CN112307804A - Blood vessel state evaluation method and blood vessel state evaluation device - Google Patents
Blood vessel state evaluation method and blood vessel state evaluation device Download PDFInfo
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Abstract
The invention provides a blood vessel state evaluation method and a blood vessel state evaluation device. The method comprises the following steps: obtaining at least one angiographic image corresponding to a target user; selecting a target image from the angiographic images; judging the blood vessel type of the target user according to the distribution state of the target blood vessel pattern in the target image; establishing a vessel topology corresponding to the target vessel pattern, which includes information of widths of vessels in the target vessel pattern and information of vessel bifurcation points in the target vessel pattern; and automatically analyzing the blood vessel state of the target user according to the blood vessel type and the blood vessel topological structure.
Description
Technical Field
The present invention relates to a physiological state evaluation technique based on image analysis, and more particularly, to a blood vessel state evaluation method and a blood vessel state evaluation device.
Background
With the change of modern dietary habits, the cardiovascular diseases have the tendency of being younger. Cardiovascular obstruction can cause myocardial infarction, and acute myocardial infarction often causes life loss, so that it is an unbearable matter to keep the cardiovascular smooth. Generally, if cardiovascular blockage occurs, in addition to taking medicine to control the disease condition, the cardiovascular blockage can be controlled by cardiac catheter operation in cardiology department, balloon expansion or stent placement, and more serious coronary artery bypass operation in cardiac surgery can be selected. In addition, the SYNTAX score is an evaluation method for stent placement or bypass surgery by calculating the degree of occlusion of the heart vessels by angiography. However, the SYNTAX scoring mechanism is very complicated, and the doctor or the examiner must determine the state of the blood vessel from the angiographic image and perform a complicated scoring procedure.
Disclosure of Invention
The invention provides a blood vessel state evaluation method and a blood vessel state evaluation device, which can effectively improve the evaluation efficiency of the blood vessel state.
An embodiment of the present invention provides a vascular condition assessment method, including: obtaining at least one angiographic image corresponding to a target user; selecting a target image from the at least one angiographic image; judging the blood vessel type of the target user according to the distribution state of the target blood vessel pattern in the target image; establishing a vessel topology corresponding to the target vessel pattern, which includes information of widths of vessels in the target vessel pattern and information of vessel bifurcation points in the target vessel pattern; and automatically analyzing the blood vessel state of the target user according to the blood vessel type and the blood vessel topological structure.
An embodiment of the present invention further provides a blood vessel state evaluation device, which includes a storage device and a processor. The storage device is used for storing at least one angiographic image corresponding to a target user. The processor is coupled to the storage device and runs an image processing module. The processor is configured to select a target image from the at least one angiographic image. The processor is further used for judging the blood vessel type of the target user according to the distribution state of the target blood vessel pattern in the target image. The processor is also configured to establish a vessel topology corresponding to the target vessel pattern, including information of widths of vessels in the target vessel pattern and information of vessel bifurcation points in the target vessel pattern. The processor is further configured to automatically analyze a vascular condition of the target user based on the blood vessel type and the blood vessel topology.
Based on the above, after obtaining at least one angiographic image corresponding to the target user, the target image may be selected from the at least one angiographic image. According to the distribution state of the target blood vessel pattern in the target image, the blood vessel type of the target user can be judged. In addition, a vessel topology corresponding to the target vessel pattern may be established to provide information of the width of the vessels in the target vessel pattern and information of vessel bifurcation points in the target vessel pattern. Then, based on the vessel type and the vessel topology, the vessel state of the target user can be automatically analyzed. Therefore, the evaluation efficiency of the vascular state can be effectively improved.
In order to make the aforementioned and other features and advantages of the invention more comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a schematic view of a blood vessel state evaluation device according to an embodiment of the present invention.
Fig. 2 is a schematic diagram illustrating the selection of a target image and the determination of the blood vessel type of a target user according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a preprocessed target blood-vessel pattern according to an embodiment of the invention.
Fig. 4 is a schematic diagram illustrating division of a plurality of image areas according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a vessel topology according to an embodiment of the present invention.
Fig. 6 is a schematic diagram illustrating scoring segments corresponding to different scoring rules according to an embodiment of the present invention.
Fig. 7 is a diagram illustrating a partition scoring segment according to an embodiment of the present invention.
FIG. 8 is a schematic diagram illustrating an analysis report in accordance with an embodiment of the present invention.
Fig. 9 is a flowchart illustrating a blood vessel state evaluation method according to an embodiment of the present invention.
The reference numbers illustrate:
10: vascular condition evaluation device
101: processor with a memory having a plurality of memory cells
102: storage device
103: image processing module
21: picture file
21(0) to 21(n), 22, 31, 41, 51 to 54, 71: image of a person
201: left side advantage
202: advantage of right side
401. 511: target blood vessel pattern
410. 420: image area
521: framework
531: contour profile
541: point of vessel bifurcation
61. 62: scoring rules
701-705: scoring segment
81: evaluating information
S901 to S905: step (ii) of
Detailed Description
Fig. 1 is a schematic view of a blood vessel state evaluation device according to an embodiment of the present invention. Referring to fig. 1, in an embodiment, the device (also referred to as a blood vessel state evaluation device) 10 may be any electronic device or computer device with image analysis and calculation functions. In another embodiment, the apparatus 10 may also be a cardiovascular condition examination device or an image acquisition device for cardiovascular imaging. The device 10 may be used to automatically analyze an angiographic image of a user (also referred to as a target user) and automatically generate assessment information reflecting the vascular status of the target user. In one embodiment, a contrast agent may be injected into and photographed against a target user's heart vessel (e.g., coronary artery) to obtain the angiographic image.
The device 10 includes a processor 101, a storage device 102, and an image processing module 103. The processor 101 is coupled to the storage device 102 and the image processing module 103. The Processor 101 may be a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or other Programmable general purpose or special purpose microprocessor, a Digital Signal Processor (DSP), a Programmable controller, an Application Specific Integrated Circuit (ASIC), a Programmable Logic Device (PLD), or other similar devices or combinations thereof. The processor 101 may be responsible for the overall or partial operation of the device 10.
The storage device 102 is used for storing images (i.e., angiographic images) and other data. Storage 102 may include volatile storage media and nonvolatile storage media. The volatile storage medium may include a Random Access Memory (RAM), and the non-volatile storage medium may include a Read Only Memory (ROM), a Solid State Disk (SSD), or a conventional hard disk (HDD), etc.
The image processing module 103 is used for identifying and/or comparing patterns in the images by performing image processing on the images stored in the storage device 102. The image processing module 103 may be implemented as a software module, a firmware module, or a hardware circuit. For example, in one embodiment, the image processing module 103 may include at least one Graphics Processor (GPU) or similar processing chip to perform the image processing. Alternatively, in one embodiment, the image processing module 103 is a program code that can be loaded into the storage device 102 and executed by the processor 101.
In an embodiment, the image processing module 103 does not have an artificial intelligence architecture such as machine learning and/or deep learning. In an embodiment, the image processing module 103 may have an artificial intelligence architecture such as machine learning and/or deep learning and may continuously improve its image processing performance through training. In an embodiment, the device 10 may also include input/output devices such as a mouse, keyboard, display, microphone, speaker, or network interface card, and the type of input/output devices is not limited thereto.
Fig. 2 is a schematic diagram illustrating the selection of a target image and the determination of the blood vessel type of a target user according to an embodiment of the present invention. Referring to fig. 1 and 2, in an embodiment, the storage device 102 can store a plurality of images 21(0) -21 (n). The images 21(0) -21 (n) may belong to one or more picture files 21. Images 21(0) to 21(n) are all angiographic images corresponding to the same target user. The processor 101 may select an image (also referred to as a target image) 22 from the images 21(0) -21 (n).
In one embodiment, the processor 101 may analyze the images 21(0) - (21 (n) through the image processing module 103. From the analysis results, the processor 101 may obtain the occupation ratio of the blood vessel pattern (also referred to as a first blood vessel pattern) in each of the images 21(0) -21 (n). For example, this ratio may include the ratio of blood vessel pattern to background pixels (or the entire image) in one of images 21(0) -21 (n). The processor 101 may select the image 22 according to this ratio. For example, the processor 101 may select one or more of the images 21(0) -21 (n) in which the blood vessel pattern is highest or higher (e.g., higher than a threshold value) in the image as the image 22.
After selecting the image 22, the processor 101 may determine the blood vessel type of the target user according to the distribution state of the blood vessel pattern (also referred to as a target blood vessel pattern) in the image 22. For example, the processor 101 may analyze the image 22 through the image processing module 103 to identify the blood vessel type of the target user as left dominant (left dominant) 201 or right dominant (right dominant) 202. For example, the left side advantage 201 and the right side advantage 202 may reflect two different types of right coronary arteries. In addition, in an embodiment, the processor 101 may pre-process the image 22 through the image processing module 103 to obtain a clearer target blood vessel pattern. The target blood vessel pattern generated by the pre-processing can be used for subsequent image processing and analysis.
Fig. 3 is a schematic diagram of a preprocessed target blood-vessel pattern according to an embodiment of the invention. Referring to fig. 2 and 3, the preprocessing may include performing morphology (morphology) processing, adaptive threshold (adaptive threshold) processing, and maximum connected component (find connected component) processing on the image 22 to obtain the binarized image 31. For example, in the binarized image 31, pixels within the area marked with the bottom of the mesh may correspond to a bit of "1" to represent the target blood vessel pattern, and pixels of the background (i.e., white portion) may correspond to a bit of "0" to be distinguished from the target blood vessel pattern.
In an embodiment, the processor 101 may segment the target image into a plurality of image regions. Then, the processor 101 may determine whether the blood vessel type of the target user is the left side dominance 201 or the right side dominance 202 according to the distribution state of the target blood vessel pattern in such image regions.
Fig. 4 is a schematic diagram illustrating division of a plurality of image areas according to an embodiment of the present invention. Referring to fig. 4, the image 41 is used to represent a target image (e.g., the image 22 of fig. 2). The image 41 may be divided into a plurality of image areas. For example, the image regions may be arranged in a grid-like manner, as shown in fig. 4. However, in other embodiments, the divided image areas may have other shapes and/or be arranged in other forms, and the invention is not limited thereto. According to the distribution state of the target blood vessel pattern 401 in such image regions, whether the blood vessel type of the target user is the left side dominant or the right side dominant can be determined.
In one embodiment, the processor 101 may determine whether the ratio of the target blood vessel pattern in some image areas in the target image is higher (or lower) than a predetermined value and determine the blood vessel type of the target user according to the determination result. Taking fig. 4 as an example, the processor 101 may determine whether the occupation ratio of the target blood vessel pattern 401 in the image area (also referred to as the first image area) 410 of the upper right half of the image 41 is smaller than a first preset value and/or determine whether the occupation ratio of the target blood vessel pattern 401 in the image area (also referred to as the second image area) 420 of the lower right half of the image 41 is larger than a second preset value. If the ratio of the target blood vessel pattern 401 in the image area 410 is smaller than the first preset value and the ratio of the target blood vessel pattern 401 in the image area 420 is not larger than the second preset value, the processor 101 may determine that the blood vessel type of the target user is the left dominant blood vessel 201 in fig. 2. Alternatively, if the ratio of the target blood vessel pattern 401 in the image area 410 is smaller than the first preset value and the ratio of the target blood vessel pattern 401 in the image area 420 is larger than the second preset value, the processor 101 may determine that the blood vessel type of the target user is the right side dominant 202 of fig. 2. In addition, if the ratio of the target blood vessel pattern 401 in the image area 410 is not less than the first preset value, the processor 101 may determine that the target image is incorrect and may reselect another image as the target image.
On the other hand, from the target image, the processor 101 may establish a vessel topology corresponding to the target vessel pattern. The vessel topology may include information of widths of vessels in the target vessel pattern and information of vessel bifurcation points in the target vessel pattern. The vessel topology may also contain other useful information, and the invention is not limited thereto.
Fig. 5 is a schematic diagram of a vessel topology according to an embodiment of the present invention. Referring to fig. 1 and 5, an image 51 is used to represent a target image. The processor 101 may analyze the target blood vessel pattern 511 in the image 51 by the image processing module 103 to obtain images 52-54. The image 52 may reflect the skeleton 521 of the target blood vessel pattern 511. The image 53 may reflect the contour 531 of the target blood-vessel pattern 511. The image 54 may reflect at least one vessel bifurcation 541 in the target vessel pattern 511.
In an embodiment, the processor 101 may obtain the width of at least one blood vessel in the target blood vessel pattern 511 according to the spacing between the skeleton 521 and the outline 531. For example, the processor 101 may compare the skeleton 521 with the outline 531 to obtain the width between the skeleton 521 and the outline 531 one by one, thereby obtaining the width of at least one blood vessel in the target blood vessel pattern 511.
In an embodiment, the processor 101 may filter the skeleton 521 in the image 52 using at least one cross-point model. For example, the bifurcation model may include a T-bifurcation model, a Y-bifurcation model, an X-bifurcation model, etc., to identify at least one type of bifurcation in the framework 521. The processor 101 may obtain a vessel bifurcation point 541 in the target vessel pattern 511 according to the filtering result.
After obtaining the blood vessel type and the blood vessel topology of the target user, the processor 101 may automatically analyze the blood vessel status of the target user according to the blood vessel type and the blood vessel topology. For example, the processor 101 may determine a scoring rule based on the determined vessel type. The scoring rule may correspond to one of the left side advantage 201 and the right side advantage 202 of fig. 2. The processor 101 may divide the vessel topology into a plurality of scoring segments according to the scoring rule. Then, the processor 101 may estimate the blood vessel state of the target user according to the occlusion state of the target blood vessel pattern in the scoring sections.
Fig. 6 is a schematic diagram illustrating scoring segments corresponding to different scoring rules according to an embodiment of the present invention. Referring to fig. 2 and 6, if the determined blood vessel type is left dominant 201, a scoring rule (also referred to as a first scoring rule) 61 may be employed to score the blood vessel occlusion status in a plurality of scoring segments labeled as values 1-15. Alternatively, if the determined vessel type is the right side superiority 202, another scoring rule (also referred to as a second scoring rule) 62 may be employed to score the vessel occlusion status within a plurality of scoring segments labeled as values 1-15, 16 and 16 a-16 c.
Fig. 7 is a diagram illustrating a partition scoring segment according to an embodiment of the present invention. Referring to fig. 6 and 7, taking right superiority as an example, after determining that the blood vessel type of the target user is right superiority, the scoring rule 62 may be adopted. According to the scoring rules 62, a target blood vessel image (or a blood vessel topology corresponding to the target blood vessel image) in the image 71 (i.e., the target image) may be divided into scoring segments 701-705. The scoring segment 701 corresponds to segment 1 indicated by scoring rule 62, the scoring segment 702 corresponds to segment 2 indicated by scoring rule 62, the scoring segment 703 corresponds to segment 3 indicated by scoring rule 62, the scoring segment 704 corresponds to segment 4 indicated by scoring rule 62, and the scoring segment 705 corresponds to segments 16, 16 a-16 c indicated by scoring rule 62. The occlusion status of the blood vessels within the scoring segments 701-705 can then be analyzed to assess the status of the blood vessels of the target user. In addition, if the target image is an angiographic image of the left main stem, left anterior descending branch and left circumflex approach of the heart of the target user, the scoring segments can be divided into the target vessel image (or vessel topology) according to the segments 5-15 indicated by the scoring rules 61 or 62.
In an embodiment, the processor 101 may analyze the width and/or the degree of occlusion of the blood vessels within a certain scored section through the image processing module 103. Based on the analysis results, the vascular status of the target user can be determined. For example, the processor 101 may analyze whether the blood vessel in a certain scoring segment has lesions such as complete Occlusion (Total Occlusion), Trifurcation lesion (Trifurcation), Bifurcation lesion (Bifurcation), ostium lesion (Aorto-spatial lesion), Severe distortion (Severe tortuosity) or Severe Calcification (Heavy Calcification) according to the information of the width of the blood vessel and the information of the Bifurcation point presented in the images 52-54 of fig. 5 and the scored segment. Such lesions are defined, for example, in the SYNTAX scoring criteria. The processor 101 may generate evaluation information to reflect the blood vessel state of the target user according to the analysis result.
FIG. 8 is a schematic diagram illustrating an analysis report in accordance with an embodiment of the present invention. Referring to fig. 8, the evaluation information 81 can be generated according to the analysis result of the target image, and the operation details of the related analysis are described above. The evaluation information 81 may be stored in the storage device 102 of fig. 1 and may be output (e.g., presented on a display) via an input/output interface.
In the present embodiment, the evaluation information 81 may record whether any lesion among the lesions 0 to 19 appears in the blood vessel in the score segments 1 to 15. If the analysis result reflects that the blood vessels in a score segment (e.g., score segment 1) appear to be a lesion (e.g., lesion 0), the cross-column between the score segment and the lesion (e.g., score segment 1 and lesion 0) may be recorded as T. Alternatively, if the analysis result reflects that the blood vessel in a score segment (e.g., score segment 2) does not present a lesion (e.g., lesion 19), the cross-column between the score segment and the lesion (e.g., score segment 2 and lesion 19) may be recorded as F. Thus, the analysis report 81 can clearly reflect the blood vessel state of the target user.
It should be noted that, in one embodiment, the evaluation information 81 may also record the association information between at least one scoring segment and at least one lesion in other forms. In another embodiment, the evaluation information 81 may also record more information describing the blood vessel status of the target user, such as the probability of a lesion occurring in a score segment, and the like, which is not limited by the invention.
Fig. 9 is a flowchart illustrating a blood vessel state evaluation method according to an embodiment of the present invention. Referring to fig. 9, in step S901, at least one angiographic image corresponding to a target user is obtained. In step S902, a target image is selected from the angiographic images. In step S903, the blood vessel type of the target user is determined according to the distribution state of the target blood vessel pattern in the target image. In step S904, a vessel topology corresponding to the target vessel pattern is established. The vessel topology includes information of widths of vessels in the target vessel pattern and information of vessel bifurcation points in the target vessel pattern. In step S905, the blood vessel status of the target user is automatically analyzed according to the blood vessel type and the blood vessel topology.
However, the steps in fig. 9 have been described in detail above, and are not described again here. It is to be noted that the steps in fig. 9 can be implemented as a plurality of program codes or circuits, and the invention is not limited thereto. In addition, the method of fig. 9 may be used with the above exemplary embodiments, or may be used alone, and the invention is not limited thereto.
In summary, after obtaining at least one angiographic image corresponding to a target user, a target image may be selected from the at least one angiographic image. According to the distribution state of the target blood vessel pattern in the target image, the blood vessel type of the target user can be judged. In addition, a vessel topology corresponding to the target vessel pattern may be established to provide information of the width of the vessels in the target vessel pattern and information of vessel bifurcation points in the target vessel pattern. Then, based on the vessel type and the vessel topology, the vessel state of the target user can be automatically analyzed. Therefore, the evaluation efficiency of the vascular state can be effectively improved.
In one embodiment, the vascular condition assessment method is a non-medical method. For example, in one embodiment, the vascular condition assessment method may be performed by a general user without an associated medical context through a specific device (e.g., the vascular condition assessment device) and generate corresponding assessment information. The evaluation information may reflect the possible physiological state of the user for reference by the user. In addition, in one embodiment, the vascular condition assessment device may also be used by a person (e.g., a physician or a detector) with an associated medical context to provide additional verification information.
Although the present invention has been described with reference to the above embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention.
Claims (12)
1. A vascular condition assessment method, comprising:
obtaining at least one angiographic image corresponding to a target user;
selecting a target image from the at least one angiographic image;
judging the blood vessel type of the target user according to the distribution state of the target blood vessel pattern in the target image;
establishing a vessel topology corresponding to the target vessel pattern, which includes information of widths of vessels in the target vessel pattern and information of vessel bifurcation points in the target vessel pattern; and
and automatically analyzing the blood vessel state of the target user according to the blood vessel type and the blood vessel topological structure.
2. The vascular condition evaluation method according to claim 1, wherein the step of selecting the target image from the at least one angiographic image comprises:
obtaining a proportion of a first blood vessel pattern in a first image of the at least one angiographic image; and
selecting the target image from the at least one angiographic image according to the ratio.
3. The blood vessel state evaluation method according to claim 1, wherein the step of judging the blood vessel type of the target user from the distribution state of the target blood vessel pattern in the target image comprises:
dividing the target image into a plurality of image regions; and
and judging whether the blood vessel type of the target user is a left side advantage or a right side advantage according to the distribution state of the target blood vessel pattern in the plurality of image areas.
4. The vascular condition evaluation method of claim 1, wherein the step of establishing the vascular topology corresponding to the target vascular pattern comprises:
obtaining a skeleton of the target blood vessel pattern;
obtaining a contour of the target blood vessel pattern; and
obtaining the width of the vessel in the target vessel pattern according to a spacing between the skeleton and the contour.
5. The vascular condition assessment method of claim 4, wherein the step of establishing the vascular topology corresponding to the target vascular pattern further comprises:
filtering the skeleton using a bifurcation point model; and
and obtaining the vessel bifurcation point in the target vessel pattern according to the filtering result.
6. The vascular condition assessment method of claim 1, wherein the step of automatically analyzing the vascular condition of the target user based on the blood vessel type and the blood vessel topology further comprises:
determining a scoring rule according to the blood vessel type;
dividing the vessel topological structure into a plurality of scoring sections according to the scoring rules; and
evaluating the vascular status of the target user according to the occlusion status of the target vascular pattern in the plurality of scoring segments.
7. A blood vessel state evaluation device comprising:
a storage device for storing at least one angiographic image corresponding to a target user;
a processor coupled to the storage device and running an image processing module,
wherein the processor is configured to select a target image from the at least one angiographic image,
the processor is further used for judging the blood vessel type of the target user according to the distribution state of the target blood vessel pattern in the target image,
the processor is further configured to establish a vessel topology corresponding to the target vessel pattern, including information of widths of vessels in the target vessel pattern and information of vessel bifurcation points in the target vessel pattern, and
the processor is further configured to automatically analyze a vascular condition of the target user based on the blood vessel type and the blood vessel topology.
8. The vascular condition assessment device of claim 7, wherein the processor selecting the target image from the at least one angiographic image comprises:
obtaining a proportion of a first blood vessel pattern in a first image of the at least one angiographic image; and
selecting the target image from the at least one angiographic image according to the ratio.
9. The blood vessel state evaluation device according to claim 7, wherein the operation of the processor judging the blood vessel type of the target user from the distribution state of the target blood vessel pattern in the target image comprises:
dividing the target image into a plurality of image regions; and
and judging whether the blood vessel type of the target user is a left side advantage or a right side advantage according to the distribution state of the target blood vessel pattern in the plurality of image areas.
10. The vascular condition assessment device of claim 7, wherein the operation of the processor to establish the vessel topology corresponding to the target vessel pattern comprises:
obtaining a skeleton of the target blood vessel pattern;
obtaining a contour of the target blood vessel pattern; and
obtaining the width of the vessel in the target vessel pattern according to a spacing between the skeleton and the contour.
11. The vascular condition assessment device of claim 10, wherein the operation of the processor to establish the vessel topology corresponding to the target vessel pattern further comprises:
filtering the skeleton using a bifurcation point model; and
and obtaining the vessel bifurcation point in the target vessel pattern according to the filtering result.
12. The vascular condition assessment device of claim 7, wherein the operation of the processor to automatically analyze the vascular condition of the target user based on the blood vessel type and the blood vessel topology further comprises:
determining a scoring rule according to the blood vessel type;
dividing the vessel topological structure into a plurality of scoring sections according to the scoring rules; and
evaluating the vascular status of the target user according to the occlusion status of the target vascular pattern in the plurality of scoring segments.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090328239A1 (en) * | 2006-07-31 | 2009-12-31 | Bio Tree Systems, Inc. | Blood vessel imaging and uses therefor |
| CN104867147A (en) * | 2015-05-21 | 2015-08-26 | 北京工业大学 | SYNTAX automatic scoring method based on coronary angiogram image segmentation |
| CN106886993A (en) * | 2017-02-07 | 2017-06-23 | 贺永明 | 17 sections cardiac muscle points-scoring system |
| CN108577883A (en) * | 2018-04-03 | 2018-09-28 | 上海交通大学 | A kind of Screening for coronary artery disease device, screening system and signal characteristic extracting methods |
| CN109350102A (en) * | 2017-11-28 | 2019-02-19 | 北京昆仑医云科技有限公司 | For determining the method, apparatus and system of Vascular capacitance and coronary blood flow |
| CN109658407A (en) * | 2018-12-27 | 2019-04-19 | 上海联影医疗科技有限公司 | Methods of marking, device, server and the storage medium of coronary artery pathological changes |
-
2019
- 2019-07-25 CN CN201910675498.4A patent/CN112307804A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090328239A1 (en) * | 2006-07-31 | 2009-12-31 | Bio Tree Systems, Inc. | Blood vessel imaging and uses therefor |
| CN104867147A (en) * | 2015-05-21 | 2015-08-26 | 北京工业大学 | SYNTAX automatic scoring method based on coronary angiogram image segmentation |
| CN106886993A (en) * | 2017-02-07 | 2017-06-23 | 贺永明 | 17 sections cardiac muscle points-scoring system |
| CN109350102A (en) * | 2017-11-28 | 2019-02-19 | 北京昆仑医云科技有限公司 | For determining the method, apparatus and system of Vascular capacitance and coronary blood flow |
| CN108577883A (en) * | 2018-04-03 | 2018-09-28 | 上海交通大学 | A kind of Screening for coronary artery disease device, screening system and signal characteristic extracting methods |
| CN109658407A (en) * | 2018-12-27 | 2019-04-19 | 上海联影医疗科技有限公司 | Methods of marking, device, server and the storage medium of coronary artery pathological changes |
Non-Patent Citations (2)
| Title |
|---|
| 拉普、克拉科夫: "《心脏导管手册诊断与介入治疗》", 31 August 2015, 济南：山东科学技术出版社, pages: 249 - 57 * |
| 谷国强 等: "冠状动脉造影及介入治疗", 北京：科学技术文献出版社, pages: 249 * |
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