CN110298846A - Based on polytypic coronary artery dividing method, device and storage equipment - Google Patents
Based on polytypic coronary artery dividing method, device and storage equipment Download PDFInfo
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- CN110298846A CN110298846A CN201910568203.3A CN201910568203A CN110298846A CN 110298846 A CN110298846 A CN 110298846A CN 201910568203 A CN201910568203 A CN 201910568203A CN 110298846 A CN110298846 A CN 110298846A
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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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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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
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
Abstract
The embodiment of the invention discloses one kind to be based on polytypic coronary artery dividing method, device and computer memory device, coronary artery volume data is split first with more sorter network models, segmentation result is obtained, the segmentation result includes at least myocardial region, aorta regions and coarse coronary artery region；Buffering blood vessel estimation range is determined according to the myocardial region in the segmentation result later；It is in progress blood vessel prediction in identified buffering blood vessel estimation range further according to the endpoint in the coarse coronary artery region, obtains blood vessel prediction result；The blood vessel prediction result is finally incorporated to the coarse coronary artery region.
Description
Technical field
The present invention relates to Medical Imaging Technology fields, more particularly to one kind to be based on polytypic coronary artery dividing method, device
And computer memory device.
Background technique
As modern science and technology are grown rapidly, living standards of the people are increasingly improved.Simultaneously as work rhythm adds
Fastly, the disease incidence of various diseases increased, wherein most commonly seen and be typically cardiovascular disease, this is also current medicine
Boundary pays close attention to the most, study and the emphasis of diagnoses and treatment currently, automation reconstructing blood vessel to doctor in the research of cardiovascular disease and
Diagnoses and treatment has important clinical value and practical significance.
However, the coronary artery segmentation stage in automation coronary artery reconstruction process causes very consumption aobvious because using 3d network
It deposits；In addition, divided automatically due to coronary artery causes predicted velocity slower because using 3d network.
Summary of the invention
The embodiment of the present invention is created to effectively overcome the drawbacks described above in the presence of the automatic cutting techniques of prior art coronary artery
It provides to the property made a kind of based on polytypic coronary artery dividing method, device and computer memory device.
According to embodiments of the present invention in a first aspect, providing one kind based on polytypic coronary artery dividing method, this method comprises:
Coronary artery volume data is split using more sorter network models, obtains segmentation result, the segmentation result includes at least cardiac muscle
Region, aorta regions and coarse coronary artery region；Buffering blood vessel Target area is determined according to the myocardial region in the segmentation result
Domain；It is in identified buffering blood vessel estimation range according to the endpoint in the coarse coronary artery region and carries out blood vessel prediction, obtained
Blood vessel prediction result；The blood vessel prediction result is incorporated to the coarse coronary artery region.
According to an embodiment of the present invention, the myocardial region according in the segmentation result determines buffering blood vessel prediction
Region, comprising: expansion and corrosion treatment are carried out to the myocardial region in the segmentation result, obtain myocardium expansion area and cardiac muscle
Corrosion area；It is determined as the region in the myocardium expansion area in addition to the myocardium corrosion area to buffer blood vessel Target area
Domain.
According to an embodiment of the present invention, the endpoint according to the coarse coronary artery region is in identified buffering blood
Blood vessel prediction is carried out in pipe estimation range, obtains blood vessel prediction result, comprising: according to the coarse coarse hat of coronary artery Area generation
The center line point set of arteries and veins；Endpoint is focused to find out from the centerline points；According to preamble point corresponding with the endpoint at endpoint
Determine predicted position；Blood vessel prediction is carried out according to identified predicted position in identified buffering blood vessel estimation range, is obtained
To blood vessel prediction result.
According to an embodiment of the present invention, it is described in identified buffering blood vessel estimation range according to identified prediction
Position carries out blood vessel prediction, comprising: utilizes small-scale model in identified buffering blood vessel estimation range according to determined by
Predicted position carries out blood vessel prediction.
According to an embodiment of the present invention, after the blood vessel prediction result is incorporated to the coarse coronary artery region, institute
State method further include: be in using the endpoint of the blood vessel prediction result determine buffering blood vessel estimation range in continue blood
Pipe prediction.
Second aspect according to embodiments of the present invention also provides a kind of based on polytypic coronary artery segmenting device, described device
Include: segmentation module, for being split using more sorter network models to coronary artery volume data, obtains segmentation result, described point
Result is cut including at least myocardial region, aorta regions and coarse coronary artery region；Determining module, for according to the segmentation result
In myocardial region determine buffering blood vessel estimation range；Blood vessel prediction module, for the endpoint according to the coarse coronary artery region
Blood vessel prediction is carried out in buffering blood vessel estimation range determined by being in, obtains blood vessel prediction result；Merging module is used for institute
It states blood vessel prediction result and is incorporated to the coarse coronary artery region.
According to an embodiment of the present invention, the determining module includes: expansion/erosion unit, for tying to the segmentation
Myocardial region in fruit carries out expansion and corrosion treatment, obtains myocardium expansion area and myocardium corrosion area；Determination unit is used for
It is determined as the region in the myocardium expansion area in addition to the myocardium corrosion area to buffer blood vessel estimation range.
According to an embodiment of the present invention, the blood vessel prediction module includes: generation unit, for according to the coarse hat
The center line point set of the coarse coronary artery of arteries and veins Area generation；Searching unit, for being focused to find out endpoint in the centerline points；It determines single
Member, for determining predicted position according to preamble point corresponding with the endpoint at endpoint；Blood vessel predicting unit, for really
Blood vessel prediction is carried out according to identified predicted position in fixed buffering blood vessel estimation range, obtains blood vessel prediction result.
According to an embodiment of the present invention, the blood vessel predicting unit is also used to using small-scale model identified
It buffers in blood vessel estimation range and blood vessel prediction is carried out according to identified predicted position.
According to an embodiment of the present invention, the blood vessel prediction module is also used to be incorporated to by the blood vessel prediction result
After the coarse coronary artery region, be in using the endpoint of the blood vessel prediction result determine buffering blood vessel estimation range in after
It is continuous to carry out blood vessel prediction.
The third aspect according to embodiments of the present invention, and a kind of computer memory device is provided, the storage equipment includes one
Group computer executable instructions are divided for described in any of the above embodiments based on polytypic coronary artery when executed
Method.
Polytypic coronary artery dividing method, device and computer memory device are based on described in the embodiment of the present invention, it is sharp first
Coronary artery volume data is split with more sorter network models, obtains segmentation result, the segmentation result includes at least myocardium area
Domain, aorta regions and coarse coronary artery region；Buffering blood vessel prediction is determined according to the myocardial region in the segmentation result later
Region；It is in progress blood vessel prediction in identified buffering blood vessel estimation range further according to the endpoint in the coarse coronary artery region,
Obtain blood vessel prediction result；The blood vessel prediction result is finally incorporated to the coarse coronary artery region.In this way, the present invention is by more points
Based on the vessel segmentation of class, by further taking off to buffering blood vessel estimation range is taken off out in myocardial region
The tracking completion blood vessel prediction optimized in buffering blood vessel estimation range out, to effectively overcome coronary artery point in the prior art
Cutting order Duan Yin consumes excessive and slower predicted velocity problem using video memory caused by 3d network.
Detailed description of the invention
The following detailed description is read with reference to the accompanying drawings, above-mentioned and other mesh of exemplary embodiment of the invention
, feature and advantage will become prone to understand.In the accompanying drawings, if showing by way of example rather than limitation of the invention
Dry embodiment, in which:
In the accompanying drawings, identical or corresponding label indicates identical or corresponding part.
Fig. 1 is implementation process schematic diagram one of the embodiment of the present invention based on polytypic coronary artery dividing method；
Fig. 2 is split acquired segmentation result to coronary artery volume data using more sorter network models for the embodiment of the present invention
Schematic diagram；
Fig. 3 is that the embodiment of the present invention buffers showing for blood vessel estimation range according to determined by the myocardial region in segmentation result
It is intended to；
Fig. 4 is that the embodiment of the present invention is in identified buffering blood vessel estimation range according to the endpoint in coarse coronary artery region
Carry out the schematic diagram of blood vessel prediction；
Fig. 5 is shown one application example of the present invention and is illustrated based on the specific implementation flow of polytypic coronary artery dividing method
Figure；
Fig. 6 is composed structure schematic diagram of the embodiment of the present invention based on polytypic coronary artery segmenting device.
Specific embodiment
To keep the purpose of the present invention, feature, advantage more obvious and understandable, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only
It is only a part of the embodiment of the present invention, and not all embodiments.Based on the embodiments of the present invention, those skilled in the art are not having
Every other embodiment obtained under the premise of creative work is made, shall fall within the protection scope of the present invention.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.Moreover, particular features, structures, materials, or characteristics described
It may be combined in any suitable manner in any one or more of the embodiments or examples.In addition, without conflicting with each other, this
The technical staff in field can be by the spy of different embodiments or examples described in this specification and different embodiments or examples
Sign is combined.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic." first " is defined as a result, the feature of " second " can be expressed or hidden
It include at least one this feature containing ground.In the description of the present invention, the meaning of " plurality " is two or more, unless otherwise
Clear specific restriction.
Fig. 1 is implementation process schematic diagram one of the embodiment of the present invention based on polytypic coronary artery dividing method；Please refer to figure
1.It includes: operation 101 that the embodiment of the present invention, which is based on polytypic coronary artery dividing method, using more sorter network models to coronary artery body
Data are split, and obtain segmentation result, and the segmentation result includes at least myocardial region, aorta regions and coarse coronary artery area
Domain；Operation 102 determines buffering blood vessel estimation range according to the myocardial region in the segmentation result；Operation 103, according to described
The endpoint in coarse coronary artery region buffers progress blood vessel prediction in blood vessel estimation range determined by being in, and obtains blood vessel prediction knot
Fruit；Operation 104, is incorporated to the coarse coronary artery region for the blood vessel prediction result.
Wherein, with reference to Fig. 2, after being split using more sorter network models to coronary artery volume data, it is available at least
Segmentation result including myocardial region, aorta regions and coarse coronary artery region three regions.
In operation 102, buffering blood vessel estimation range is determined according to the myocardial region in the segmentation result, it is specific to wrap
It includes: expansion and corrosion treatment is carried out to the myocardial region in the segmentation result, obtain myocardium expansion area and myocardium corrosion region
Domain；It is determined as the region in the myocardium expansion area in addition to the myocardium corrosion area to buffer blood vessel estimation range.Tool
Body, it is to buffer blood vessel prediction by myocardium expansion area and myocardium corrosion area difference region between the two referring to Fig. 3
Region.The buffering blood vessel estimation range has wrapped all coronary arterys space that may be present.Those skilled in the art should understand that
, the range that dilation erosion is carried out to myocardial region centainly includes existing blood vessel segmentation, therefore gives certain buffering, Ji Kebao again
The blood vessel that card segmentation obtains all is fallen within this range.
According to an embodiment of the present invention, the endpoint according to the coarse coronary artery region is in identified buffering blood
Blood vessel prediction is carried out in pipe estimation range, obtains blood vessel prediction result, comprising: according to the coarse coarse hat of coronary artery Area generation
The center line point set of arteries and veins；Endpoint is focused to find out from the centerline points；According to preamble point corresponding with the endpoint at endpoint
Determine predicted position；Blood vessel prediction is carried out according to identified predicted position in identified buffering blood vessel estimation range, is obtained
To blood vessel prediction result.
According to an embodiment of the present invention, it is described in identified buffering blood vessel estimation range according to identified prediction
Position carries out blood vessel prediction, comprising: utilizes small-scale model in identified buffering blood vessel estimation range according to determined by
Predicted position carries out blood vessel prediction.Specifically, it is carried out " tree " through the result to blood vessel in buffering estimation range with reference to Fig. 4
Type prediction tracking completion blood vessel prediction.Specifically, center line extraction based on polytypic segmentation result, will be carried out, it is right
Centerline end point predicted, and with buffering blood vessel estimation range come constrained forecast range.Further, pass through in predicted position
Small-scale model carries out blood vessel prediction, and the blood vessel prediction result is incorporated to the coarse coronary artery region.
According to an embodiment of the present invention, after the blood vessel prediction result is incorporated to the coarse coronary artery region, institute
State method further include: be in using the endpoint of the blood vessel prediction result determine buffering blood vessel estimation range in continue blood
Pipe prediction.Specifically, that is, Fig. 4 is referred to, is predicted using small-scale model to continue blood vessel at the endpoint of blood vessel prediction result,
And with buffering blood vessel estimation range come constrained forecast range.
Polytypic coronary artery dividing method is based on described in the embodiment of the present invention, first with more sorter network models to coronary artery
Volume data is split, and obtains segmentation result, and the segmentation result includes at least myocardial region, aorta regions and coarse coronary artery
Region；Buffering blood vessel estimation range is determined according to the myocardial region in the segmentation result later；Further according to the coarse coronary artery
The endpoint in region buffers progress blood vessel prediction in blood vessel estimation range determined by being in, and obtains blood vessel prediction result；Finally will
The blood vessel prediction result is incorporated to the coarse coronary artery region.In this way, the present invention is using polytypic vessel segmentation as base
Plinth, by taken off out in myocardial region buffering blood vessel estimation range, further in the buffering blood vessel estimation range taken off out
Optimize tracking completion blood vessel prediction, thus effectively overcome in the prior art coronary artery segmentation the stage led because using 3d network
The video memory of cause consumes excessive and slower predicted velocity problem.
Equally, based on polytypic coronary artery dividing method is based on as described above, the embodiment of the present invention provides a kind of meter again
Calculation machine readable storage medium storing program for executing, the computer-readable recording medium storage have program, when said program is executed by a processor, make
It obtains the processor and at least executes operating procedure as described below: operation 101, using more sorter network models to coronary artery volume data
It is split, obtains segmentation result, the segmentation result includes at least myocardial region, aorta regions and coarse coronary artery region；
Operation 102 determines buffering blood vessel estimation range according to the myocardial region in the segmentation result；Operation 103, according to described coarse
The endpoint in coronary artery region buffers progress blood vessel prediction in blood vessel estimation range determined by being in, and obtains blood vessel prediction result；Behaviour
Make 104, the blood vessel prediction result is incorporated to the coarse coronary artery region.
Further, it is based on polytypic coronary artery dividing method based on described above, the embodiment of the present invention also provides one kind
Based on polytypic coronary artery segmenting device, as shown in fig. 6, described device 60 includes: segmentation module 601, for utilizing classification net
Network model is split coronary artery volume data, obtains segmentation result, and the segmentation result includes at least myocardial region, aortic area
Domain and coarse coronary artery region；Determining module 602, for determining buffering blood vessel prediction according to the myocardial region in the segmentation result
Region；Blood vessel prediction module 603, for being in identified buffering blood vessel Target area according to the endpoint in the coarse coronary artery region
Blood vessel prediction is carried out in domain, obtains blood vessel prediction result；Merging module 604, it is described for the blood vessel prediction result to be incorporated to
Coarse coronary artery region.
According to an embodiment of the present invention, the determining module 602 includes: expansion/erosion unit, for the segmentation
As a result the myocardial region in carries out expansion and corrosion treatment, obtains myocardium expansion area and myocardium corrosion area；Determination unit is used
It is determined as buffering blood vessel estimation range in by the region in the myocardium expansion area in addition to the myocardium corrosion area.
According to an embodiment of the present invention, the blood vessel prediction module 603 includes: generation unit, for according to described thick
The center line point set of the rough coarse coronary artery of coronary artery Area generation；Searching unit, for being focused to find out endpoint in the centerline points；Really
Order member, for determining predicted position according to preamble point corresponding with the endpoint at endpoint；Blood vessel predicting unit is used for
Blood vessel prediction is carried out according to identified predicted position in identified buffering blood vessel estimation range, obtains blood vessel prediction result.
According to an embodiment of the present invention, the blood vessel predicting unit is also used to using small-scale model identified
It buffers in blood vessel estimation range and blood vessel prediction is carried out according to identified predicted position.
According to an embodiment of the present invention, the blood vessel prediction module 603, be also used to by the blood vessel prediction result simultaneously
After entering the coarse coronary artery region, it is in and is determined in buffering blood vessel estimation range using the endpoint of the blood vessel prediction result
Continue blood vessel prediction.
It need to be noted that: it is and preceding above to being directed to the description based on polytypic coronary artery segmenting device embodiment
State embodiment of the method shown in FIG. 1 description be it is similar, have with aforementioned embodiment of the method shown in FIG. 1 it is similar beneficial to effect
Fruit, therefore do not repeat them here.For the present invention to based on undisclosed technical detail in polytypic coronary artery segmenting device embodiment,
It please refers to the description of the aforementioned embodiment of the method shown in FIG. 1 of the present invention and understands, to save length, therefore repeat no more.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row
His property includes, so that the process, method, article or the device that include a series of elements not only include those elements, and
And further include other elements that are not explicitly listed, or further include for this process, method, article or device institute it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to do
There is also other identical elements in the process, method of element, article or device.
In several embodiments provided herein, it should be understood that disclosed device and method can pass through it
Its mode is realized.Apparatus embodiments described above are merely indicative, for example, the division of the unit, only
A kind of logical function partition, there may be another division manner in actual implementation, such as: multiple units or components can combine, or
It is desirably integrated into another device, or some features can be ignored or not executed.In addition, shown or discussed each composition portion
Mutual coupling or direct-coupling or communication connection is divided to can be through some interfaces, the INDIRECT COUPLING of equipment or unit
Or communication connection, it can be electrical, mechanical or other forms.
Above-mentioned unit as illustrated by the separation member, which can be or may not be, to be physically separated, aobvious as unit
The component shown can be or may not be physical unit；Both it can be located in one place, and may be distributed over multiple network lists
In member；Some or all of units can be selected to achieve the purpose of the solution of this embodiment according to the actual needs.
In addition, each functional unit in various embodiments of the present invention can be fully integrated in one processing unit, it can also
To be each unit individually as a unit, can also be integrated in one unit with two or more units；It is above-mentioned
Integrated unit both can use formal implementation of hardware, also can use hardware and the form of SFU software functional unit is added to realize.
Those of ordinary skill in the art will appreciate that: realize that all or part of the steps of above method embodiment can pass through
The relevant hardware of program instruction is completed, and program above-mentioned can store in computer-readable storage medium, which exists
When execution, step including the steps of the foregoing method embodiments is executed；And storage medium above-mentioned includes: movable storage device, read-only deposits
The various media that can store program code such as reservoir (Read Only Memory, ROM), magnetic or disk.
If alternatively, the above-mentioned integrated unit of the present invention is realized in the form of software function module and as independent product
When selling or using, it also can store in a computer readable storage medium.Based on this understanding, the present invention is implemented
Substantially the part that contributes to existing technology can be embodied in the form of software products the technical solution of example in other words,
The computer software product is stored in a storage medium, including some instructions are used so that computer equipment (can be with
It is personal computer, server or network equipment etc.) execute all or part of each embodiment the method for the present invention.
And storage medium above-mentioned includes: various Jie that can store program code such as movable storage device, ROM, magnetic or disk
Matter.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.Therefore, protection scope of the present invention should be subject to the protection scope in claims.
Claims (10)
1. one kind is based on polytypic coronary artery dividing method, which is characterized in that the described method includes:
Coronary artery volume data is split using more sorter network models, obtains segmentation result, the segmentation result includes at least
Myocardial region, aorta regions and coarse coronary artery region；
Buffering blood vessel estimation range is determined according to the myocardial region in the segmentation result；
It is in identified buffering blood vessel estimation range according to the endpoint in the coarse coronary artery region and carries out blood vessel prediction, obtained
Blood vessel prediction result；
The blood vessel prediction result is incorporated to the coarse coronary artery region.
2. the method according to claim 1, wherein the myocardial region according in the segmentation result determines
Buffer blood vessel estimation range, comprising:
Expansion and corrosion treatment are carried out to the myocardial region in the segmentation result, obtain myocardium expansion area and myocardium corrosion region
Domain；
It is determined as the region in the myocardium expansion area in addition to the myocardium corrosion area to buffer blood vessel estimation range.
3. the method according to claim 1, wherein the endpoint according to the coarse coronary artery region is in institute
Blood vessel prediction is carried out in determining buffering blood vessel estimation range, obtains blood vessel prediction result, comprising:
According to the center line point set of the coarse coarse coronary artery of coronary artery Area generation；
Endpoint is focused to find out from the centerline points；
Predicted position is determined according to preamble point corresponding with the endpoint at endpoint；
Blood vessel prediction is carried out according to identified predicted position in identified buffering blood vessel estimation range, obtains blood vessel prediction
As a result.
4. according to the method described in claim 3, it is characterized in that, the basis in identified buffering blood vessel estimation range
Identified predicted position carries out blood vessel prediction, comprising:
It is pre- that blood vessel is carried out according to identified predicted position in identified buffering blood vessel estimation range using small-scale model
It surveys.
5. method according to any one of claims 1 to 4, which is characterized in that the blood vessel prediction result is being incorporated to institute
After stating coarse coronary artery region, the method also includes:
It is in using the endpoint of the blood vessel prediction result and determines that continuing blood vessel in buffering blood vessel estimation range predicts.
6. one kind is based on polytypic coronary artery segmenting device, which is characterized in that described device includes:
Divide module and obtains segmentation result, the segmentation for being split using more sorter network models to coronary artery volume data
As a result myocardial region, aorta regions and coarse coronary artery region are included at least；
Determining module, for determining buffering blood vessel estimation range according to the myocardial region in the segmentation result；
Blood vessel prediction module, for being in identified buffering blood vessel estimation range according to the endpoint in the coarse coronary artery region
Blood vessel prediction is carried out, blood vessel prediction result is obtained；
Merging module, for the blood vessel prediction result to be incorporated to the coarse coronary artery region.
7. device according to claim 6, which is characterized in that the determining module includes:
It is swollen to obtain cardiac muscle for carrying out expansion and corrosion treatment to the myocardial region in the segmentation result for expansion/erosion unit
Swollen region and myocardium corrosion area；
Determination unit, for being determined as the region in the myocardium expansion area in addition to the myocardium corrosion area to buffer blood
Pipe estimation range.
8. device according to claim 6, which is characterized in that the blood vessel prediction module includes:
Generation unit, for the center line point set according to the coarse coarse coronary artery of coronary artery Area generation；
Searching unit, for being focused to find out endpoint in the centerline points；
Determination unit, for determining predicted position according to preamble point corresponding with the endpoint at endpoint；
Blood vessel predicting unit, for carrying out blood vessel according to identified predicted position in identified buffering blood vessel estimation range
Prediction, obtains blood vessel prediction result.
9. device according to claim 8, which is characterized in that
The blood vessel predicting unit, be also used to using small-scale model in identified buffering blood vessel estimation range according to it is true
Fixed predicted position carries out blood vessel prediction.
10. a kind of computer memory device, which is characterized in that the storage equipment includes a group of computer-executable instructions, when
Described instruction is performed requires 1-5 described in any item based on polytypic coronary artery dividing method for perform claim.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111312374A (en) * | 2020-01-21 | 2020-06-19 | 上海联影智能医疗科技有限公司 | Medical image processing method, device, storage medium and computer equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109325948A (en) * | 2018-10-09 | 2019-02-12 | 数坤（北京）网络科技有限公司 | A kind of coronary artery dividing method and device based on special area optimization |
| CN109389606A (en) * | 2018-09-30 | 2019-02-26 | 数坤（北京）网络科技有限公司 | A kind of coronary artery dividing method and device |
| US10249048B1 (en) * | 2017-11-15 | 2019-04-02 | Beijing Curacloud Technology Co., Ltd. | Method and system for predicting blood flow features based on medical images |
| CN109872336A (en) * | 2019-03-13 | 2019-06-11 | 数坤（北京）网络科技有限公司 | A kind of blood vessel segmentation method, equipment and computer storage medium |
| CN109903298A (en) * | 2019-03-12 | 2019-06-18 | 数坤（北京）网络科技有限公司 | Restorative procedure, system and the computer storage medium of blood vessel segmentation image fracture |
-
2019
- 2019-06-27 CN CN201910568203.3A patent/CN110298846A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10249048B1 (en) * | 2017-11-15 | 2019-04-02 | Beijing Curacloud Technology Co., Ltd. | Method and system for predicting blood flow features based on medical images |
| CN109389606A (en) * | 2018-09-30 | 2019-02-26 | 数坤（北京）网络科技有限公司 | A kind of coronary artery dividing method and device |
| CN109325948A (en) * | 2018-10-09 | 2019-02-12 | 数坤（北京）网络科技有限公司 | A kind of coronary artery dividing method and device based on special area optimization |
| CN109903298A (en) * | 2019-03-12 | 2019-06-18 | 数坤（北京）网络科技有限公司 | Restorative procedure, system and the computer storage medium of blood vessel segmentation image fracture |
| CN109872336A (en) * | 2019-03-13 | 2019-06-11 | 数坤（北京）网络科技有限公司 | A kind of blood vessel segmentation method, equipment and computer storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111312374A (en) * | 2020-01-21 | 2020-06-19 | 上海联影智能医疗科技有限公司 | Medical image processing method, device, storage medium and computer equipment |
| CN111312374B (en) * | 2020-01-21 | 2024-03-22 | 上海联影智能医疗科技有限公司 | Medical image processing method, medical image processing device, storage medium and computer equipment |
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