the positioning of the electrode patches is more accurate and the electromagnetic curative effect is better.
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FIG. 1 is a schematic diagram of an embodiment employing the present invention.
Detailed Description
The present invention will be further described with reference to fig. 1 and the following detailed description.
An electrode patch positioning method based on head tumor treatment is characterized by comprising the following steps:
(1) Constructing a head tumor image training sample set: extracting image texture features and shape features of the obtained head CT image containing the tumor; the tumor identification BP neural network identifies whether the human organ CT image contains suspected tumor or not according to the image texture characteristics; marking each CT image pixel by pixel, and packaging the CT images and the corresponding marked CT images to form a training sample; selecting M training samples from the obtained total tumor target samples, requiring the training samples to contain tumors of different types as much as possible, and manually judging and marking the attributes of the training samples; training a tumor recognition BP neural network by adopting a CT image training sample set to obtain a head tumor image training sample set;
The tumor recognition BP neural network comprises 1 input layer, 1 hidden layer and 1 output layer, the texture feature vector and the shape feature parameter of the area where the suspected focus is located are used as neurons of the input layer, the number of nodes of the hidden layer is set to be 2 times of the number of the neurons of the input layer according to the prior experience, and the output layer comprises 1 node and three output values; the three output values respectively represent the lesion in the CT image as benign tumor, malignant tumor and non-tumor.
The texture feature is a visual feature reflecting homogeneity phenomenon in an image, and embodies the surface structure organization arrangement attribute with slow change or periodic change of the surface of an object; texture has three major landmarks: some local sequence is repeated continuously, non-random arrangement and uniform unity in the texture region, and can be extracted by adopting a statistical method, a model method and the like.
In an embodiment, the CT image may be replaced with an ultrasound image or an MRI image.
Selecting a diagnosed CT image from a CT image training sample set in an image library, performing iterative training on a tumor identification BP neural network, wherein each training sample number comprises 50 normal CT image samples and 50 CT image samples containing suspected tumors, and after each training, selecting 20 normal CT image samples and 20 CT image samples containing suspected lesions, which have been diagnosed, to perform inspection until the identification error is less than 0.2%. Compared with the prior art, the method and the device can improve the identification accuracy of the abnormal CT image.
(2) Acquiring a three-dimensional image of the head of a patient by utilizing CT (computed tomography), and extracting image texture characteristics and shape characteristics;
(3) the identification of the head tumor comprises the steps of intercepting a rectangular area containing a tumor target according to the textural features of the tumor target image, rotating the tumor target by- α degrees based on the orientation angle α of the tumor target obtained by detecting the tumor target to obtain a horizontal tumor target sample, enveloping the horizontal tumor target by using a minimum rectangle, intercepting the minimum rectangle to obtain a horizontal sample target slice of the tumor target, and marking the horizontal sample target slice as a test sample;
(4) Determining the coordinates of the tumor: establishing head characteristics of a patient for modeling to obtain a head model of the patient, and forming digital coordinates of the head model of the patient in a three-dimensional space; inputting the head tumor target identified in the step (3) into a head model of the patient, so as to obtain a coordinate position of the tumor in the head model of the patient;
In the step (4), the brain is subdivided into 5 large divisions according to parietal lobe, frontal lobe, occipital lobe, temporal lobe and islet lobe of a human brain structure, and then the brain is subdivided into left parietal lobe, right parietal lobe, left frontal lobe, left occipital lobe, right occipital lobe, left temporal lobe, right temporal lobe, left islet lobe and right islet lobe according to tumor positions; A3D head model is generated from the patient's brain structure.
(5) According to the principle that the conductivity and the resistivity of a human body and the electromagnetic action center position of an electrode patch array coincide with the center position of a head tumor, a neural network algorithm is adopted, the electrode patch array is input into a head model of a patient to carry out finite element analysis, a maximum electric field generated at the tumor part is simulated and formed, and then the specific coordinate position of each electrode patch in the electrode patch array at the head is obtained according to the distribution of the electric field; the electromagnetic direction of each electrode patch in the electrode patch array is orthogonally arranged;
The brain model of the patient forms a stereoscopic image in software, a highlighted display area is formed in the model according to the size of the tumor part of the patient, the tumor of the patient is generated in a color with obvious contrast, and the position of the tumor on the head is displayed stereoscopically.
The pulse electrodes of the electrode patch output pulse voltages in a cyclic single or multiple manner.
(6) Converting the specific coordinate position of each electrode patch on the head into the head position of the patient;
And completing electrode patch positioning based on head tumor treatment.
In specific application, as shown in fig. 1, under the condition that the head tumor image training sample set is constructed, the information of the patient and the like are input into a software system, and the three-dimensional image information of the head of the patient is led into a tumor recognition BP neural network for tumor recognition and modeling is performed according to the head characteristic part characteristic information of the patient, so that the coordinate position of the tumor in the head model of the patient is obtained; and (5) analyzing and forming an electric field optimal solution according to the scheme in the step (5), forming a distribution scheme of the electrode array according to the electric field optimal solution, and generating a report of the specific coordinate position of each electrode patch in the electrode patch array at the head for medical staff and patients to use.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. An electrode patch positioning method based on head tumor treatment is characterized by comprising the following steps:
(1) Constructing a head tumor image training sample set: extracting image texture features and shape features of the obtained head CT image containing the tumor; the tumor identification BP neural network identifies whether the human organ CT image contains suspected tumor or not according to the image texture characteristics; marking each CT image pixel by pixel, and packaging the CT images and the corresponding marked CT images to form a training sample; selecting M training samples from the obtained total tumor target samples, requiring the training samples to contain tumors of different types as much as possible, and manually judging and marking the attributes of the training samples; training a tumor recognition BP neural network by adopting a CT image training sample set to obtain a head tumor image training sample set;
(2) Acquiring a three-dimensional image of the head of a patient by utilizing CT (computed tomography), and extracting image texture characteristics and shape characteristics;
(3) the identification of the head tumor comprises the steps of intercepting a rectangular area containing a tumor target according to the textural features of the tumor target image, rotating the tumor target by- α degrees based on the orientation angle α of the tumor target obtained by detecting the tumor target to obtain a horizontal tumor target sample, enveloping the horizontal tumor target by using a minimum rectangle, intercepting the minimum rectangle to obtain a horizontal sample target slice of the tumor target, and marking the horizontal sample target slice as a test sample;
(4) Determining the coordinates of the tumor: establishing head characteristics of a patient for modeling to obtain a head model of the patient, and forming digital coordinates of the head model of the patient in a three-dimensional space; inputting the head tumor target identified in the step (3) into a head model of the patient, so as to obtain a coordinate position of the tumor in the head model of the patient;
(5) According to the principle that the conductivity and the resistivity of a human body and the electromagnetic action center position of an electrode patch array coincide with the center position of a head tumor, a neural network algorithm is adopted, the electrode patch array is input into a head model of a patient to carry out finite element analysis, a maximum electric field generated at the tumor part is simulated and formed, and then the specific coordinate position of each electrode patch in the electrode patch array at the head is obtained according to the distribution of the electric field; the electromagnetic direction of each electrode patch in the electrode patch array is orthogonally arranged;
(6) Converting the specific coordinate position of each electrode patch on the head into the head position of the patient;
And completing electrode patch positioning based on head tumor treatment.
2. The electrode patch positioning method for head tumor treatment according to claim 1, wherein: the tumor recognition BP neural network comprises 1 input layer, 1 hidden layer and 1 output layer, the texture feature vector and the shape feature parameter of the area where the suspected focus is located are used as neurons of the input layer, the number of nodes of the hidden layer is set to be 2 times of the number of the neurons of the input layer according to the prior experience, and the output layer comprises 1 node and three output values; the three output values respectively represent the lesion in the CT image as benign tumor, malignant tumor and non-tumor.
3. The electrode patch positioning method for head tumor treatment according to claim 2, wherein: in the step (4), the brain is subdivided into 5 large divisions according to parietal lobe, frontal lobe, occipital lobe, temporal lobe and islet lobe of a human brain structure, and then the brain is subdivided into left parietal lobe, right parietal lobe, left frontal lobe, left occipital lobe, right occipital lobe, left temporal lobe, right temporal lobe, left islet lobe and right islet lobe according to tumor positions; A3D head model is generated from the patient's brain structure.
4. The electrode patch positioning method for head tumor treatment according to claim 3, wherein: the brain model of the patient forms a stereoscopic image in software, a highlighted display area is formed in the model according to the size of the tumor part of the patient, the tumor of the patient is generated in a color with obvious contrast, and the position of the tumor on the head is displayed stereoscopically.
5. The electrode patch positioning method for head tumor treatment according to claim 4, wherein: the pulse electrodes of the electrode patch output pulse voltages in a cyclic single or multiple manner.
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| CN117679635A (en) * | 2024-02-04 | 2024-03-12 | 湖南安泰康成生物科技有限公司 | Electrode slice operation control method, tumor electric field therapeutic apparatus and storage medium |
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Application publication date: 20200717 |