CN113299391B - Risk assessment method for remote thyroid nodule ultrasound image - Google Patents

Risk assessment method for remote thyroid nodule ultrasound image Download PDF

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CN113299391B
CN113299391B CN202110567911.2A CN202110567911A CN113299391B CN 113299391 B CN113299391 B CN 113299391B CN 202110567911 A CN202110567911 A CN 202110567911A CN 113299391 B CN113299391 B CN 113299391B
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CN113299391A (en
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李玉宏
胡树煜
王亮
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Abstract

The invention provides a risk assessment method for remote thyroid nodule ultrasonic images, which comprises the steps of acquiring ultrasonic images of thyroid parts and uploading the ultrasonic images to a cloud platform; after the cloud platform acquires the ultrasonic image, judging whether thyroid nodules exist in the ultrasonic image; a sample evaluation value C is set in the cloud platform, the cloud platform diagnoses the grade of the thyroid nodule according to the related information of the thyroid nodule, and risk assessment is carried out on the thyroid nodule subjected to grade diagnosis through the sample evaluation value C; the cloud platform transmits diagnosis and risk assessment results of thyroid nodules back to the PACS system of the hospital. According to the invention, the cloud platform is connected with the PACS system of the hospital, after the ultrasonic image of the thyroid part is acquired, the ultrasonic image is uploaded into the cloud platform, and diagnosis and risk assessment are carried out on the thyroid nodule by the ultrasonic image through the cloud platform, so that the diagnosis efficiency can be greatly improved, and the occurrence of misdiagnosis during manual diagnosis is avoided.

Description

Risk assessment method for remote thyroid nodule ultrasound image
Technical Field
The invention relates to the technical field of ultrasonic imaging, in particular to a risk assessment method of remote thyroid nodule ultrasonic imaging.
Background
At present, in recent years, the incidence rate of thyroid cancer in the global scope is rapidly increased, and according to the data of the national tumor registration center, the incidence rate of female thyroid cancer in urban areas in China is shown to be located at the 4 th position of all malignant tumors of females. Thyroid cancer in China continues to grow at a rate of 20% per year. Ultrasound examination is the preferred imaging examination in thyroid disease diagnostic systems. In general population, the detection rate of thyroid nodules can reach 20-76% by applying ultrasonic examination, and 5-15% of the nodules are malignant. With the advent of accurate medicine and big data, patient uniqueness and node diversity determine tumor heterogeneity that necessarily requires personalized treatment. The job responsibilities of the clinician, whether abroad or domestically, include reporting diagnostics, selecting review, reporting consultation, data querying, etc. However, the foreign ultrasound department belongs to the imaging department, the staff is divided into an ultrasound technician and a sonographer, the technician is responsible for scanning examination and data summarization of patients, the doctor performs diagnosis and consultation, the two are definite in division, the workload and the working intensity are relatively low, and the timeliness requirement for reporting is not as high as domestic. The domestic ultrasonic department is an independent platform department, and because ultrasonic examination has the advantages of convenience, economy, no damage, no radiation, quick report and the like, the requirements of clinicians and patients on ultrasonic examination diagnosis are increased sharply, so that the information quantity of ubiquitous ultrasonic medical data is large, the workload, the working intensity and the responsibility risk of the ultrasonic doctors are large, and meanwhile, the ultrasonic diagnosis is very dependent on the personal level, the clinical experience and the type definition of the ultrasonic doctors. And high-level sonographers in China are mostly concentrated in large trimethyl hospitals, diagnosis levels are uneven among different areas and hospitals, subjective difference of the diagnosis levels is large, and quality lacks of unified standards.
How to rapidly and effectively diagnose and evaluate thyroid nodule becomes a urgent problem to be solved.
Disclosure of Invention
In view of the above, the invention provides a risk assessment method for remote thyroid nodule ultrasound images, which aims to solve the problem of lower diagnosis efficiency caused by how to reduce the dependence of ultrasound diagnosis on a sonographer when performing ultrasound diagnosis of thyroid nodules.
In one aspect, the invention provides a risk assessment method for remote thyroid nodule ultrasound images, comprising the following steps:
step S1: acquiring an ultrasonic image of a thyroid position and uploading the ultrasonic image to a cloud platform;
step S2: after the ultrasonic image is acquired by the cloud platform, the ultrasonic image is subjected to data processing by a processing unit arranged in the cloud platform so as to judge whether thyroid nodules exist in the ultrasonic image:
if the thyroid nodule does not exist, the cloud platform makes a diagnosis of the thyroid gland being normal;
if the thyroid nodule exists, the cloud platform acquires information related to the thyroid nodule in the ultrasonic image and carries out subsequent steps;
step S3: a sample evaluation value C is set in the cloud platform, the cloud platform diagnoses the grade of the thyroid nodule according to the related information of the thyroid nodule, and risk assessment is carried out on the thyroid nodule after grade diagnosis through the sample evaluation value C;
Step S4: the cloud platform outputs diagnosis and risk assessment results of the thyroid nodule and transmits the diagnosis and risk assessment results of the thyroid nodule back to a PACS system (Picture Archiving and Communication Systems, image archiving and communication system) of a hospital;
in the step S3, after the processing unit detects the thyroid nodule from the ultrasound image, the processing module is configured to determine a property B and an aspect ratio D of the thyroid nodule, and the processing module is further configured to obtain an echo value Δa of the thyroid nodule, establish a preset echo value matrix a, and set a (A1, A2, A3, A4), where A1 is a first preset echo value, A2 is a second preset echo value, A3 is a third preset echo value, A4 is a fourth preset echo value, and A1 is greater than A2 and less than A3 and less than A4; the processing module performs grade judgment on the thyroid nodule according to the character B, the aspect ratio D and the echo value delta A of the thyroid nodule:
when A3 is less than delta A4 or delta A is more than or equal to A4, if the character B of the thyroid nodule is a cystic nodule and the aspect ratio D of the thyroid nodule is less than 1, judging the grade of the thyroid nodule as a first grade, and calculating a first sampling evaluation value C01 of the thyroid nodule;
When A2 is less than delta A and less than or equal to A3, if the character B of the thyroid nodule is a cystic nodule and the aspect ratio D of the thyroid nodule is less than 1, judging the grade of the thyroid nodule is a second grade, and calculating a second sampling evaluation value C02 of the thyroid nodule;
when A1 is less than delta A and less than or equal to A2, if the character B of the thyroid nodule is a solid nodule and the aspect ratio D of the thyroid nodule is less than 1, judging the grade of the thyroid nodule is three-grade, and calculating a third sampling evaluation value C03 of the thyroid nodule;
when delta A is less than or equal to A1, if the character B of the thyroid nodule is a solid nodule and the aspect ratio D of the thyroid nodule is more than 1, judging the grade of the thyroid nodule is four grades, and calculating a fourth sampling evaluation value C04 of the thyroid nodule;
and when the i-th sampling evaluation value C0i is less than C, the grade of the thyroid nodule is reduced by one step, the lowest grade of the thyroid nodule is one step, and the thyroid nodule is judged to be a low-risk nodule, and when the i-th sampling evaluation value C0i is more than C, the grade of the thyroid nodule is increased by one step, and the thyroid nodule is judged to be a high-risk nodule, wherein i=1, 2,3 and 4.
Further, the i-th sampling evaluation value c0i=Δa/a0+b/b0+d/d0+e/e0+f/F0, the sample evaluation value c=Δaa/a0+bb/b0+dd/d0+ee/e0+ff/F0, wherein Δa is an echo value of the thyroid nodule, A0 is a preset standard echo value of the thyroid nodule, Δaa is a preset echo value of the thyroid nodule, B is a property of the thyroid nodule, b=1 when the property of the thyroid nodule is a cystic nodule, b=2 when the property of the thyroid nodule is a solid nodule, b0=1, bb=1.5, D is an aspect ratio of the thyroid nodule, d0=1, dd=0.5, E is a calcified area percentage of the thyroid nodule, e0=10%, ee=20%, F is a ratio of the thyroid area of the thyroid nodule, and a preset ratio of the perimeter of the thyroid nodule to the perimeter of the thyroid nodule.
Further, the processing module obtains an area S1 of the thyroid nodule and a calcification area S2 of the thyroid nodule after the detected thyroid nodule, e=s2/s1×100%;
the processing module adjusts the grade of the thyroid nodule according to the calcified area percentage E of the thyroid nodule and the standard calcified area percentage E0 of the preset thyroid nodule,
When E is more than E0, the grade of the thyroid nodule is increased by one grade;
when E is less than E0, reducing the grade of the thyroid nodule by one step;
when e=e0, no adjustment is made to the grade of the thyroid nodule.
Further, the processing module is further configured to obtain a perimeter Z of the thyroid nodule after the detected thyroid nodule, and calculate a ratio F of the perimeter to the area of the thyroid nodule according to a ratio between the perimeter Z of the thyroid nodule and the area S1 of the thyroid nodule, f=z/S1, wherein a preset area S3 and a preset perimeter Z1 of the thyroid nodule are set in the processing module, and a preset standard ratio f0=z1/S3 of the perimeter to the area of the thyroid nodule;
the processing module adjusts the grade of the thyroid nodule after the calcification area percentage of the thyroid nodule is adjusted again according to the relation between the ratio F of the perimeter to the area of the thyroid nodule and the preset standard ratio F0 of the perimeter to the area of the thyroid nodule:
when E is more than E0, after the grade of the thyroid nodule is judged to be increased by one step, if F is more than F0, the grade of the thyroid nodule is increased by one step, if F is less than F0, the grade of the thyroid nodule is reduced by one step, and if F=F0, the grade of the thyroid nodule is not adjusted;
When E < E0, reducing the grade of the thyroid nodule by one step, if F > F0, increasing the grade of the thyroid nodule by one step, if F < F0, reducing the grade of the thyroid nodule by one step, and if F=F0, not adjusting the grade of the thyroid nodule;
when e=e0, the grade of the thyroid nodule is not adjusted, and then, if F > F0, the grade of the thyroid nodule is increased by one step, if F < F0, the grade of the thyroid nodule is decreased by one step, and if f=f0, the grade of the thyroid nodule is not adjusted.
Further, a compensation coefficient matrix K is set in the processing module, and the processing module compensates the i-th sampling evaluation value C0i through each compensation coefficient in the compensation coefficient matrix K.
Further, setting K (K1, K2, K3, K4) for the compensation coefficient matrix K, wherein K1 is a first compensation coefficient, K2 is a second compensation coefficient, K3 is a third compensation coefficient, K4 is a fourth compensation coefficient, and K1 is more than 1 and less than K2 and K3 is more than 1.5; the processing module is also internally provided with a preset calcification area percentage matrix Ee0 of thyroid nodules, ee0 (Ee 1, ee2, ee3 and Ee 4) is set, wherein Ee1 is a first preset calcification area percentage, ee2 is a second preset calcification area percentage, ee3 is a third preset calcification area percentage, ee4 is a fourth preset calcification area percentage, ee1 is more than Ee2 and less than Ee3 and less than Ee4;
The processing module determines a compensation coefficient according to the relation between the preset calcification area percentage Ee of the thyroid nodule and each preset calcification area percentage to compensate the ith sampling evaluation value C0 i:
when Ee is smaller than Ee1, selecting the first compensation coefficient K1 to compensate the i-th sampling evaluation value C0i, wherein the i-th sampling evaluation value C0i after compensation is C0i x K1;
when Ee1 is less than or equal to Ee2, selecting the second compensation coefficient K2 to compensate the ith sampling evaluation value C0i, wherein the compensated ith sampling evaluation value C0i is C0i x K2;
when Ee2 is less than or equal to Ee3, selecting the third compensation coefficient K3 to compensate the ith sampling evaluation value C0i, wherein the compensated ith sampling evaluation value C0i is C0i x K3;
when Ee3 is less than or equal to Ee4, selecting the fourth compensation coefficient K4 to compensate the ith sampling evaluation value C0i, wherein the compensated ith sampling evaluation value C0i is C0i x K4.
Further, a correction coefficient matrix T is set in the processing module, and the processing module corrects the i-th sampling evaluation value C0i compensated by the compensation coefficient matrix K through each correction coefficient in the correction coefficient matrix T.
Further, for the correction coefficient matrix T, T (T1, T2, T3, T4), T1 is a first correction coefficient, T2 is a second correction coefficient, T3 is a third correction coefficient, T4 is a fourth correction coefficient, and 0.9 < T1 < T2 < 1 < T3 < T4 < 1.1; the processing module is also internally provided with a preset ratio matrix Ff0 of the perimeter and the area of the thyroid nodule, ff0 (Ff 1, ff2, ff3 and Ff 4) is set, wherein Ff1 is the ratio of the first preset perimeter to the area, ff2 is the ratio of the second preset perimeter to the area, ff3 is the ratio of the third preset perimeter to the area, ff4 is the ratio of the fourth preset perimeter to the area, and Ff1 is more than Ff2 and less than Ff3 and less than Ff4;
the processing module determines a correction coefficient according to the relation between the preset ratio Ff of the perimeter to the area of the thyroid nodule and the ratio of each preset perimeter to the area so as to correct the ith sampling evaluation value C0i after compensation by the compensation coefficient:
when Ff is smaller than Ff1, selecting the first correction coefficient T1 to correct the compensated i-th sampling evaluation value C0i×k1, where the corrected i-th sampling evaluation value C0i×k1×t1;
when Ff1 is less than or equal to Ff < Ff2, selecting the second correction coefficient T2 to correct the compensated i-th sampling evaluation value C0i x K2, where the corrected i-th sampling evaluation value C0i x K2 x T2;
When Ff2 is less than or equal to Ff < Ff3, selecting the third correction coefficient T3 to correct the compensated i-th sampling evaluation value C0i×k3, where the corrected i-th sampling evaluation value C0i×k3×t3;
and when Ff3 is less than or equal to Ff4, selecting the fourth correction coefficient T4 to correct the compensated i-th sampling evaluation value C0i x K4, wherein the corrected i-th sampling evaluation value C0i x K4 x T4.
Further, a preset thyroid nodule area correction matrix P0 is set in the processing module, P0 (P1, P2, P3, P4) is set, wherein P1 is a first preset area correction coefficient, P2 is a second preset area correction coefficient, P3 is a third preset area correction coefficient, P4 is a fourth preset area correction coefficient, and P1 is more than 1 and less than P2 and P3 is more than 1.2;
the processing module is also used for obtaining the average value delta q of the gray values of the thyroid nodule, and the processing module is also pre-provided with the standard gray average value q0 of the thyroid nodule, and selects the preset area correction coefficient to correct the area S1 of the thyroid nodule according to the relation between the average value delta q of the gray values and the standard gray average value q0 and the relation between the perimeter and the area ratio F of the thyroid nodule and the preset standard ratio F0 of the perimeter and the area of the thyroid nodule,
When F is smaller than F0 and delta q is larger than q0, the first preset area correction coefficient P1 is selected to correct the area S1 of the thyroid nodule, and the corrected area of the thyroid nodule is S1P 1;
when F is smaller than F0 and delta q is smaller than or equal to q0, selecting the second preset area correction coefficient P2 to correct the area S1 of the thyroid nodule, wherein the area of the thyroid nodule after correction is S1P 2;
when F is larger than F0 and deltaq is larger than q0, selecting the third preset area correction coefficient P3 to correct the area S1 of the thyroid nodule, wherein the corrected area of the thyroid nodule is S1P 3;
when F is larger than F0 and deltaq is smaller than or equal to q0, selecting the fourth preset area correction coefficient P4 to correct the area S1 of the thyroid nodule, wherein the corrected area of the thyroid nodule is S1P 4;
after determining the area after thyroid nodule correction, the area after thyroid nodule correction is S1 Pi as the area of the thyroid nodule, and the calculations of E, E, ee, F0, and Ff are performed, i=1, 2,3,4.
Compared with the prior art, the diagnosis method has the beneficial effects that the cloud platform is connected with the PACS system of the hospital, after the PACS system of the hospital acquires the ultrasonic image of the thyroid gland, the ultrasonic image is uploaded into the cloud platform, and diagnosis and risk assessment are carried out on the thyroid nodule by the ultrasonic image through the cloud platform, so that the diagnosis efficiency can be greatly improved, and the occurrence of misdiagnosis during manual diagnosis is avoided.
Further, the detected characteristics, echo value, aspect ratio, calcification area and ratio of circumference to area of the thyroid nodule are respectively subjected to ratio summation calculation with preset standard values to obtain sampling evaluation values, sample evaluation values are calculated according to the sum of the ratios between the corresponding preset values and the preset standard values, and the risk of the thyroid nodule is estimated according to the magnitude relation between the sampling evaluation values and the calculated sample evaluation values, so that the accuracy of risk estimation can be greatly improved.
Further, by adjusting the grade of the thyroid nodule according to the calcification area percentage of the thyroid nodule, when the calcification area of the thyroid nodule exceeds a preset standard value, the grade of the thyroid nodule can be timely adjusted, so that the accuracy of a diagnosis result and the accuracy of subsequent risk assessment are improved.
Further, by adjusting the grade of the thyroid nodule again according to the relation between the ratio of the perimeter to the area of the thyroid nodule and the preset standard ratio, the accuracy of grade judgment of the thyroid nodule can be greatly improved, and the accuracy of a subsequent risk assessment result can be greatly improved.
Further, by selecting a corresponding compensation coefficient according to the calcification area percentage of the thyroid nodule to compensate the sampling evaluation value, an influence relation is established between the sampling evaluation value and the calcification area percentage of the thyroid nodule, namely, the final sampling evaluation value is influenced by the calcification area percentage of the thyroid nodule, so that the final result of the sampling evaluation value is more accurate, the condition degree of the thyroid nodule can be accurately reflected by the sampling evaluation value, and the risk degree of the thyroid nodule can be more accurately evaluated by the sampling evaluation value.
The correction coefficient is determined according to the relation between the preset ratio Ff of the perimeter to the area of the thyroid nodule and the ratio of each preset perimeter to the area, so that the ith sampling evaluation value C0i after being compensated by the compensation coefficient is corrected, the calculation of the sampling evaluation value is more accurate after the ith sampling evaluation value C0i after being compensated is corrected by the correction coefficient, and the acquisition of the sampling evaluation value is influenced by the ratio of the perimeter to the area of the thyroid nodule, so that the thyroid nodule can be effectively evaluated according to the complexity degree of the outline of the thyroid nodule, and the accuracy of the risk evaluation result of the thyroid nodule can be improved.
Further, by correcting the area of the thyroid nodule, the accuracy of the results of the ratio F of the perimeter to the area of the thyroid nodule, the preset standard ratio F0 of the perimeter to the area of the thyroid nodule, the preset ratio Ff of the perimeter to the area of the thyroid nodule, the calcified area percentage E of the thyroid nodule, the standard calcified area percentage E0 of the thyroid nodule and the preset calcified area percentage Ee of the thyroid nodule can be greatly improved, and the subsequent diagnosis and apple efficiency can be improved.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 is a flowchart of a risk assessment method for remote thyroid nodule ultrasound images provided in an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a risk assessment system for remote thyroid nodule ultrasound images according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
Referring to fig. 1, the embodiment provides a risk assessment method for remote thyroid nodule ultrasound images, which includes the following steps:
step S1: acquiring an ultrasonic image of a thyroid position and uploading the ultrasonic image to a cloud platform;
step S2: after the ultrasonic image is acquired by the cloud platform, the ultrasonic image is subjected to data processing by a processing unit arranged in the cloud platform so as to judge whether thyroid nodules exist in the ultrasonic image:
if the thyroid nodule does not exist, the cloud platform makes a diagnosis of the thyroid gland being normal;
If the thyroid nodule exists, the cloud platform acquires information related to the thyroid nodule in the ultrasonic image and carries out subsequent steps;
step S3: a sample evaluation value C is set in the cloud platform, the cloud platform diagnoses the grade of the thyroid nodule according to the related information of the thyroid nodule, and risk assessment is carried out on the thyroid nodule after grade diagnosis through the sample evaluation value C;
step S4: the cloud platform outputs the diagnosis and risk assessment results of the thyroid nodule and transmits the diagnosis and risk assessment results of the thyroid nodule back to a PACS system of a hospital;
in the step S3, after the processing unit detects the thyroid nodule from the ultrasound image, the processing module is configured to determine a property B and an aspect ratio D of the thyroid nodule, and the processing module is further configured to obtain an echo value Δa of the thyroid nodule, establish a preset echo value matrix a, and set a (A1, A2, A3, A4), where A1 is a first preset echo value, A2 is a second preset echo value, A3 is a third preset echo value, A4 is a fourth preset echo value, and A1 is greater than A2 and less than A3 and less than A4; the processing module performs grade judgment on the thyroid nodule according to the character B, the aspect ratio D and the echo value delta A of the thyroid nodule:
When A3 is less than delta A4 or delta A is more than or equal to A4, if the character B of the thyroid nodule is a cystic nodule and the aspect ratio D of the thyroid nodule is less than 1, judging the grade of the thyroid nodule as a first grade, and calculating a first sampling evaluation value C01 of the thyroid nodule;
when A2 is less than delta A and less than or equal to A3, if the character B of the thyroid nodule is a cystic nodule and the aspect ratio D of the thyroid nodule is less than 1, judging the grade of the thyroid nodule is a second grade, and calculating a second sampling evaluation value C02 of the thyroid nodule;
when A1 is less than delta A and less than or equal to A2, if the character B of the thyroid nodule is a solid nodule and the aspect ratio D of the thyroid nodule is less than 1, judging the grade of the thyroid nodule is three-grade, and calculating a third sampling evaluation value C03 of the thyroid nodule;
when delta A is less than or equal to A1, if the character B of the thyroid nodule is a solid nodule and the aspect ratio D of the thyroid nodule is more than 1, judging the grade of the thyroid nodule is four grades, and calculating a fourth sampling evaluation value C04 of the thyroid nodule;
and when the i-th sampling evaluation value C0i is less than C, the grade of the thyroid nodule is reduced by one step, the lowest grade of the thyroid nodule is one step, and the thyroid nodule is judged to be a low-risk nodule, and when the i-th sampling evaluation value C0i is more than C, the grade of the thyroid nodule is increased by one step, and the thyroid nodule is judged to be a high-risk nodule, wherein i=1, 2,3 and 4.
Specifically, after the processing unit detects the thyroid nodule from the ultrasound image, the processing module is configured to obtain calcification information of the thyroid nodule and perimeter and area information of the thyroid nodule.
It can be seen that, in this embodiment, the cloud platform is connected with the PACS system of the hospital, and when the PACS system of the hospital obtains the ultrasonic image of the thyroid site, the ultrasonic image is uploaded into the cloud platform, and diagnosis and risk assessment are performed on the thyroid nodule by the ultrasonic image through the cloud platform, so that the diagnosis efficiency can be greatly improved, and misdiagnosis during manual diagnosis can be avoided.
Specifically, the i-th sampling evaluation value c0i=Δa/a0+b/b0+d/d0+e/e0+f/F0, the sample evaluation value c=Δaa/a0+bb/b0+dd/d0+ee/e0+ff/F0, wherein Δa is an echo value of the thyroid nodule, A0 is a preset standard echo value of the thyroid nodule, Δaa is a preset echo value of the thyroid nodule, B is a property of the thyroid nodule, b=1 when the property of the thyroid nodule is a cystic nodule, b=2 when the property of the thyroid nodule is a solid nodule, b0=1, bb=1.5, D is an aspect ratio of the thyroid nodule, d0=1, dd=0.5, E is an area percentage of calcification of the thyroid nodule, e0=10%, ee=20%, F is a ratio of a perimeter of the thyroid nodule to an area of the thyroid nodule, b=1 when the property of the thyroid nodule is a cystic nodule, b=2 when the property of the thyroid nodule is a solid nodule, b=1, D is a preset standard ratio of a perimeter of the thyroid nodule to an area of the thyroid nodule.
Specifically, after obtaining the area information of the thyroid nodule, the processing unit obtains the calcified area information of the thyroid nodule, obtains the calcified area percentage E of the thyroid nodule according to the calcified area of the thyroid nodule and the area of the thyroid nodule, and presets the standard calcified area percentage E0 of the thyroid nodule and the preset calcified area percentage Ee of the thyroid nodule.
It can be seen that, by calculating the sum of the detected characteristics, echo values, aspect ratios, calcification areas and the ratio of the circumferences to the areas of the thyroid nodules with preset standard values, respectively, a sampling evaluation value is obtained, a sample evaluation value is calculated by the sum of the ratios between the corresponding preset values and the preset standard values, and the risk of the thyroid nodules is estimated by the magnitude relation between the sampling evaluation value and the sample evaluation value, so that the accuracy of risk estimation can be greatly improved.
Specifically, the processing module obtains an area S1 of the thyroid nodule and a calcification area S2 of the thyroid nodule after the detected thyroid nodule, e=s2/s1×100%;
the processing module adjusts the grade of the thyroid nodule according to the calcified area percentage E of the thyroid nodule and the standard calcified area percentage E0 of the preset thyroid nodule,
When E is more than E0, the grade of the thyroid nodule is increased by one grade;
when E is less than E0, reducing the grade of the thyroid nodule by one step;
when e=e0, no adjustment is made to the grade of the thyroid nodule.
Specifically, when the thyroid nodule has a first-level grade, the thyroid nodule is lifted only when the thyroid nodule satisfies the lifting condition, and the grade lowering operation is not performed.
It can be seen that by adjusting the grade of the thyroid nodule according to the calcification area percentage of the thyroid nodule, the grade of the thyroid nodule can be adjusted in time when the calcification area of the thyroid nodule exceeds a preset standard value, so that the accuracy of a diagnosis result and the accuracy of subsequent risk assessment can be improved.
Specifically, the processing module is further configured to obtain a perimeter Z of the thyroid nodule after the detected thyroid nodule, and calculate a perimeter-to-area ratio F of the thyroid nodule according to a ratio between the perimeter Z of the thyroid nodule and an area S1 of the thyroid nodule, f=z/S1, wherein a preset area S3 and a preset perimeter Z1 of the thyroid nodule are set in the processing module, and a preset standard ratio f0=z1/S3 of the perimeter-to-area of the thyroid nodule;
The processing module adjusts the grade of the thyroid nodule after the calcification area percentage of the thyroid nodule is adjusted again according to the relation between the ratio F of the perimeter to the area of the thyroid nodule and the preset standard ratio F0 of the perimeter to the area of the thyroid nodule:
when E is more than E0, after the grade of the thyroid nodule is judged to be increased by one step, if F is more than F0, the grade of the thyroid nodule is increased by one step, if F is less than F0, the grade of the thyroid nodule is reduced by one step, and if F=F0, the grade of the thyroid nodule is not adjusted;
when E < E0, reducing the grade of the thyroid nodule by one step, if F > F0, increasing the grade of the thyroid nodule by one step, if F < F0, reducing the grade of the thyroid nodule by one step, and if F=F0, not adjusting the grade of the thyroid nodule;
when e=e0, the grade of the thyroid nodule is not adjusted, and then, if F > F0, the grade of the thyroid nodule is increased by one step, if F < F0, the grade of the thyroid nodule is decreased by one step, and if f=f0, the grade of the thyroid nodule is not adjusted.
It can be seen that by adjusting the grade of the thyroid nodule again according to the relationship between the ratio of the perimeter to the area of the thyroid nodule and the preset standard ratio, the accuracy of the grade judgment of the thyroid nodule can be greatly improved, and the accuracy of the subsequent risk assessment result can be greatly improved.
Specifically, a compensation coefficient matrix K is set in the processing module, and the processing module compensates the i-th sampling evaluation value C0i through each compensation coefficient in the compensation coefficient matrix K.
Specifically, for the compensation coefficient matrix K, K (K1, K2, K3, K4) is set, where K1 is a first compensation coefficient, K2 is a second compensation coefficient, K3 is a third compensation coefficient, K4 is a fourth compensation coefficient, and 1 < K2 < K3 < K4 < 1.5; the processing module is also internally provided with a preset calcification area percentage matrix Ee0 of thyroid nodules, ee0 (Ee 1, ee2, ee3 and Ee 4) is set, wherein Ee1 is a first preset calcification area percentage, ee2 is a second preset calcification area percentage, ee3 is a third preset calcification area percentage, ee4 is a fourth preset calcification area percentage, ee1 is more than Ee2 and less than Ee3 and less than Ee4;
the processing module determines a compensation coefficient according to the relation between the preset calcification area percentage Ee of the thyroid nodule and each preset calcification area percentage to compensate the ith sampling evaluation value C0 i:
When Ee is smaller than Ee1, selecting the first compensation coefficient K1 to compensate the i-th sampling evaluation value C0i, wherein the i-th sampling evaluation value C0i after compensation is C0i x K1;
when Ee1 is less than or equal to Ee2, selecting the second compensation coefficient K2 to compensate the ith sampling evaluation value C0i, wherein the compensated ith sampling evaluation value C0i is C0i x K2;
when Ee2 is less than or equal to Ee3, selecting the third compensation coefficient K3 to compensate the ith sampling evaluation value C0i, wherein the compensated ith sampling evaluation value C0i is C0i x K3;
when Ee3 is less than or equal to Ee4, selecting the fourth compensation coefficient K4 to compensate the ith sampling evaluation value C0i, wherein the compensated ith sampling evaluation value C0i is C0i x K4.
Specifically, the standard calcified area percentage E0 of the preset thyroid nodule is a standard reference value, that is, when the thyroid nodule is calcified, and the thyroid nodule is a normal nodule, the ratio between the area of the calcified area and the area of the nodule at this time is denoted as E0, that is, E0 represents the ratio between the area of the calcified area and the area of the nodule when the thyroid nodule is normal. The preset calcified area percentage Ee of the thyroid nodule is a preset reference value, the Ee is expressed as a maximum threshold value of the ratio between the area of the calcified area and the area of the nodule when the thyroid nodule is a normal nodule, the Ee can be obtained according to the data in the ratio between the area of the calcified area of the normal thyroid nodule and the area of the nodule in the past case data, and the Ee is obtained according to the preset maximum ratio.
It can be seen that, by selecting a corresponding compensation coefficient according to the calcification area percentage of the thyroid nodule to compensate the sampling evaluation value, an influence relationship is established between the sampling evaluation value and the calcification area percentage of the thyroid nodule, namely, the final sampling evaluation value is influenced by the calcification area percentage of the thyroid nodule, so that the final result of the sampling evaluation value is more accurate, the condition degree of the thyroid nodule can be accurately reflected by the sampling evaluation value, and the risk degree of the thyroid nodule can be more accurately evaluated by the sampling evaluation value.
Specifically, a correction coefficient matrix T is set in the processing module, and the processing module corrects the i-th sampling evaluation value C0i compensated by the compensation coefficient matrix K by using each correction coefficient in the correction coefficient matrix T.
Specifically, for the correction coefficient matrix T, T (T1, T2, T3, T4), T1 is a first correction coefficient, T2 is a second correction coefficient, T3 is a third correction coefficient, T4 is a fourth correction coefficient, and 0.9 < T1 < T2 < 1 < T3 < T4 < 1.1; the processing module is also internally provided with a preset ratio matrix Ff0 of the perimeter and the area of the thyroid nodule, ff0 (Ff 1, ff2, ff3 and Ff 4) is set, wherein Ff1 is the ratio of the first preset perimeter to the area, ff2 is the ratio of the second preset perimeter to the area, ff3 is the ratio of the third preset perimeter to the area, ff4 is the ratio of the fourth preset perimeter to the area, and Ff1 is more than Ff2 and less than Ff3 and less than Ff4;
The processing module determines a correction coefficient according to the relation between the preset ratio Ff of the perimeter to the area of the thyroid nodule and the ratio of each preset perimeter to the area so as to correct the ith sampling evaluation value C0i after compensation by the compensation coefficient:
when Ff is smaller than Ff1, selecting the first correction coefficient T1 to correct the compensated i-th sampling evaluation value C0i×k1, where the corrected i-th sampling evaluation value C0i×k1×t1;
when Ff1 is less than or equal to Ff < Ff2, selecting the second correction coefficient T2 to correct the compensated i-th sampling evaluation value C0i x K2, where the corrected i-th sampling evaluation value C0i x K2 x T2;
when Ff2 is less than or equal to Ff < Ff3, selecting the third correction coefficient T3 to correct the compensated i-th sampling evaluation value C0i×k3, where the corrected i-th sampling evaluation value C0i×k3×t3;
and when Ff3 is less than or equal to Ff4, selecting the fourth correction coefficient T4 to correct the compensated i-th sampling evaluation value C0i x K4, wherein the corrected i-th sampling evaluation value C0i x K4 x T4.
Specifically, the preset standard ratio F0 of the perimeter to the area of the thyroid nodule is a standard reference value, that is, when the thyroid nodule is a normal nodule, the ratio between the perimeter of the thyroid nodule and the area of the thyroid nodule is denoted as F0, that is, F0 represents the ratio between the perimeter of the thyroid nodule and the area of the thyroid nodule when the thyroid nodule is normal. The preset ratio Ff of the perimeter to the area of the thyroid nodule is a preset reference value, the Ff is represented as a maximum threshold value of the ratio of the perimeter to the area of the thyroid nodule when the thyroid nodule is a normal nodule, the Ff can be obtained according to data in the ratio of the perimeter to the area of the normal thyroid nodule in the past case data, and the Ff is preset according to the maximum ratio of the perimeter to the area of the normal thyroid nodule, so that the Ff is obtained.
It can be seen that, by determining the correction coefficient according to the relationship between the preset ratio Ff of the perimeter to the area of the thyroid nodule and the ratio of each preset perimeter to the area, the i-th sampling evaluation value C0i after being compensated by the compensation coefficient is corrected, and after the i-th sampling evaluation value C0i after being compensated is corrected by the correction coefficient, the calculation of the sampling evaluation value can be more accurate, and the acquisition of the sampling evaluation value is affected by the ratio of the perimeter to the area of the thyroid nodule, so that the thyroid nodule can be effectively evaluated according to the complexity degree of the outline of the thyroid nodule, and the accuracy of the risk evaluation result of the thyroid nodule can be improved.
Specifically, a preset thyroid nodule area correction matrix P0 is set in the processing module, and P0 (P1, P2, P3, P4) is set, wherein P1 is a first preset area correction coefficient, P2 is a second preset area correction coefficient, P3 is a third preset area correction coefficient, P4 is a fourth preset area correction coefficient, and P1 is more than 1 and less than P2 and P3 and P4 is more than 1.2;
the processing module is also used for obtaining the average value delta q of the gray values of the thyroid nodule, and the processing module is also pre-provided with the standard gray average value q0 of the thyroid nodule, and selects the preset area correction coefficient to correct the area S1 of the thyroid nodule according to the relation between the average value delta q of the gray values and the standard gray average value q0 and the relation between the perimeter and the area ratio F of the thyroid nodule and the preset standard ratio F0 of the perimeter and the area of the thyroid nodule,
When F is smaller than F0 and delta q is larger than q0, the first preset area correction coefficient P1 is selected to correct the area S1 of the thyroid nodule, and the corrected area of the thyroid nodule is S1P 1;
when F is smaller than F0 and delta q is smaller than or equal to q0, selecting the second preset area correction coefficient P2 to correct the area S1 of the thyroid nodule, wherein the area of the thyroid nodule after correction is S1P 2;
when F is larger than F0 and deltaq is larger than q0, selecting the third preset area correction coefficient P3 to correct the area S1 of the thyroid nodule, wherein the corrected area of the thyroid nodule is S1P 3;
when F is larger than F0 and deltaq is smaller than or equal to q0, selecting the fourth preset area correction coefficient P4 to correct the area S1 of the thyroid nodule, wherein the corrected area of the thyroid nodule is S1P 4;
after determining the area after thyroid nodule correction, the area after thyroid nodule correction is S1 Pi as the area of the thyroid nodule, and the calculations of E, E, ee, F0, and Ff are performed, i=1, 2,3,4.
It can be seen that by correcting the area of the thyroid nodule, the accuracy of the results of the ratio F of the perimeter to the area of the thyroid nodule, the preset standard ratio F0 of the perimeter to the area of the thyroid nodule, the preset ratio Ff of the perimeter to the area of the thyroid nodule, the calcified area percentage E of the thyroid nodule, the standard calcified area percentage E0 of the thyroid nodule and the preset calcified area percentage Ee of the thyroid nodule can be greatly improved, so that the subsequent diagnosis and apple efficiency can be improved.
Referring to fig. 2, in another preferred implementation manner based on the above embodiment, the present embodiment provides a risk assessment system for remote thyroid nodule ultrasound images, which includes a cloud platform 1, an ultrasound image acquisition room 2 and a PACS system 3 of a hospital, where the ultrasound image acquisition room 2 is connected in data communication with the cloud platform 1 through the PACS system 3 of the hospital. The ultrasonic image acquisition room 2 is used for acquiring ultrasonic images and uploading the ultrasonic images to the cloud platform 1 through the PACS system 3 of the hospital, and the cloud platform 1 is used for processing the ultrasonic images and outputting diagnosis results and risk assessment results.
Specifically, the cloud platform 1 includes a processing unit 4, and after the cloud platform 1 acquires an ultrasound image, the processing unit 4 disposed in the cloud platform performs data processing on the ultrasound image to determine whether a thyroid nodule exists in the ultrasound image:
if the thyroid nodule does not exist, the cloud platform makes a diagnosis of the thyroid gland being normal;
if the thyroid nodule exists, the cloud platform acquires information related to the thyroid nodule in the ultrasonic image and carries out subsequent steps;
when the processing unit detects the thyroid nodule from the ultrasonic image, the processing module is used for judging the character B and the aspect ratio D of the thyroid nodule, the processing module is also used for acquiring an echo value delta A of the thyroid nodule, establishing a preset echo value matrix A and setting A (A1, A2, A3 and A4), wherein A1 is a first preset echo value, A2 is a second preset echo value, A3 is a third preset echo value, A4 is a fourth preset echo value, and A1 is more than A2 and less than A3 and less than A4; the processing module performs grade judgment on the thyroid nodule according to the character B, the aspect ratio D and the echo value delta A of the thyroid nodule:
When A3 is less than delta A4 or delta A is more than or equal to A4, if the character B of the thyroid nodule is a cystic nodule and the aspect ratio D of the thyroid nodule is less than 1, judging the grade of the thyroid nodule as a first grade, and calculating a first sampling evaluation value C01 of the thyroid nodule;
when A2 is less than delta A and less than or equal to A3, if the character B of the thyroid nodule is a cystic nodule and the aspect ratio D of the thyroid nodule is less than 1, judging the grade of the thyroid nodule is a second grade, and calculating a second sampling evaluation value C02 of the thyroid nodule;
when A1 is less than delta A and less than or equal to A2, if the character B of the thyroid nodule is a solid nodule and the aspect ratio D of the thyroid nodule is less than 1, judging the grade of the thyroid nodule is three-grade, and calculating a third sampling evaluation value C03 of the thyroid nodule;
when delta A is less than or equal to A1, if the character B of the thyroid nodule is a solid nodule and the aspect ratio D of the thyroid nodule is more than 1, judging the grade of the thyroid nodule is four grades, and calculating a fourth sampling evaluation value C04 of the thyroid nodule;
and when the i-th sampling evaluation value C0i is less than C, the grade of the thyroid nodule is reduced by one step, the lowest grade of the thyroid nodule is one step, and the thyroid nodule is judged to be a low-risk nodule, and when the i-th sampling evaluation value C0i is more than C, the grade of the thyroid nodule is increased by one step, and the thyroid nodule is judged to be a high-risk nodule, wherein i=1, 2,3 and 4.
It can be seen that the system can automatically diagnose and evaluate thyroid nodule, can greatly save manpower, and improve diagnosis efficiency and accuracy.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.

Claims (9)

1. The risk assessment method for the remote thyroid nodule ultrasound image is characterized by comprising the following steps of:
step S1: acquiring an ultrasonic image of a thyroid position and uploading the ultrasonic image to a cloud platform;
step S2: after the ultrasonic image is acquired by the cloud platform, the ultrasonic image is subjected to data processing by a processing unit arranged in the cloud platform so as to judge whether thyroid nodules exist in the ultrasonic image:
if the thyroid nodule does not exist, the cloud platform makes a diagnosis of the thyroid gland being normal;
if the thyroid nodule exists, the cloud platform acquires information related to the thyroid nodule in the ultrasonic image and carries out subsequent steps;
step S3: a sample evaluation value C is set in the cloud platform, the cloud platform diagnoses the grade of the thyroid nodule according to the related information of the thyroid nodule, and risk assessment is carried out on the thyroid nodule after grade diagnosis through the sample evaluation value C;
step S4: the cloud platform outputs the diagnosis and risk assessment results of the thyroid nodule and transmits the diagnosis and risk assessment results of the thyroid nodule back to a PACS system of a hospital;
In the step S3, after the processing unit detects the thyroid nodule from the ultrasound image, the processing module is configured to determine a property B and an aspect ratio D of the thyroid nodule, and the processing module is further configured to obtain an echo value Δa of the thyroid nodule, establish a preset echo value matrix a, and set a (A1, A2, A3, A4), where A1 is a first preset echo value, A2 is a second preset echo value, A3 is a third preset echo value, A4 is a fourth preset echo value, and A1 is greater than A2 and less than A3 and less than A4; the processing module performs grade judgment on the thyroid nodule according to the character B, the aspect ratio D and the echo value delta A of the thyroid nodule:
when A3 is less than delta A4 or delta A is more than or equal to A4, if the character B of the thyroid nodule is a cystic nodule and the aspect ratio D of the thyroid nodule is less than 1, judging the grade of the thyroid nodule as a first grade, and calculating a first sampling evaluation value C01 of the thyroid nodule;
when A2 is less than delta A and less than or equal to A3, if the character B of the thyroid nodule is a cystic nodule and the aspect ratio D of the thyroid nodule is less than 1, judging the grade of the thyroid nodule is a second grade, and calculating a second sampling evaluation value C02 of the thyroid nodule;
When A1 is less than delta A and less than or equal to A2, if the character B of the thyroid nodule is a solid nodule and the aspect ratio D of the thyroid nodule is less than 1, judging the grade of the thyroid nodule is three-grade, and calculating a third sampling evaluation value C03 of the thyroid nodule;
when delta A is less than or equal to A1, if the character B of the thyroid nodule is a solid nodule and the aspect ratio D of the thyroid nodule is more than 1, judging the grade of the thyroid nodule is four grades, and calculating a fourth sampling evaluation value C04 of the thyroid nodule;
and when the i-th sampling evaluation value C0i is less than C, the grade of the thyroid nodule is reduced by one step, the lowest grade of the thyroid nodule is one step, and the thyroid nodule is judged to be a low-risk nodule, and when the i-th sampling evaluation value C0i is more than C, the grade of the thyroid nodule is increased by one step, and the thyroid nodule is judged to be a high-risk nodule, wherein i=1, 2,3 and 4.
2. The method for risk assessment of remote thyroid nodule ultrasound imaging of claim 1,
the ith sampling evaluation value c0i=Δa/a0+b/b0+d/d0+e/e0+f/F0, the sample evaluation value c=Δaa/a0+bb/b0+dd/d0+ee/e0+ff/F0, wherein Δa is an echo value of the thyroid nodule, A0 is a preset standard echo value of the thyroid nodule, Δaa is a preset echo value of the thyroid nodule, B is a property of the thyroid nodule, b=1 when the property of the thyroid nodule is a cystic nodule, b=2 when the property of the thyroid nodule is a solid nodule, b0=1, bb=1.5, D is an aspect ratio of the thyroid nodule, d0.5, E is a calcified area percentage of the thyroid nodule, e0=10%, ee=20%, F is a perimeter to area ratio of the thyroid nodule, F0 is a preset perimeter to area standard ratio of the thyroid nodule, and F is a preset perimeter to a perimeter to an area ratio of the thyroid nodule.
3. The risk assessment method of remote thyroid nodule ultrasound imaging of claim 2, wherein the processing module obtains an area S1 of the thyroid nodule and a calcified area S2 of the thyroid nodule after the detected thyroid nodule, E = S2/S1 x 100%;
the processing module adjusts the grade of the thyroid nodule according to the calcified area percentage E of the thyroid nodule and the standard calcified area percentage E0 of the preset thyroid nodule,
when E is more than E0, the grade of the thyroid nodule is increased by one grade;
when E is less than E0, reducing the grade of the thyroid nodule by one step;
when e=e0, no adjustment is made to the grade of the thyroid nodule.
4. The method for risk assessment of remote thyroid nodule ultrasound images according to claim 3, wherein the processing module is further configured to obtain a perimeter Z of the thyroid nodule after the detected thyroid nodule, and calculate a perimeter to area ratio F of the thyroid nodule according to a ratio between the perimeter Z of the thyroid nodule and an area S1 of the thyroid nodule, f=z/S1, wherein a preset area S3 and a preset perimeter Z1 of the thyroid nodule are set in the processing module, and a preset standard ratio f0=z1/S3 of the perimeter to the area of the thyroid nodule;
The processing module adjusts the grade of the thyroid nodule after the calcification area percentage of the thyroid nodule is adjusted again according to the relation between the ratio F of the perimeter to the area of the thyroid nodule and the preset standard ratio F0 of the perimeter to the area of the thyroid nodule:
when E is more than E0, after the grade of the thyroid nodule is judged to be increased by one step, if F is more than F0, the grade of the thyroid nodule is increased by one step, if F is less than F0, the grade of the thyroid nodule is reduced by one step, and if F=F0, the grade of the thyroid nodule is not adjusted;
when E < E0, reducing the grade of the thyroid nodule by one step, if F > F0, increasing the grade of the thyroid nodule by one step, if F < F0, reducing the grade of the thyroid nodule by one step, and if F=F0, not adjusting the grade of the thyroid nodule;
when e=e0, the grade of the thyroid nodule is not adjusted, and then, if F > F0, the grade of the thyroid nodule is increased by one step, if F < F0, the grade of the thyroid nodule is decreased by one step, and if f=f0, the grade of the thyroid nodule is not adjusted.
5. The method for risk assessment of remote thyroid nodule ultrasound image according to claim 4, wherein a compensation coefficient matrix K is set in the processing module, and the processing module compensates the i-th sampling assessment value C0i through each compensation coefficient in the compensation coefficient matrix K.
6. The method according to claim 5, wherein for the compensation coefficient matrix K, K (K1, K2, K3, K4) is set, wherein K1 is a first compensation coefficient, K2 is a second compensation coefficient, K3 is a third compensation coefficient, K4 is a fourth compensation coefficient, and 1 < K2 < K3 < K4 < 1.5; the processing module is also internally provided with a preset calcification area percentage matrix Ee0 of thyroid nodules, ee0 (Ee 1, ee2, ee3 and Ee 4) is set, wherein Ee1 is a first preset calcification area percentage, ee2 is a second preset calcification area percentage, ee3 is a third preset calcification area percentage, ee4 is a fourth preset calcification area percentage, ee1 is more than Ee2 and less than Ee3 and less than Ee4;
the processing module determines a compensation coefficient according to the relation between the preset calcification area percentage Ee of the thyroid nodule and each preset calcification area percentage to compensate the ith sampling evaluation value C0 i:
When Ee is smaller than Ee1, selecting the first compensation coefficient K1 to compensate the i-th sampling evaluation value C0i, wherein the i-th sampling evaluation value C0i after compensation is C0i x K1;
when Ee1 is less than or equal to Ee2, selecting the second compensation coefficient K2 to compensate the ith sampling evaluation value C0i, wherein the compensated ith sampling evaluation value C0i is C0i x K2;
when Ee2 is less than or equal to Ee3, selecting the third compensation coefficient K3 to compensate the ith sampling evaluation value C0i, wherein the compensated ith sampling evaluation value C0i is C0i x K3;
when Ee3 is less than or equal to Ee4, selecting the fourth compensation coefficient K4 to compensate the ith sampling evaluation value C0i, wherein the compensated ith sampling evaluation value C0i is C0i x K4.
7. The risk assessment method according to claim 6, wherein a correction coefficient matrix T is set in the processing module, and the processing module corrects the i-th sampling assessment value C0i compensated by the compensation coefficient matrix K by each correction coefficient in the correction coefficient matrix T.
8. The method according to claim 7, wherein T (T1, T2, T3, T4) is set for the correction coefficient matrix T, T1 is a first correction coefficient, T2 is a second correction coefficient, T3 is a third correction coefficient, T4 is a fourth correction coefficient, and 0.9 < T1 < T2 < 1 < T3 < T4 < 1.1; the processing module is also internally provided with a preset ratio matrix Ff0 of the perimeter and the area of the thyroid nodule, ff0 (Ff 1, ff2, ff3 and Ff 4) is set, wherein Ff1 is the ratio of the first preset perimeter to the area, ff2 is the ratio of the second preset perimeter to the area, ff3 is the ratio of the third preset perimeter to the area, ff4 is the ratio of the fourth preset perimeter to the area, and Ff1 is more than Ff2 and less than Ff3 and less than Ff4;
The processing module determines a correction coefficient according to the relation between the preset ratio Ff of the perimeter to the area of the thyroid nodule and the ratio of each preset perimeter to the area so as to correct the ith sampling evaluation value C0i after compensation by the compensation coefficient:
when Ff is smaller than Ff1, selecting the first correction coefficient T1 to correct the compensated i-th sampling evaluation value C0i×k1, where the corrected i-th sampling evaluation value C0i×k1×t1;
when Ff1 is less than or equal to Ff < Ff2, selecting the second correction coefficient T2 to correct the compensated i-th sampling evaluation value C0i x K2, where the corrected i-th sampling evaluation value C0i x K2 x T2;
when Ff2 is less than or equal to Ff < Ff3, selecting the third correction coefficient T3 to correct the compensated i-th sampling evaluation value C0i×k3, where the corrected i-th sampling evaluation value C0i×k3×t3;
and when Ff3 is less than or equal to Ff4, selecting the fourth correction coefficient T4 to correct the compensated i-th sampling evaluation value C0i x K4, wherein the corrected i-th sampling evaluation value C0i x K4 x T4.
9. The method for risk assessment of remote thyroid nodule ultrasound image according to claim 8, wherein a preset thyroid nodule area correction matrix P0 is set in the processing module, P0 (P1, P2, P3, P4) is set, wherein P1 is a first preset area correction coefficient, P2 is a second preset area correction coefficient, P3 is a third preset area correction coefficient, P4 is a fourth preset area correction coefficient, and 1 < P2 < P3 < P4 < 1.2;
The processing module is also used for obtaining the average value delta q of the gray values of the thyroid nodule, and the processing module is also pre-provided with the standard gray average value q0 of the thyroid nodule, and selects the preset area correction coefficient to correct the area S1 of the thyroid nodule according to the relation between the average value delta q of the gray values and the standard gray average value q0 and the relation between the perimeter and the area ratio F of the thyroid nodule and the preset standard ratio F0 of the perimeter and the area of the thyroid nodule,
when F is smaller than F0 and delta q is larger than q0, the first preset area correction coefficient P1 is selected to correct the area S1 of the thyroid nodule, and the corrected area of the thyroid nodule is S1P 1;
when F is smaller than F0 and delta q is smaller than or equal to q0, selecting the second preset area correction coefficient P2 to correct the area S1 of the thyroid nodule, wherein the area of the thyroid nodule after correction is S1P 2;
when F is larger than F0 and deltaq is larger than q0, selecting the third preset area correction coefficient P3 to correct the area S1 of the thyroid nodule, wherein the corrected area of the thyroid nodule is S1P 3;
when F is larger than F0 and deltaq is smaller than or equal to q0, selecting the fourth preset area correction coefficient P4 to correct the area S1 of the thyroid nodule, wherein the corrected area of the thyroid nodule is S1P 4;
After determining the area after thyroid nodule correction, the area after thyroid nodule correction is S1 Pi as the area of the thyroid nodule, and the calculations of E, E, ee, F0, and Ff are performed, i=1, 2,3,4.
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