CN109222980A - Method of the measurement coronarogram based on deep learning as blood vessel diameter - Google Patents
Method of the measurement coronarogram based on deep learning as blood vessel diameter Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
- A61B5/1075—Measuring physical dimensions, e.g. size of the entire body or parts thereof for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
- G06T7/0014—Biomedical image inspection using an image reference approach
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
Abstract
Method of the measurement coronarogram that the invention discloses a kind of based on deep learning as blood vessel diameter, comprising: the DSA image data obtained in real time is changed into the data flow for subsequent module for processing and stores into memory and be sent to depth network segmentation module by DSA image processing module；The image in DSA image data that depth network segmentation module will acquire is split processing, and blood vessel pixel and background pixel are distinguished；Central line pick-up module extracts the vessel centerline distinguished in blood vessel pixel image；Diameter calculation module calculates vessel measurement diameter, reference diameter and stenosis rate based on the image and vessel centerline after dividing processing.Present invention result compared with PVA method is more objective, avoid between different doctors, Different hospital due to interpretation method is different and existing difference；This method measurement process is not required to manual intervention, and operating method is simple and easy, uses in coronary angigraphy progress for doctor, provides objectively for doctor with reference to Severity of Coronary Artery Stenosis.
Description
Technical field
The present invention relates to a kind of to measure coronarogram as the method for blood vessel diameter based on deep learning, belongs to
In computing technique field.
Background technique
Coronary arteriography is the important method of heart disease diagnosis, from contrastographic picture observation analysis vascular morphology,
It moves and estimates the diameter of blood vessel, diagnosis cardiovascular disease can be assisted and determine suitable therapeutic scheme, clinically
It is of great significance.
Currently, clinically doctor judges that the standard method of Severity of Coronary Artery Stenosis is still PVA in coronary angigraphy
(Physician Visual Assessment, doctor's visually rank), American Society of Cardiology in 2017 and American Heart Association
The standard of patients with stable angina pectoris revascularization is that there are Serious Stenosis for coronary artery, estimates lumen diameter >=70% or blood flow
Laying in score is 0.80 or lower.In past more than 20 years, generally just decide whether to carry out in angiography coronal dynamic
Arteries and veins interventional therapy, this makes the assessment accuracy of operator particularly important.The stenosis and meter for thering is studies have shown that PVA to judge
The QCA (Quantifying Coronary Analysis, quantitative coronary analysis) of calculation machine auxiliary is compared, and PVA tends to height
Estimate Severity of Coronary Artery Stenosis, especially non-acute heart infarction patient selects a time in PCI treatment crowd, over-evaluated about 16%, in the acute heart
In flesh infarct crowd, the stenosis of PVA judgement has over-evaluated 10.2%, is all remarkably higher than QCA result.Studying also found, PVA high
The degree estimated difference between Different hospital, different doctors is huge, and such as non-acute Patients With Myocardial Infarction, PVA over-evaluates narrow
Narrow degree is differed from 7.6% to 21.3% between Different hospital, and the difference between different doctors is from 6.9% to 26.4%.
Computer assisted QCA can preferably analyze diseased region, stenosis and involvement range, but in operation
It needs manually to participate in, DSA image is carried out to manually select suitable frame, calibration of Pixel-level etc., these factors can be brought to result
It influences to a certain degree, and operation is more complex, is not suitable for carrying out in coronary angigraphy.
Summary of the invention
In view of the foregoing drawbacks, the present invention provides a kind of measurement coronarogram based on deep learning is as blood vessel is straight
The method of diameter, this method is a kind of measurement method for being automatically applied to DSA image, ongoing by obtaining radiography in real time
DSA image, is input in analysis and processing module, by calculating the measurement diameter for finally providing contrastographic picture and corresponding to coronary artery blood vessel,
This method result compared with PVA method is more objective, avoid between different doctors, Different hospital since interpretation method is different and
Existing difference；This method measurement process is not required to manual intervention, and operating method is simple and easy, for doctor coronary angigraphy into
It uses in row, provides objectively for doctor with reference to Severity of Coronary Artery Stenosis.
In order to achieve the above objectives, the present invention implements by the following technical programs:
The present invention provides a kind of method of the measurement coronarogram as blood vessel diameter based on deep learning, the party
Method includes:
The DSA image data obtained in real time is changed into and is deposited for the data flow of subsequent module for processing by DSA image processing module
It stores up in memory and is sent to depth network segmentation module；
Image in the DSA image data that will acquire of depth network segmentation module is split processing, by blood vessel pixel and
Background pixel is distinguished；
Central line pick-up module extracts the vessel centerline distinguished in blood vessel pixel image；
Diameter calculation module calculates vessel measurement diameter, with reference to straight based on the image and vessel centerline after dividing processing
Diameter and stenosis rate.
DSA image data incoming in real time is decoded processing by the DSA image processing module, after decoding process
Image data is written in memory and reads for depth network segmentation module.
The image in DSA image data that the depth network segmentation module will acquire is split processing, by blood vessel picture
Element is distinguished with background pixel, is specifically included:
Step 1 reads existing model from model database, initializes depth network, starts primary training or test
Process；
Step 2, training process: the image data and labeled data in DSA image data are read, by depth network query function
Data are propagated to network front end, are exported result calculating parameter renewal amount according to last network and are updated network parameter, will update
Obtained parameter model, which is stored, to be used into model database for next time；
Step 3, test process: reading the image data in DSA image data, by depth network query function data to network
Front end is propagated, and is obtained network to the end and is exported image after result is divided.
The central line pick-up module divides the processing result figure of module according to depth network, calculates the center for extracting blood vessel
Line simultaneously stores.
The diameter calculation module calculates vessel measurement diameter, ginseng based on the image and vessel centerline after dividing processing
Examine diameter and stenosis rate, comprising:
Diameter calculation module calculates center line at corresponding vessel lumen and hangs down according to the processing result of central line pick-up module
Line counts blood vessel pixel quantity on vertical line and goes out blood vessel diameter to obtain the final product.
A kind of method of the measurement coronarogram as blood vessel diameter based on deep learning provided by the invention, the party
Method is that a kind of measurement method for being automatically applied to DSA image is inputted by obtaining the ongoing DSA image of radiography in real time
Into analysis and processing module, by calculating the measurement diameter for finally providing contrastographic picture and corresponding to coronary artery blood vessel, this method and the side PVA
Method is more objective compared to result, avoid between different doctors, Different hospital due to interpretation method is different and existing difference；This
Method measurement process is not required to manual intervention, and operating method is simple and easy, uses in coronary angigraphy progress for doctor, for doctor
Raw provide objectively refers to Severity of Coronary Artery Stenosis, and whole process is not necessarily to manual intervention, and operating method is simple and easy；Computer measurement result
It is more objective, exclude subjective observation error present in the PVA method of current clinical application；Reach Pixel-level calculated result, phase
It is more more accurate than PVA method.
Detailed description of the invention
Fig. 1 show a kind of measurement coronarogram based on deep learning provided by the invention as blood vessel diameter
One flow chart of embodiment of method.
Fig. 2 show the image data schematic diagram in reading DSA image data provided by the invention.
Fig. 3 show image schematic diagram after segmentation provided by the invention.
Fig. 4 show the center line schematic diagram provided by the invention for extracting blood vessel.
Specific embodiment
Technical solution of the present invention is specifically addressed below, it should be pointed out that technical solution of the present invention is unlimited
Embodiment described in embodiment, those skilled in the art refers to and learns from the content of technical solution of the present invention, in this hair
The improvement and design carried out on the basis of bright, should belong to protection scope of the present invention.
Embodiment one
The embodiment of the present invention one provides a kind of measurement coronarogram based on deep learning as blood vessel diameter
Method, this method are participated in without artificial, the automatic method for calculating coronary artery blood vessel diameter, stenotic lesion stenosis rate.Pass through computer
Read DSA (Digital subtraction angiography, Digital Subtraction angiocardiography) shadow in coronary angigraphy
Picture by image segmentation, extraction center line, calculates the operations such as vertical line calculating image medium vessels diameter, on this basis further
Calculate the stenosis rate at stenotic lesion.In general, doctor can carry out PVA to contrastographic picture when carrying out coronary angigraphy
(note: Physician Visual Assessment, doctor's visually rank) judges coronary stenosis severity.Mass data
It has been shown that, compared with computer-aid method, result variability is larger between different cases between PVA observer.The present invention proposes
Method compared with doctor PVA during surgery, have many advantages, such as that accuracy is high, there is no subjective differences.Specifically, such as Fig. 1
It is shown, the method comprising the steps of S110- step S140:
Step S110, DSA image processing module changes into the DSA image data obtained in real time for subsequent module for processing
Data flow storage is into memory and is sent to depth network segmentation module；
Step S120, the image in the DSA image data that depth network segmentation module will acquire is split processing, by blood
Pipe pixel and background pixel are distinguished；
Step S130, central line pick-up module extracts the vessel centerline distinguished in blood vessel pixel image；
Step S140, it is straight to calculate vessel measurement based on the image and vessel centerline after dividing processing for diameter calculation module
Diameter, reference diameter and stenosis rate.
DSA image data incoming in real time is decoded processing by the DSA image processing module, after decoding process
Image data is written in memory and reads for depth network segmentation module.Wherein, the image data after the decoding process be for
The data flow of subsequent module for processing.
The image in DSA image data that the depth network segmentation module will acquire is split processing, by blood vessel picture
Element is distinguished with background pixel, is specifically included:
Step 1 reads existing model from model database, initializes depth network, starts primary training or test
Process；
Step 2, training process: the image data (such as Fig. 2) and labeled data in DSA image data are read, by depth
Network query function data are propagated to network front end, are exported result calculating parameter renewal amount according to last network and are updated network ginseng
Number will update obtained parameter model and store into model database for use next time；
Step 3, test process: reading the image data in DSA image data, by depth network query function data to network
Front end is propagated, and is obtained network to the end and is exported image (such as Fig. 3) after result is divided.
The central line pick-up module divides the processing result figure of module according to depth network, calculates the center for extracting blood vessel
Line simultaneously stores, and extraction effect is as shown in Figure 4.
The diameter calculation module calculates vessel measurement diameter, ginseng based on the image and vessel centerline after dividing processing
Examine diameter and stenosis rate, comprising:
Diameter calculation module calculates center line at corresponding vessel lumen and hangs down according to the processing result of central line pick-up module
Line counts blood vessel pixel quantity on vertical line and goes out blood vessel diameter to obtain the final product.
A kind of method of the measurement coronarogram as blood vessel diameter based on deep learning provided by the invention, the party
Method is that a kind of measurement method for being automatically applied to DSA image is inputted by obtaining the ongoing DSA image of radiography in real time
Into analysis and processing module, by calculating the measurement diameter for finally providing contrastographic picture and corresponding to coronary artery blood vessel, this method and the side PVA
Method is more objective compared to result, avoid between different doctors, Different hospital due to interpretation method is different and existing difference；This
Method measurement process is not required to manual intervention, and operating method is simple and easy, uses in coronary angigraphy progress for doctor, for doctor
Raw provide objectively refers to Severity of Coronary Artery Stenosis, and whole process is not necessarily to manual intervention, and operating method is simple and easy；Computer measurement result
It is more objective, exclude subjective observation error present in the PVA method of current clinical application；Reach Pixel-level calculated result, phase
It is more more accurate than PVA method.
Disclosed above is only several specific embodiments of the invention, and still, the present invention is not limited to above-described embodiment,
The changes that any person skilled in the art can think of should all fall into protection scope of the present invention.
Claims (5)
1. a kind of measurement coronarogram based on deep learning is as the method for blood vessel diameter, which is characterized in that this method
Include:
The DSA image data obtained in real time is changed into and is arrived for the data flow storage of subsequent module for processing by DSA image processing module
In memory and it is sent to depth network segmentation module；
The image in DSA image data that depth network segmentation module will acquire is split processing, by blood vessel pixel and background
Pixel is distinguished；
Central line pick-up module extracts the vessel centerline distinguished in blood vessel pixel image；
Diameter calculation module based on the image and vessel centerline after dividing processing, calculate vessel measurement diameter, reference diameter with
And stenosis rate.
2. the method as described in claim 1, which is characterized in that the DSA image that the DSA image processing module will be passed in real time
Data are decoded processing, read module is divided for depth network in the image data write-in memory after decoding process.
3. the method as described in claim 1, which is characterized in that the DSA image number that the depth network segmentation module will acquire
Image in is split processing, and blood vessel pixel and background pixel are distinguished, specifically included:
Step 1 reads existing model from model database, initializes depth network, starts primary training or test process；
Step 2, training process: the image data and labeled data in DSA image data are read, by depth network query function data
It is propagated to network front end, result calculating parameter renewal amount is exported according to last network and updates network parameter, update is obtained
Parameter model store into model database for next time use；
Step 3, test process: reading the image data in DSA image data, by depth network query function data to network front end
It propagates, obtains network to the end and export image after result is divided.
4. method as claimed in claim 1 or 3, which is characterized in that the central line pick-up module is divided according to depth network
The processing result figure of module calculates the center line for extracting blood vessel and storage.
5. the method as described in claim 1, which is characterized in that the diameter calculation module based on after dividing processing image and
Vessel centerline calculates vessel measurement diameter, reference diameter and stenosis rate, comprising:
Diameter calculation module calculates center line vertical line at corresponding vessel lumen, system according to the processing result of central line pick-up module
It counts blood vessel pixel quantity on vertical line and goes out blood vessel diameter to obtain the final product.
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| CN110223271A (en) * | 2019-04-30 | 2019-09-10 | 深圳市阅影科技有限公司 | The automatic horizontal collection dividing method and device of blood-vessel image |
| CN110310256A (en) * | 2019-05-30 | 2019-10-08 | 上海联影智能医疗科技有限公司 | Coronary stenosis detection method, device, computer equipment and storage medium |
| CN110889896A (en) * | 2019-11-11 | 2020-03-17 | 苏州润迈德医疗科技有限公司 | Method, device and system for obtaining angiostenosis lesion interval and three-dimensional synthesis |
| CN111462047A (en) * | 2020-03-06 | 2020-07-28 | 深圳睿心智能医疗科技有限公司 | Blood vessel parameter measuring method, blood vessel parameter measuring device, computer equipment and storage medium |
| JP2021058281A (en) * | 2019-10-03 | 2021-04-15 | キヤノンメディカルシステムズ株式会社 | X-ray diagnostic apparatus, medical image processing device and program |
| CN113592939A (en) * | 2021-07-20 | 2021-11-02 | 燕山大学 | Deep learning method for judging size of narrow blood vessel based on coronary angiography image |
| TWI790508B (en) * | 2020-11-30 | 2023-01-21 | 宏碁股份有限公司 | Blood vessel detecting apparatus and blood vessel detecting method based on image |
| WO2023130663A1 (en) * | 2022-01-07 | 2023-07-13 | 乐普（北京）医疗器械股份有限公司 | Coronary angiogram quantitative analysis method and apparatus based on angiogram video |
| CN116649996A (en) * | 2023-07-28 | 2023-08-29 | 杭州脉流科技有限公司 | Method and device for obtaining intracranial arterial stenosis hemodynamic parameters |
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| CN110223271A (en) * | 2019-04-30 | 2019-09-10 | 深圳市阅影科技有限公司 | The automatic horizontal collection dividing method and device of blood-vessel image |
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| CN110889896B (en) * | 2019-11-11 | 2024-03-22 | 苏州润迈德医疗科技有限公司 | Method, device and system for acquiring vascular stenosis interval and three-dimensional synthesis |
| CN111462047A (en) * | 2020-03-06 | 2020-07-28 | 深圳睿心智能医疗科技有限公司 | Blood vessel parameter measuring method, blood vessel parameter measuring device, computer equipment and storage medium |
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| CN113592939A (en) * | 2021-07-20 | 2021-11-02 | 燕山大学 | Deep learning method for judging size of narrow blood vessel based on coronary angiography image |
| WO2023130663A1 (en) * | 2022-01-07 | 2023-07-13 | 乐普（北京）医疗器械股份有限公司 | Coronary angiogram quantitative analysis method and apparatus based on angiogram video |
| CN116649996A (en) * | 2023-07-28 | 2023-08-29 | 杭州脉流科技有限公司 | Method and device for obtaining intracranial arterial stenosis hemodynamic parameters |
| CN116649996B (en) * | 2023-07-28 | 2023-10-31 | 杭州脉流科技有限公司 | Method and device for obtaining intracranial arterial stenosis hemodynamic parameters |
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