CN114093467A - One-stop CT (computed tomography) automatic structured reporting system and method for cerebral apoplexy - Google Patents
One-stop CT (computed tomography) automatic structured reporting system and method for cerebral apoplexy Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 208000006011 Stroke Diseases 0.000 title claims description 18
- 206010008190 Cerebrovascular accident Diseases 0.000 title claims description 8
- 230000002490 cerebral effect Effects 0.000 title claims description 8
- 238000002591 computed tomography Methods 0.000 title description 12
- 201000010099 disease Diseases 0.000 claims abstract description 50
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 50
- 238000003745 diagnosis Methods 0.000 claims abstract description 30
- 208000024891 symptom Diseases 0.000 claims abstract description 22
- 238000001514 detection method Methods 0.000 claims abstract description 18
- 238000012549 training Methods 0.000 claims description 16
- 238000007781 pre-processing Methods 0.000 claims description 13
- 238000005520 cutting process Methods 0.000 claims description 11
- 239000011159 matrix material Substances 0.000 claims description 10
- 238000011156 evaluation Methods 0.000 claims description 8
- 238000012360 testing method Methods 0.000 claims description 4
- 238000013100 final test Methods 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000000605 extraction Methods 0.000 claims description 2
- 230000003902 lesion Effects 0.000 claims description 2
- 239000000284 extract Substances 0.000 claims 1
- 230000009545 invasion Effects 0.000 claims 1
- 230000005855 radiation Effects 0.000 abstract description 3
- 230000010354 integration Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010968 computed tomography angiography Methods 0.000 description 2
- 238000000265 homogenisation Methods 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 102000056548 Member 3 Solute Carrier Family 12 Human genes 0.000 description 1
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- 238000003709 image segmentation Methods 0.000 description 1
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- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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Abstract
The invention discloses a stroke one-stop CT automatic structured report system and a method thereof, wherein the system comprises a data layer, an image display layer, a report output layer and an editing layer; the data layer is respectively in signal connection with the image display layer and the report output layer, and the report output layer is in signal connection with the editing layer; the image display layer is used for assisting in acquiring a detection image of a patient and displaying an image symptom, the data layer is used for storing the content of a disease library, and the content of the disease library comprises the content of the image symptom and the content of disease diagnosis; the stroke one-stop CT automatic structured reporting system and the method thereof are used for assisting an emergency radiation diagnostician in performing multi-mode CT image interpretation and diagnosis, and can shorten the time consumed by diagnosis reporting and assist the radiologist in an emergency environment to reduce the risks of missing reporting and false reporting; the report content and the professional terms can be standardized, and the radiation report can be homogenized and mutual recognition of results among disciplines, multi-hospital intervals and regional union hospitals.
Description
Technical Field
The invention relates to the field of cerebral apoplexy diagnosis result display, in particular to a cerebral apoplexy one-stop CT automatic structured reporting system and a method thereof.
Background
In daily work of doctors, the report template is the legal treasure of the radiology department first-line report doctor. On the basis of copying and pasting the report template, a report influencing diagnosis can be quickly finished by carrying out moderate modification. The larger and more detailed the template is, the higher the efficiency is, but the more the chance of error is; although the template has less chance of making a small error, the personalized modification has more contents and low efficiency. First line reports that doctors are constantly looking for a balance among the contradictions, and the finally generated text type report (narrative type report) has systematic defects (a template generation method and a template switching method) which cannot be eliminated in terms of diagnosis quality and clinical satisfaction.
In the actual determination process, because the business levels of the radiodiagnostic doctors are different and the writing styles are different, the attention points of written reports and the contents of the reported reports are often different. The image structured report is a report form for standardizing the frame, logic, content and terms of the image symptom description, and can ensure that the image diagnosis reports of different doctors have similar form and content to the maximum extent. At present, some hospital radiology departments try to use paper-version structured reports, but the paper-version structured reports are insufficient in aspects of standardizing the reading habits of doctors, expanding diagnosis ideas, presenting modes of final reports and the like, and the clinical popularization of the image structured reports is limited to a greater extent.
Disclosure of Invention
In order to solve the technical problems, the invention provides a one-stop CT automatic structured report system for stroke and a method thereof, which can directly acquire related symptoms and final suspected disease conditions aiming at a detected image, can effectively avoid missed report diagnosis, can standardize report contents and professional terms, and promote medical resource integration and academic exchange.
The technical purpose of the invention is realized by the following technical scheme:
a one-stop CT automatic structured report system for cerebral apoplexy and a method thereof comprise a data layer, an image display layer, a report output layer and an editing layer; the data layer is respectively in signal connection with the image display layer and the report output layer, and the report output layer is in signal connection with the editing layer;
the image display layer is used for assisting in acquiring a detection image of a patient and displaying an image symptom, the data layer is used for storing the content of a disease library, and the content of the disease library comprises the content of the image symptom and the content of disease diagnosis; the report output layer interacts with the image display layer, corresponding positions of the detected images are selected, then the image display layer interacts with the data layer to generate suspected image signs of the corresponding positions, and the radiologist selects specific image sign contents and outputs the selected suspected image signs and severity contents through the report output layer; the report output layer interacts with the data layer, obtains the diagnosis results of all suspected diseases by integrating all suspected image signs, and finally outputs a specific radiodiagnosis report by the selection of a radiologist; the report output layer interacts with the editing layer, and corresponding revision or remark content is generated on the specific radiodiagnosis report through editing of a radiologist.
As a preferable scheme, the report output layer is provided with a template unit, the suspected image symptom content and the suspected disease diagnosis result content acquired by the report output layer are generated to corresponding positions according to the template unit, and the template unit automatically outputs 'no content' if some positions do not acquire the corresponding content.
As a preferred scheme, the image display layer comprises an image acquisition module and an image preprocessing module, wherein the image acquisition module is used for preprocessing a patient detection image through the image preprocessing module after acquiring the patient detection image, and then performing interactive action with the data layer and the report output layer; the image preprocessing module comprises a feature comparison unit, an image cutting unit and an image matrix generating unit.
As a preferred scheme, the method for constructing the feature comparison unit specifically includes the following steps:
first, set feature set F1 ═ F1,f2,…fnIntegrating the characteristic samples of the diseased images, and using 80% of the diseased characteristics for contrast training of the characteristic set F1, wherein the contrast training process is as follows:
Output(hl,hl-1)＝Process-Function(hl,hl-1)
wherein h isl-1Is the sick character of the last input, hlThe disease characteristics input this time are input, and the final output value is placed in the corresponding characteristic set f through multiple comparison training of multiple sampleslPerforming the following steps; then, carrying out comparison training on a plurality of different features through the comparison training algorithm to finally generate all the features in the feature set F1; and then using the remaining 20% of samples for testing, and obtaining a final test result to obtain a final characteristic set model.
As a preferable scheme, the image cutting unit is used for cutting a detection image of a patient, the image acquisition module inputs the detection image of the patient into the final feature set model, then compares and judges that a disease area exists, cuts the image without the disease area, and sends the residual image to the image matrix unit.
Preferably, the image matrix unit compares the features existing in the residual image after cutting with the disease features in the database, and outputs the existing contents of the suspected image signs when the corresponding position of the detection image is selected by the radiologist.
Preferably, the image display layer further comprises an image evaluation module, wherein the image evaluation module is configured to assign a value of 0, 1 or 2 to each feature in the image, and respectively represent three mutually exclusive states in which the lesion may exist: "no", "suspect", "present".
As a preferred scheme, when the image evaluation module performs feature extraction, all feature assignments are extracted and are subjected to superposition calculation to obtain a final feature evaluation score.
In conclusion, the invention has the following beneficial effects:
the stroke one-stop CT automatic structured reporting system and the method thereof are used for assisting an emergency radiation diagnostician in performing multi-mode CT image interpretation and diagnosis, and can shorten the time consumed by diagnosis reporting and assist the radiologist in an emergency environment to reduce the risks of missing reporting and false reporting; the method can standardize report contents and professional terms, is beneficial to realizing homogenization and mutual recognition of results of the radiology reports among disciplines, multi-hospital intervals and regional union hospitals, avoids resource waste caused by repeated examination, and promotes medical resource integration and academic communication.
Drawings
Fig. 1 is a block diagram of a one-stop CT automatic structured reporting system for stroke and a method thereof according to an embodiment of the present invention.
Detailed Description
This specification and claims do not intend to distinguish between components that differ in name but not function. In the following description and in the claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. "substantially" means within an acceptable error range, within which a person skilled in the art can solve the technical problem to substantially achieve the technical result.
The terms in the upper, lower, left, right and the like in the specification and the claims are used for further explanation, so that the application is more convenient to understand and is not limited to the application.
The present invention will be described in further detail with reference to the accompanying drawings.
Example 1:
a one-stop CT automatic structured report system for cerebral apoplexy and a method thereof comprise a data layer, an image display layer, a report output layer and an editing layer; the data layer is respectively in signal connection with the image display layer and the report output layer, and the report output layer is in signal connection with the editing layer;
the image display layer is used for assisting in acquiring a detection image of a patient and displaying image symptoms, the data layer is used for storing the contents of a disease library, and the contents of the disease library comprise image symptom contents and disease diagnosis contents; the report output layer interacts with the image display layer, corresponding positions of the detected images are selected, then the image display layer interacts with the data layer to generate suspected image signs of the corresponding positions, and the radiologist selects specific image sign contents and outputs the selected suspected image signs and severity contents through the report output layer; the report output layer interacts with the data layer, obtains the diagnosis results of all suspected diseases by integrating all suspected image signs, and finally outputs a specific radiodiagnosis report by the selection of a radiologist; the report output layer interacts with the editing layer, and corresponding revision or remark content is generated on the specific radiodiagnosis report through editing of a radiologist.
In example 1, a patient test image is first acquired, which is based on the actual conditions of the hospital, including but not limited to CT-flat scan (NCCT), CT perfusion (CTP), and CT angiography (CTA); then when a radiologist watches the detection image, the radiologist clicks the relevant position of the image, the image is connected with the data layer to display possible symptoms at the position, and the report output layer finally generates all symptoms along with the observation of a plurality of places; and clicking to confirm, interacting the report output layer and the data layer, and automatically obtaining a medical record diagnosis result according to all the symptoms. The embodiment 1 can realize the structured medical record diagnosis result, and does not need a radiologist to judge and edit manually, so that the finally generated result can realize homogenization and mutual recognition of results among disciplines, multi-hospital intervals and regional alliance hospitals, the resource waste caused by repeated examination is avoided, and the integration of medical resources and academic exchange are promoted.
Example 2
The method is basically similar to that of embodiment 1, and is different in that the report output layer is provided with a template unit, the suspected image symptom content and the suspected disease diagnosis result content acquired by the report output layer are generated to corresponding positions according to the template unit, and the template unit automatically outputs 'no content' if certain positions do not acquire corresponding content.
In embodiment 2, through the template unit, when a radiologist outputs a medical condition through the report output layer, the radiologist can automatically acquire related content, and when there is no corresponding medical condition, the radiologist can display "no content" so that all conditions of the patient are clearer, and risks such as missing report and misinformation of the radiologist are avoided.
Example 3
The system is basically similar to the embodiments 1 and 2, and is different in that the image display layer comprises an image acquisition module and an image preprocessing module, wherein the image acquisition module performs preprocessing through the image preprocessing module after acquiring a patient detection image, and then performs interactive action with the data layer and the report output layer; the image preprocessing module comprises a feature comparison unit, an image cutting unit and an image matrix generating unit. The method for constructing the feature comparison unit specifically comprises the following steps:
first, set feature set F1 ═ F1,f2,…fnAnd then integrating the diseased image feature samples, and using 80% of the diseased features for contrast training of the feature set F1, wherein the contrast training process is as follows:
Output(hl,hl-1)＝Process-Function(hl,hl-1)
wherein h isl-1Is the sick character of the last input, hlThe disease characteristics of the current input are compared and trained for multiple times by a plurality of samples, and the final output value is obtainedPlace in the corresponding feature set flPerforming the following steps; then, carrying out comparison training on a plurality of different features through the comparison training algorithm to finally generate all the features in the feature set F1; and then using the remaining 20% of samples for testing, and obtaining a final test result to obtain a final characteristic set model.
In embodiment 3, a disease diagnosis model can be obtained online, after a detected image of a patient is obtained, an effective image with a disease state is finally generated through preprocessing, image comparison and segmentation, and a 'no content' is automatically generated at a corresponding position of the template, compared with an original image observed, the diagnosis time of a radiologist can be greatly reduced, and after the diagnosis model is learned through training, the content of the disease state in the detected image of the patient can be automatically identified, the final disease diagnosis content is automatically generated with the assistance of a database, and the content is automatically filled in corresponding through a template unit.
Example 4
The method is basically similar to embodiments 1, 2 and 3, and is different in that the image segmentation unit is used for segmenting the detection image of the patient, the image acquisition module inputs the detection image of the patient into the final feature set model, then compares and judges the existence of the disease area, segments the image of the disease area, and sends the rest of the image to the image matrix unit. The image matrix unit compares the features existing in the residual images after cutting with the disease features in the database, and outputs the existing suspected image symptom content when the radiologist selects the corresponding position of the detected image.
In example 4, the image cutting process and how to automatically obtain the final disease diagnosis content with the assistance of the data layer are explained in detail, and the workload of the radiologist is greatly reduced.
The working principle is as follows:
firstly, a data layer and a disease diagnosis model are constructed, wherein the data layer comprises symptoms of stroke-related diseases and specific disease results, and the symptoms and the specific disease results are obtained by presetting a database; the disease diagnosis model carries out a plurality of times of machine training through a large number of detection images of diseases to obtain a relevant position capable of automatically identifying diseases and symptoms; after the patient is detected, firstly, the detection image of the patient is output, the disease diagnosis model automatically identifies the detection image, the detection image is cut to obtain a region containing a disease, the corresponding position in the template unit automatically fills 'no content' in the cut image content, then the image matrix generation unit enables the relevant characteristics of the reserved image content to be interacted through a data layer, a new image containing the local content of the relevant disease is newly generated, the relevant disease content is obtained, after a radiologist selects the corresponding position in the newly generated image, all suspected image symptom contents are automatically obtained, then the radiologist manually obtains the specific disease content, and finally a disease result report is generated.
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present invention.
Claims (8)
1. A one-stop CT automatic structured report system and a method thereof for cerebral apoplexy are characterized by comprising a data layer, an image display layer, a report output layer and an editing layer; the data layer is respectively in signal connection with the image display layer and the report output layer, and the report output layer is in signal connection with the editing layer;
the image display layer is used for assisting in acquiring a detection image of a patient and displaying an image symptom, the data layer is used for storing the content of a disease library, and the content of the disease library comprises the content of the image symptom and the content of disease diagnosis; the report output layer interacts with the image display layer, corresponding positions of the detected images are selected, then the image display layer interacts with the data layer to generate suspected image signs of the corresponding positions, and the radiologist selects specific image sign contents and outputs the selected suspected image signs and severity contents through the report output layer; the report output layer interacts with the data layer, obtains the diagnosis results of all suspected diseases by integrating all suspected image signs, and finally outputs a specific radiodiagnosis report by the selection of a radiologist; the report output layer interacts with the editing layer, and corresponding revision or remark content is generated on the specific radiodiagnosis report through editing of a radiologist.
2. The automatic structured reporting system for one-stop CT of stroke and the method thereof as claimed in claim 1, wherein the report output layer is provided with a template unit, the suspected image symptom content and the suspected disease diagnosis result content obtained by the report output layer are generated to corresponding positions according to the template unit, and the template unit automatically outputs "no content" if some of the positions do not obtain the corresponding content.
3. The automatic structured report system for one-stop CT of stroke and the method thereof as claimed in claim 1, wherein the image display layer comprises an image acquisition module and an image preprocessing module, the image acquisition module performs preprocessing by the image preprocessing module after acquiring the detection image of the patient, and then performs interactive action with the data layer and the report output layer; the image preprocessing module comprises a feature comparison unit, an image cutting unit and an image matrix generating unit.
4. The automatic structured report system for one-stop CT for stroke and the method thereof as claimed in claim 3, wherein the method for constructing the feature comparison unit specifically comprises the following steps:
first, set feature set F1 ═ F1,f2,…fnIntegrating the characteristic samples of the diseased images, and using 80% of the diseased characteristics for contrast training of the characteristic set F1, wherein the contrast training process is as follows:
Output(hl,hl-1)＝Process-Function(hl,hl-1)
wherein h isl-1Is the last time of transfusionThe disease characteristics of invasion, hlThe disease characteristics input this time are input, and the final output value is placed in the corresponding characteristic set f through multiple comparison training of multiple sampleslPerforming the following steps; then, carrying out comparison training on a plurality of different features through the comparison training algorithm to finally generate all the features in the feature set F1; and then using the remaining 20% of samples for testing, and obtaining a final test result to obtain a final characteristic set model.
5. The automatic structured reporting system for one-stop CT for stroke and the method thereof as claimed in claim 4, wherein the image cutting unit is used for cutting the detected image of the patient, the image obtaining module inputs the detected image of the patient into the final feature set model, then compares and determines the existence of the disease area, cuts the image of the disease area, and sends the remaining image to the image matrix unit.
6. The automatic structured reporting system for one-stop CT of cerebral apoplexy and the method thereof as claimed in claim 5, wherein the image matrix unit compares the features existing in the residual image after cutting with the disease features in the database, and outputs the existing suspected image symptom contents when the radiologist selects the corresponding position of the detected image.
7. The automatic structured report system for one-stop CT in stroke and the method thereof as claimed in claim 1, wherein the image display layer further comprises an image evaluation module, the image evaluation module is used to assign a value of 0, 1 or 2 to each feature in the image, respectively representing three mutually exclusive states that the lesion may exist: "no", "suspect", "present".
8. The automatic structured reporting system for one-stop CT of stroke and the method thereof as claimed in claim 7, wherein the image evaluation module extracts all feature assignments and performs superposition calculation to obtain the final feature evaluation score during feature extraction.
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| US20160364862A1 (en) * | 2015-06-12 | 2016-12-15 | Merge Healthcare Incorporated | Methods and Systems for Performing Image Analytics Using Graphical Reporting Associated with Clinical Images |
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