CN109087357A - Scan orientation method, apparatus, computer equipment and computer readable storage medium - Google Patents

Scan orientation method, apparatus, computer equipment and computer readable storage medium Download PDF

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Publication number
CN109087357A
CN109087357A CN201810835317.5A CN201810835317A CN109087357A CN 109087357 A CN109087357 A CN 109087357A CN 201810835317 A CN201810835317 A CN 201810835317A CN 109087357 A CN109087357 A CN 109087357A
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plane
image data
point
history
distance
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CN109087357B (en
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宋燕丽
吴迪嘉
滕艳群
徐健
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Shanghai United Imaging Intelligent Healthcare Co Ltd
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Shanghai United Imaging Intelligent Healthcare Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • 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
    • G06T2207/30048—Heart; Cardiac

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  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)

Abstract

The embodiment of the invention discloses a kind of Scan orientation method, apparatus, computer equipment and computer readable storage medium, method includes: the 3 d image data for obtaining position to be positioned；3 d image data is input in preparatory trained location point characteristic model, each position point feature data of location point characteristic model output are obtained；Plane fitting point is determined according to each position point feature data, and plane equation to be positioned is determined according to plane fitting point.The positioning of the plane of scanning motion only can be realized in method provided by the embodiment of the present invention by the 3 d image data at position to be positioned, be applicable to different scanning portions are scanned by different scanning mode situation when positioning, so that the positioning of the plane of scanning motion is more quickly, accurately, the time required to reducing scanning process, improve the accuracy of scan image, and overcome the inaccuracy positioned according to anatomic landmarks point, it is not full-time in prescan range, remain to the positioning for preferably realizing the plane of scanning motion.

Description

Scan orientation method, apparatus, computer equipment and computer readable storage medium
Technical field
The present embodiments relate to medical image scanning technique field more particularly to a kind of Scan orientation method, apparatus, meter Calculate machine equipment and computer readable storage medium.
Background technique
Medical imaging inspection has very important booster action in the clinical diagnosis of disease, is carrying out medical scanning When, it is often necessary to the scanning of position/plane is fixed to subject target area, with the diagnosis of aided disease, treatment etc..
By taking magnetic resonance imaging as an example, for conventional magnetic resonance scan sequences, prescan figure is usually passed through by doctor first As demarcating reference location line by hand after identifying anatomical location, scanning sequence is applied to anatomical location according to reference location line Carry out accurate scan.
By taking cardiac magnetic resonance scans as an example, cardiac magnetic resonance positioning scanning at least needs to hold one's breath for patient 5 times, and respectively three is disconnected Face, multilayer axle position, false two chambers, false four chambers and multilayer short axle, breath-hold scans time at least need 4~5 minutes, and centre also needs to cure Raw six manual positioning, each positioning result can all influence subsequent scanning result, if technician's experience is insufficient, may need Multiple scanning, resetting, positioning time is long and last positioning result may be difficult to meet the needs of clinical diagnosis.Together When, result disunity when carrying out heart positioning scanning by manual positioning method, to the same subject, between different operation person The positioning result of the time different with the same operator can may all have very big difference.It can be seen that existing Scan orientation Mode efficiency is lower, extends the time required for entire scanning process, and due to labour variance, experience difference, may Cause recognition result, calibration result inconsistent, the precision of sweep parameter not can guarantee.
Currently, the positioning of plane can also be scanned according to the anatomical location points at position to be positioned.Specifically, passing through The 3D investigation image for treating positioning position carries out the detection of each anatomical location, is swept by the determination of anatomical fixed position point Retouch plane.For example, investigating the bicuspid valve point that image determines heart area, cardiac apical by 3D, flow out point for left room to aorta, right Room maximum angle point equipotential is set a little, and plane composed by specific location point is determined as the plane of scanning motion.If brchypinacoid is perpendicular to two points The line of valve point and the apex of the heart, by left ventricle center, four Cavity surfaces pass through bicuspid valve and apex of the heart line, and by right ventricle's maximum diameter Point.But these points are that the Position Approximate point come is summed up according to doctors experience, and when actual scanning, according to anatomical location points, institute is true Fixed face may not be optimal face, and certain pre-scan images possible range deficiencies cause certain solution plane location points to lack It loses, so that the position inaccurate of the plane of scanning motion.
Summary of the invention
The embodiment of the invention provides a kind of Scan orientation method, apparatus, computer equipment and computer-readable storage mediums Matter, to realize the positioning for fast and accurately realizing the plane of scanning motion.
In a first aspect, the embodiment of the invention provides a kind of Scan orientation methods, comprising:
Obtain the 3 d image data at position to be positioned；
The 3 d image data is input in preparatory trained location point characteristic model, it is special to obtain the location point Levy each position point feature data of model output；
Plane fitting point is extracted from each location point according to each location point characteristic, and according to described flat Face match point determines plane equation to be positioned.
Second aspect, the embodiment of the invention also provides a kind of Scan orientation devices, comprising:
Data acquisition module, for obtaining the 3 d image data at position to be positioned；
Characteristic module, for the 3 d image data to be input to preparatory trained location point characteristic model In, obtain each position point feature data of the location point characteristic model output；
Plane determining module, for determining plane fitting point according to each position point feature data, and according to plane fitting point Determine plane equation to be positioned.
The third aspect, the embodiment of the invention also provides a kind of computer equipment, the equipment includes:
One or more processors；
Storage device, for storing one or more programs；
When one or more of programs are executed by one or more of processors, so that one or more of processing Device realizes such as Scan orientation method provided by any embodiment of the invention.
Fourth aspect, the embodiment of the invention also provides a kind of computer readable storage mediums, are stored thereon with computer Program realizes such as Scan orientation method provided by any embodiment of the invention when the program is executed by processor.
The 3 d image data that the embodiment of the present invention passes through acquisition position to be positioned；The 3 d image data is input to In preparatory trained location point characteristic model, each position point feature data of the location point characteristic model output are obtained；Root Plane fitting point is determined according to each position point feature data, and plane equation to be positioned is determined according to plane fitting point, the present invention The 3 d image data that Scan orientation method only passes through position to be positioned provided by embodiment can be realized position to be positioned and sweep The positioning for retouching plane is applicable to be scanned different scanning portions by different scanning mode situation when positioning, makes The positioning for obtaining the plane of scanning motion is more quick, accurate, and then reduces scanning process required time, improves the accurate of scan image Degree, and the inaccuracy positioned according to anatomic landmarks point is overcome, it is not full-time in prescan range, it remains to preferable Realize the positioning of the plane of scanning motion.
Detailed description of the invention
Fig. 1 is the flow chart of Scan orientation method provided by the embodiment of the present invention one；
Fig. 2 a is the flow chart of Scan orientation method provided by the embodiment of the present invention two；
Fig. 2 b is to be scanned positioning using apart from computation model in Scan orientation method provided by the embodiment of the present invention Schematic diagram；
Fig. 3 a is the flow chart of Scan orientation method provided by the embodiment of the present invention three；
Fig. 3 b is that the training process of distance field computation model in Scan orientation method provided by the embodiment of the present invention three is shown It is intended to；
Fig. 3 c is the signal that computation model is trained of adjusting the distance in Scan orientation method provided by the embodiment of the present invention Figure；
Fig. 4 a is the flow chart of Scan orientation method provided by the embodiment of the present invention four；
Fig. 4 b is that using face parted pattern is scanned positioning in Scan orientation method provided by the embodiment of the present invention Schematic diagram；
Fig. 5 a is the flow chart of Scan orientation method provided by the embodiment of the present invention five；
Fig. 5 b is the schematic diagram that opposite parted pattern is trained in Scan orientation method provided by the embodiment of the present invention；
Fig. 6 is the structural schematic diagram of Scan orientation device provided by the embodiment of the present invention six；
Fig. 7 is the structural schematic diagram of computer equipment provided by the embodiment of the present invention seven.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched The specific embodiment stated is used only for explaining the present invention rather than limiting the invention.It also should be noted that in order to just Only the parts related to the present invention are shown in description, attached drawing rather than entire infrastructure.
Embodiment one
Fig. 1 is the flow chart of Scan orientation method provided by the embodiment of the present invention one, and the present embodiment is applicable to pass through Various scanning mode carry out situation when positioning scanning to each medical imaging position, are particularly suitable for using cardiac magnetic resonance imaging System is scanned situation when positioning to heart.This method can be executed by Scan orientation device, which can It is realized in a manner of using software and/or hardware, for example, the Scan orientation device is configured in computer equipment.Such as Fig. 1 institute Show, this method specifically includes:
S110, the 3 d image data for obtaining position to be positioned.
In the present embodiment, the positioning of plane is scanned by the 3 d image data at position to be positioned.Optionally, to The 3 d image data at positioning position is to treat positioning position progress prescan to be formed by 3D investigation data.Illustratively, when When position to be positioned is heart area, 3 d image data is that the 3D of heart area investigates data.
Optionally, the acquisition modes of the 3 d image data at position to be positioned include: to obtain same direction in scanning area Multi-layer image data, multi-layer image data are subjected to the 3 d image data that image reconstruction forms scanning area, using default Image zooming-out algorithm the 3 d image data at position to be positioned is extracted from the 3 d image data of scanning area.It is optional , the 3 d image data of scanning area can be split using automatic threshold algorithm, by the 3-D image of scanning area Data are divided into foreground data and background data, and the 3-D image number at position to be positioned is then extracted using morphological method According to.
Illustratively, Multi Slice Mode can be carried out along the z-axis direction, obtained the multi-layer image data on z-axis direction, used figure As algorithm for reconstructing on z-axis direction multi-layer image data carry out image reconstruction after obtain scanning area 3 d image data, so The 3 d image data at position to be positioned is extracted from the 3 d image data of scanning area afterwards.When position to be positioned is heart When region, the segmentation of foreground and background can be carried out to the 3 d image data of scanning area using automatic threshold segmentation algorithm, Then the 3 d image data of heart area is extracted using morphological method.
S120,3 d image data is input in preparatory trained location point characteristic model, obtains position point feature The each position point feature data of model output.
In the present embodiment, after the 3 d image data for obtaining position to be positioned, by the 3-D image number at position to be positioned According to being input in preparatory trained location point characteristic model, the spy of each position point in position 3 d image data to be positioned is obtained Levy data.
Optionally, the characteristic of each position point includes between each position point coordinate and each position point and plane to be positioning Distance parameter.Wherein, the distance parameter of each position point and plane to be positioning can be between each position point and plane to be positioning Distance value, or the distance between each position point and plane to be positioning correlation.
Optionally, before 3 d image data to be input to preparatory trained location point characteristic model, further includes:
It is normalized by the 3 d image data that pre-set image data normalization algorithm treats positioning position, Obtain image normalization data.
In the present embodiment, before the 3 d image data input position point feature model by position to be positioned, to three Dimensional data image is normalized.It is to advise 3 d image data by setting that 3 d image data, which is normalized, Scaling is then carried out, falls in it in small specific sections, facilitates the data handling procedure in follow-up data treatment process.Its In, the algorithm that 3 d image data is normalized is herein with no restrictions.For example, normalization algorithm can be min-max Standardized algorithm, zero-mean value standardized algorithm or decimal calibrate standardized algorithm.Wherein, min-max standardized algorithm is logical Data are normalized in the maxima and minima for crossing data, and zero-mean value standardized algorithm is being averaged by data Data are normalized in value and standard deviation, and decimal calibration standardized algorithm is the scaling position pair by mobile data Data are normalized.
Optionally, image data normalization algorithm is min-max standardized algorithm, it can is advised by min-max The 3 d image data that generalized algorithm treats positioning position is normalized.If the pixel value of each position point in position to be positioned Maximum value is Imax, minimum value Imin, location point pixel value in position to be positioned is Ic, it is after location point pixel value normalization I, then each position point pixel value in position to be positioned normalizes calculation formula are as follows: I=(Ic-Imin)/(Imax-Imin)。
Optionally, image data normalization algorithm is zero-mean value standardized algorithm, it can is standardized by zero-mean value The 3 d image data that algorithm treats positioning position is normalized.If the pixel value mean value of each position point in position to be positioned ForStandard deviation is σ, and location point pixel value in position to be positioned is Ic, it is I after location point pixel value normalization, then it is to be positioned Position each position point pixel value normalizes calculation formula are as follows:
S130, plane fitting point is determined according to each position point feature data, and determined according to plane fitting point to be positioned Plane equation.
In the present embodiment, portion point can be filtered out from each position point feature data as plane fitting point, Plane equation to be positioned is determined according to the plane fitting point filtered out；Each position point can not also be screened, direct root According to whole location points or portion point is randomly selected as plane fitting point, and plane to be positioned is determined according to plane fitting point Equation.
Optionally, according to plane fitting point determine plane equation to be positioned include: according to each position point feature parameter from Plane fitting point is filtered out in each position point, and each plane fitting point coordinate value is fitted using preset fitting algorithm, is obtained To plane equation to be positioned.
Optionally, it is put down according to pre-determined distance parameter area and position 3 d image data each position point to be positioned with to be positioned The distance between face parameter screens each position point, will meet corresponding to the distance parameter of pre-determined distance parameter area condition Location point as plane fitting point, fit plane equation to be positioned further according to the coordinate value of each plane fitting point.Example Property, the coordinate value of each plane fitting point can be fitted using least square fitting algorithm, calculate to be positioned put down Face equation.
Optionally, determine that plane equation to be positioned includes: by optimization algorithm according to each flat according to plane fitting point The coordinate value of face match point determines plane equation to be positioned.Optionally, plane side to be positioned is determined by optimization algorithm Cheng Shi, plane fitting point can be all location points in position 3 d image data to be positioned, or 3-D image number Portion point in.
Illustratively, the parameter of plane to be positioning equation can be calculated using optimization algorithm.In the present embodiment, right Optimization algorithm is with no restrictions.For example, optimization algorithm can be gradient descent method, Newton method, conjugate direction method or conjugation ladder Degree method scheduling algorithm.
Illustratively, pass throughDetermine plane to be positioning side The parameter of journey.Wherein, n is the number of all plane fitting points in position to be positioned, xiFor i-th of plane fitting of plane to be positioning The x-axis coordinate value of point, yiFor the y-axis coordinate value of i-th of plane fitting point of plane to be positioning, ziFor i-th of plane of plane to be positioning The z-axis coordinate value of match point, DiFor when plane to be positioning equation is ax+by+cz+d=0, i-th of plane in position to be positioned is quasi- The distance between chalaza and plane to be positioning value, will meet all plane fitting points and ax+by+cz+d=0 in position to be positioned It is difference between the distance between plane value and the distance value obtained by distance field computation model and minimum, and meet a2+b2 +c2Parameter of=1 a, b, c, d value as plane to be positioning.
Optionally, determine that plane equation to be positioned includes: by ballot mode according to each plane according to plane fitting point The coordinate value of match point determines plane equation to be positioned.Optionally, when determining plane equation to be positioned by ballot mode, Plane fitting point can be all location points in position 3 d image data to be positioned, or in 3 d image data Portion point.All or part of plane fitting point can be selected to vote, the parameter combination of maximum probability is determined as The parameter of plane to be positioning equation.
The 3 d image data that the embodiment of the present invention passes through acquisition position to be positioned；The 3 d image data is input to In preparatory trained location point characteristic model, each position point feature data of the location point characteristic model output are obtained；Root Plane fitting point is determined according to each position point feature data, and plane equation to be positioned is determined according to plane fitting point, the present invention Determining for the plane of scanning motion can be realized in the 3 d image data that Scan orientation method only passes through position to be positioned provided by embodiment Position, is applicable to be scanned different scanning portions by different scanning mode situation when positioning, so that the plane of scanning motion Positioning more quickly, it is accurate, and then the time required to reducing scanning process, improve the accuracy of scan image, and gram The inaccuracy positioned according to anatomic landmarks point has been taken, it is not full-time in prescan range, it remains to preferably realize scanning The positioning of plane.
Embodiment two
Fig. 2 a is the flow chart of Scan orientation method provided by the embodiment of the present invention two, and the present embodiment is in above-described embodiment On the basis of further optimized.As shown in Figure 2 a, which comprises
S210, the 3 d image data for obtaining position to be positioned.
S220,3 d image data is input in preparatory trained distance field computation model, is obtained apart from field computation The distance matrix of model output.
In the present embodiment, preparatory trained location point characteristic model is embodied as distance field computation model, it will be each Location point characteristic is embodied as the distance between each position point and plane to be positioning value, is calculated by distance field computation model The distance between each position point and plane to be positioning are worth in position 3 d image data to be positioned, each position point and plane to be positioning The distance between value composition distance matrix.
It should be noted that if before the 3 d image data transmission range field computation model by position to be positioned, it is right Data have carried out normalized, then the distance value for including in the distance matrix of distance field computation model output can be normalization Distance value after processing, or the actual distance value between each position point and plane to be positioning.Preferably, it will normalize The distance value in distance matrix that distance value after processing is exported as distance field computation model, makes the positioning of plane to be positioning It is more accurate.
S230, each position point is screened according to pre-determined distance range and each distance value, the position of fitting condition will be met A conduct plane fitting point is set, and plane equation to be positioned is determined according to plane fitting point.
Optionally, using location point corresponding to the distance value within the scope of pre-determined distance as plane fitting point, using pre- If fitting algorithm each plane fitting is fitted, plane equation to be positioned is calculated.Optionally, pre-determined distance range It can be determined according to the concrete condition at position to be positioned.Illustratively, when position to be positioned is heart, pre-determined distance range can Think (0,0.1).Optionally, preset fitting algorithm can be least square fitting algorithm.
In another embodiment of the invention, each position point can not also be screened, directly according to whole positions Portion point is set a little or randomly selected as plane fitting point, according to plane fitting point by way of optimization algorithm or ballot Determine plane equation to be positioned.
Optionally, by way of preset fitting algorithm, optimization algorithm or ballot according to each plane fitting point determine to The more detailed content of the plane equation of positioning can be found in above-described embodiment, and details are not described herein.
Fig. 2 b is to be scanned positioning using apart from computation model in Scan orientation method provided by the embodiment of the present invention Schematic diagram.The positioning that plane is scanned by preparatory trained distance field computation model is schematically illustrated in figure Process.As shown in Figure 2 b, the 3 d image data at the position to be positioned extracted from prescan figure is input to and is trained in advance Distance field computation model in, obtain distance field computation model output distance field, plane to be positioning is determined according to distance field Plane parameter.
It should be noted that being carried out using Scan orientation method provided by the embodiment of the present invention to 57 subjects The test of cardiac magnetic resonance imaging, test result are as shown in table 1.It is shown in table 1 in the different planes of scanning motion, uses this hair The normal vector mean error of Scan orientation plane determined by Scan orientation method provided by bright embodiment and putting down for distance field Equal error.Normal vector mean error corresponding to each Scan orientation plane and distance field average error value can be obtained from table 1. And the heart is carried out using each heart scanning plane of the Scan orientation method provided by the embodiment of the present invention to different subjects Dirty magnetic resonance imaging, imaging results pass through the assessment of doctor, and only an example needs doctor to finely tune, other are clinical acceptable.
Table 1
Brchypinacoid Two Cavity surfaces Three Cavity surfaces Four Cavity surfaces
Normal vector mean error (°) 5.7 5.4 7.2 5.4
Distance field mean error (mm) 5.4 3.7 4.7 6.1
Wherein, normal vector angular error calculation are as follows:
Wherein,The normal vector in face is marked for craft,Localization method institute is determined for scanning provided by the embodiment of the present invention The normal vector of the determining plane of scanning motion.In the present embodiment, each test data is corresponding with a brchypinacoid, two chambers Face, three Cavity surfaces and four Cavity surfaces, the normal vector mean error of each plane of scanning motion refers to all test data institutes in table 1 The average value of the angular error of the normal vector of the corresponding Scan orientation plane.
Distance field error calculation mode are as follows:Wherein, D0For cardia each position The distance between point and manual mark face, D1For Scan orientation side provided by cardia each position point and the embodiment of the present invention Actual range between the plane of scanning motion determined by method.Optionally, the distance matrix that can be exported according to distance field computation model Determine the actual range between each position point and the plane of scanning motion.
Optionally, if the distance value in the distance matrix of distance field computation model output is the distance after not normalizing Value, then the distance value in distance matrix directly exported distance field computation model is as corresponding each position point and the plane of scanning motion Between actual range.If the distance value in the distance matrix of distance field computation model output is the distance value after normalization, The distance between each position point and the plane of scanning motion of the field computation model that can then adjust the distance output carry out retrospectively calculate, obtain heart Actual range between position each position point and the plane of scanning motion.Specifically, each distance value that distance field computation model is exported with Product of the matrix of adjusting the distance when being normalized between used preset threshold is as between each position point and the plane of scanning motion Actual distance.In table 1 distance field mean error refer to each position point corresponding to each distance value within preset threshold away from Average value from value error.
The technical solution of the embodiment of the present invention is input to by the 3 d image data on the basis of the above embodiments In preparatory trained location point characteristic model, each position point feature data of the location point characteristic model output, root are obtained It determines that plane fitting point is embodied according to each position point feature data, passes through preparatory trained distance field computation model meter The distance between position each position point and plane to be positioning to be positioned value is calculated, and plane fitting point is determined according to each distance value, is made The determination for obtaining plane fitting point is more accurate, and then keeps the positioning of plane to be positioning more accurate.
Embodiment three
Fig. 3 a is the flow chart of Scan orientation method provided by the embodiment of the present invention three, and the present embodiment is in above-described embodiment On the basis of further optimized.As shown in Figure 3a, which comprises
S310, the plane ginseng for obtaining history 3 d image data and the corresponding plane to be positioning of history 3 d image data Number.
In the present embodiment, based on history 3 d image data and the corresponding plane to be positioning of history 3 d image data Plane parameter is trained the distance field computation model pre-established.Wherein, history 3 d image data and history are three-dimensional The plane parameter of the corresponding plane to be positioning of image data is for trained data.Optionally, history 3 d image data The 3 d image data that can be extracted by the history scan data at position to be positioned, history 3 d image data it is corresponding to The plane parameter for positioning plane can be the normal vector of plane to be positioning or the plane equation of plane to be positioning.
Optionally, the acquisition modes of the plane parameter of the corresponding plane to be positioning of history 3 d image data include:
By image processing software, each plane to be positioning is positioned using position line from history 3 d image data, is obtained The plane parameter of each plane to be positioning.Optionally, position line manually can be used to plane to be positioning by image processing software Positioning, obtains the plane parameter of corresponding plane to be positioning.
Optionally, the acquisition modes of the plane parameter of the corresponding plane to be positioning of history 3 d image data include:
According to the figure of each sub-portion bit image in position to be positioned in scan image information corresponding to history 3 d image data As information calculates the plane parameter of plane to be positioning.
Optionally, scan image information is to treat the high resolution scanning image obtained when positioning position is formally scanned Information.Optionally, obtained from true high resolution scanning image information when scanning history 3 d image data it is corresponding to Position the plane parameter of plane.In general, treating the more general prescan of scan data formal when positioning position is scanned The information content of data is more abundant, and includes doctor in formal scan data in the data formally scanned to scanning area The markup information (for example, the localization process such as the various sections at position to be positioned, chamber) of progress, by formal scan data and doctor Markup information as " goldstandard " during training pattern, the distance field computation model pre-established is trained.
Optionally, pre-scan images are the image of DICOM format, include the figure of current layer in every width DICOM image Picture information, for example, the top left co-ordinate position of current layer and current layer x-axis unit vector vx and y-axis unit vector vy, it will be with vx Unit normal vector of the unit vector vertical with vy as plane to be positioning, by determining unit normal vector (a, b, c) as to Position the plane parameter of plane.In addition, in conjunction with current layer top left co-ordinate information (x0,y0,z0), determine parameter d=-ax0-by0- cz0, the plane parameter for finally obtaining plane to be positioning is (a, b, c, d), and the plane equation of plane to be positioning is ax+by+cz+d =0.
Preferably, by way of being positioned manually, plane to be positioning is positioned using position line using image processing software, Obtain the plane parameter of corresponding plane to be positioning.The acquisition of the plane parameter of plane to be positioning is carried out using image processing software It is more convenient, accurate.
S320, calculated according to history 3 d image data and plane parameter the corresponding history of history 3 d image data away from From matrix.
Optionally, by it is preset apart from computational algorithm calculate in history 3 d image data each position point with it is to be positioned History distance value between plane, the history distance value between each position point and plane to be positioning form history 3 d image data Corresponding history distance matrix.It optionally, can be Euclidean distance algorithm apart from computational algorithm.Illustratively, if it is to be positioned flat Face equation is ax+by+cz+d=0, then point (x in any position in history 3 d image datai,yi,zi) and plane to be positioning between Distance are as follows:
S330, training sample set is generated based on history 3 d image data and history distance matrix, uses training sample set The distance field computation model pre-established is trained, trained distance field computation model is obtained.
In the present embodiment, history corresponding to usage history 3 d image data and history 3 d image data is apart from square Battle array is trained as sample to the distance field computation model pre-established.Optionally, the distance field meter pre-established Calculation model is convolutional neural networks model.
Fig. 3 b is that the training process of distance field computation model in Scan orientation method provided by the embodiment of the present invention three is shown It is intended to.Illustratively, convolutional neural networks used by the embodiment of the present invention are as shown in Figure 3b.Wherein, solid line (short) straight arrow Channel before indicating arrow obtains the channel after arrow, dotted line through the operation of convolutional layer, block homogenization layer and relu active coating Curved arrow and plus sige represent superposition and relu active coating, and solid line (length) curved arrow represents concat layers, i.e., by the channel of front and Subsequent channel is side by side.
Illustratively, the calculating process of 16 channel A is obtained by 1 channel A are as follows: 1 channel A is uniformed into layer through convolutional layer, block And relu active coating operation obtains 16 channel A.The calculating process of 32 channel A is obtained by 16 channel A and 16 channel B are as follows: is led to 16 Road A be superimposed with 16 channel B after through relu active coating operation, the operation result obtained through relu active coating is again through convolutional layer, block Homogenization layer and relu active coating operation obtain 32 channel A.The calculating process of 32 channel Cs is obtained by 16 channel B and 64 channel Cs Are as follows: 64 channel Cs are obtained into 16 channel D after convolutional layer, block homogenization layer and relu active coating operation, by 16 channel B and are drained into After 16 channel D, 32 channel Cs are obtained.The calculating process of 64 channel Cs is obtained by 64 channel A, 64 channel B and 32 channel B are as follows: will 64 channel A be superimposed with 64 channel B after through relu active coating operation, the operation result obtained through relu active coating is again through convolution Layer, block homogenization layer and relu active coating operation obtain 32 channel E, and by 32 channel B and after draining into 32 channel E, it is logical to obtain 64 Road C.The calculating process of 1 channel B is obtained by 32 channel Cs and 32 channel D operations are as follows: is passed through after being superimposed 32 channel Cs with 32 channel D Relu active coating operation, the operation result obtained through relu active coating are transported through convolutional layer, block homogenization layer and relu active coating again Calculation obtains 1 channel B.
Optionally, the method for field computation of adjusting the distance model training is herein with no restrictions.Illustratively, field computation of adjusting the distance mould The training method of type can be back-propagation algorithm, stochastic gradient descent method or randomized optimization process.It in the present embodiment, can be with Training method using randomized optimization process (adam method) as distance field computation model.
Optionally, distance field computation model is exported into IoutWith goldstandard IlabelBetween 1 norm be used as apart from field computation mould Cost function in type training process.Wherein, goldstandard IlabelFor history distance matrix corresponding to history 3 d image data, When history 3 d image data being input in distance field computation model, the gauged distance matrix that should export.Illustratively, generation Valence function loss=| Iout-Ilabel|.Optionally, the cost function in distance field computation model training process can also be distance Field computation model exports IoutWith goldstandard IlabelBetween 2 norms, weighting 1 norm or weighting 2 norms, herein with no restrictions.
Optionally, in order to enable trained distance field computation model is suitable for a plurality of types of 3 d image datas.It can To be expanded to training data, history 3 d image data and corresponding history distance matrix are deformed, after deformation Data be also used as the training data of distance field computation model.It illustratively, can be to history 3 d image data and corresponding History distance matrix translated (such as along tri- directions x, y or z random translation, range of translation are ± 50mm), rotation (such as around Random-Rotation axis rotates random angles, and angular range is ± 20 °), scaling (such as random scaling 0.7-1.3 times) handle, will locate As training sample, field computation of adjusting the distance model is trained data after reason.As training after being deformed to training data Data can expand limited training data, increase the training sample of distance field computation model, make the distance field trained Computation model still is able to export accurate distance matrix in 3 d image data inaccuracy, the generation offset of input.
Optionally, the history distance matrix corresponding to usage history 3 d image data and history 3 d image data is raw Before training sample set, further includes:
Take slip gauge then to history distance corresponding to history 3 d image data and history 3 d image data according to default Matrix carries out taking block, using taking history distance matrix corresponding to the history three-dimensional data after block and history 3 d image data raw At training sample set.
Optionally, the image block of same size, the object of image block are extracted using sliding window or by the way of randomly selecting Reason may range from 50mm*50mm*50mm-120mm*120mm*120mm, and the spatial resolution of image block may range from 2mm* 2mm*2mm-5mm*5mm*5mm.Illustratively, the size of image block is 100mm*100mm*100mm, spatial resolution 3mm* 3mm*3mm.Using taking history distance matrix corresponding to the history three-dimensional data after block and history 3 d image data to generate instruction Practice sample set, the size of each training sample pair is reduced, so that the training process speed based on training sample set is faster.
Optionally, before usage history 3 d image data and history distance matrix generate training sample, further includes:
History 3 d image data and history distance matrix data are normalized respectively, history of forming image is returned One changes data and history range normalization matrix.
Optionally, above-described embodiment can be found in the more detailed content that history 3 d image data is normalized, Details are not described herein.
Optionally, normalization algorithm identical with history 3 d image data can be used to return history distance matrix One change processing, also can be used the normalization algorithm different from history 3 d image data and history distance matrix is normalized Processing.In the present embodiment, the data distribution rule of history 3 d image data and history distance matrix is different, using being different from History distance matrix is normalized in the normalization algorithm of history 3 d image data.
Illustratively, distance threshold T can be preset1, according to distance matrix to history distance matrix carry out truncation and Normalization.Specifically, if any distance value is D in history distance matrixi, then by Di>T1Distance value be set as T1, then will All distance values are divided by T in history distance matrix1, obtain history range normalization matrix.It can be seen that history is apart from normalizing Change the history range normalization value in matrix between 0-1.Optionally, distance threshold T1Value range is (30mm, 200mm). History range normalization matrix size after normalization is identical as history 3 d image data size, history range normalization matrix In it is each value be corresponding position point to plane to be positioning normalized distance.
Fig. 3 c is the signal that computation model is trained of adjusting the distance in Scan orientation method provided by the embodiment of the present invention Figure.The process that computation model of adjusting the distance is trained is schematically illustrated in figure.As shown in Figure 3c, according to plane to be positioning Plane parameter calculates distance field corresponding to 3 d image data, by distance field and the position to be positioned extracted from prescan figure 3 d image data computation model of adjusting the distance is trained as training sample, obtain it is trained apart from computation model.
S340, the 3 d image data for obtaining position to be positioned.
S350,3 d image data is input in preparatory trained distance field computation model, is obtained apart from field computation The distance matrix of model output.
S360, each position point is screened according to pre-determined distance range and each distance value, the position of fitting condition will be met It sets a conduct and determines plane equation to be positioned apart from plane fitting point, and according to apart from plane fitting point.
It should be noted that the training method of distance field computation model provided by the embodiment of the present invention can be individually performed. That is, the operating procedure that can be used alone in S310-S330 provided by the embodiment of the present invention completes field meter of adjusting the distance The training of model is calculated, the 3 d image data based on position to be positioned passes through distance in no longer execution subsequent step S340-S360 Computation model determines the operation of plane equation to be positioned.
The technical solution of the embodiment of the present invention increases field computation model progress of adjusting the distance on the basis of the above embodiments Trained operation, the plane by obtaining history 3 d image data and the corresponding plane to be positioning of history 3 d image data are joined Number；The corresponding history distance matrix of history 3 d image data is calculated according to history 3 d image data and plane parameter；Base In history 3 d image data and history distance matrix generate training sample set, using training sample set to pre-establish away from Computation model of leaving the theatre is trained, and obtains trained distance field computation model, so that training is obtained apart from computation model It is more accurate.
Example IV
Fig. 4 a is the flow chart of Scan orientation method provided by the embodiment of the present invention four, and the present embodiment is in above-described embodiment On the basis of further optimized.As shown in fig. 4 a, which comprises
S410, the 3 d image data for obtaining position to be positioned.
S420,3 d image data is input in preparatory trained face parted pattern, obtains the output of face parted pattern Apart from subdivision matrix.
In the present embodiment, preparatory trained location point characteristic model is embodied as face parted pattern, by each position Point feature data materialization is the distance between each position point and plane to be positioning partition value, is calculated by face parted pattern undetermined The distance between each position point and plane to be positioning partition value, each position point and plane to be positioning in the 3 d image data of position position The distance between partition value composition apart from subdivision matrix.
Optionally, face parted pattern is disaggregated model, and output includes prospect output channel and background output channel, is obtained Prospect output channel output data, as corresponding to position 3 d image data to be positioned apart from subdivision matrix.
S430, basis are preset the first segmentation threshold and are respectively screened apart from partition value to each position point, divide meeting The location point of condition is as plane fitting point.
Optionally, obtain face parted pattern output after subdivision matrix, according to default first segmentation threshold and distance Each position point is treated each in positioning position 3 d image data with the distance between plane to be positioning partition value in subdivision matrix Location point is screened, and will be greater than the location point corresponding to the partition value of default first segmentation threshold as plane fitting Point.Optionally, presetting the first segmentation threshold can be adjusted according to specific position to be positioned.Schematically, when to be positioned When position is heart area, presetting the first segmentation threshold can be 0.5.
S440, each plane fitting point is fitted by preset fitting algorithm, obtains plane equation to be positioned.
In the present embodiment, each plane fitting point is fitted using fitting algorithm, obtains plane equation to be positioned. Optionally, be fitted according to each plane fitting point to form plane to be positioning equation with it is true according to plane fitting point in above-described embodiment The mode of fixed plane equation to be positioned is similar, and more detailed content can be found in above-described embodiment, and details are not described herein.
Fig. 4 b is that using face parted pattern is scanned positioning in Scan orientation method provided by the embodiment of the present invention Schematic diagram.The process that the positioning of plane is scanned by preparatory trained face parted pattern is schematically illustrated in figure. As shown in Figure 4 b, the 3 d image data at the position to be positioned extracted from prescan figure is input to preparatory trained face point Cut in model, obtain the output of face parted pattern apart from subdivision matrix, determine the flat of plane to be positioning according to apart from subdivision matrix Face parameter.
The technical solution of the embodiment of the present invention is input to by the 3 d image data on the basis of the above embodiments In preparatory trained location point characteristic model, each position point feature data of the location point characteristic model output, root are obtained Determine that plane fitting point is embodied according to each position point feature data, by trained face parted pattern in advance calculate to The distance between position each position point and plane to be positioning partition value are positioned, and will be met by default first segmentation threshold and be preset The location point corresponding to the partition value of segmentation condition is as segmentation plane match point, so that the determination of segmentation plane match point It is more accurate, and then keep the positioning of plane to be positioning more accurate, and filter out portion point conduct from each position point Segmentation plane match point makes the fitting speed of plane to be positioning faster, the time required to reducing scanning process.
Embodiment five
Fig. 5 a is the flow chart of Scan orientation method provided by the embodiment of the present invention five, and the present embodiment is in above-described embodiment On the basis of further optimized.As shown in Figure 5 a, which comprises
S510, the plane ginseng for obtaining history 3 d image data and the corresponding plane to be positioning of history 3 d image data Number.
S520, calculated according to history 3 d image data and plane parameter the corresponding history of history 3 d image data away from From matrix.
In the present embodiment, history 3 d image data and the corresponding plane to be positioning of history 3 d image data are obtained The mode of plane parameter, and the mode of calculating history distance matrix are similar with above-described embodiment, and specific detailed content can be found in Above-described embodiment, details are not described herein.
S530, history distance matrix is split according to preset second segmentation threshold, obtains history distance segmentation square Battle array.
In the present embodiment, after being split according to preset second segmentation threshold to history distance matrix, segmentation is used Obtained history carries out the training of face parted pattern apart from subdivision matrix.Optionally, history is apart from subdivision matrix by history three-dimensional The segmentation distance value of each position point and plane to be positioning forms in image data.
Optionally, history distance matrix is split according to preset second segmentation threshold, obtains history distance segmentation Matrix, comprising:
History distance value each in history distance matrix is adjusted according to preset second segmentation threshold, it will be adjusted Matrix composed by history distance value is as history apart from subdivision matrix.
Illustratively, if the second segmentation threshold is T, according to the second segmentation threshold T to each history in history distance matrix Distance value is adjusted.Optionally, if any history distance value is D in history distance matrixi, then by DiThe history distance value of < T It is adjusted to 1, other history distance values are adjusted to 0.Optionally, the value of the second segmentation threshold can according to position to be positioned into Row adjustment.Illustratively, when position to be positioned is heart area, the second segmentation threshold can be 5mm.
S540, training sample set is generated based on history 3 d image data and history segmentation distance matrix, uses training sample This collection is trained the face parted pattern pre-established, obtains trained face parted pattern.
Optionally, the cost function in face parted pattern training process can be focal loss function focalloss, dice Deng the cost function for classification.In the present embodiment, it adjusts the distance in the training method of opposite parted pattern and above-described embodiment The training method of computation model is similar, and more detailed content can be found in above-described embodiment, and details are not described herein.
Fig. 5 b is the schematic diagram that opposite parted pattern is trained in Scan orientation method provided by the embodiment of the present invention. The process that opposite parted pattern is trained is schematically illustrated in figure.As shown in Figure 5 b, according to the plane of plane to be positioning Parameter calculates corresponding to 3 d image data apart from subdivision matrix, will be extracted apart from subdivision matrix and from prescan figure to The 3 d image data at positioning position is trained opposite parted pattern as training sample, obtains trained face segmentation mould Type.
S550, the 3 d image data for obtaining position to be positioned.
S560,3 d image data is input in preparatory trained face parted pattern, obtains the output of face parted pattern Apart from subdivision matrix.
S570, basis are preset the first segmentation threshold and are respectively screened apart from partition value to each position point, divide meeting The location point of condition is as plane fitting point.
S580, each plane fitting point is fitted by preset fitting algorithm, obtains plane equation to be positioned.
It should be noted that the training method of face parted pattern provided by the embodiment of the present invention can be individually performed.Also It is to say, the operating procedure that can be used alone in S510-S540 provided by the embodiment of the present invention completes opposite parted pattern It is true by face parted pattern no longer to execute the 3 d image data based on position to be positioned in subsequent step S550-S580 for training The operation of fixed plane equation to be positioned.
The technical solution of the embodiment of the present invention increases opposite parted pattern on the basis of the above embodiments and is trained Operation, pass through obtain history 3 d image data and the corresponding plane to be positioning of history 3 d image data plane parameter； The corresponding history distance matrix of history 3 d image data is calculated according to history 3 d image data and plane parameter；According to pre- If the second segmentation threshold history distance matrix is split, obtain history apart from subdivision matrix；Based on history 3-D image Data and history segmentation distance matrix generate training sample set, using training sample set to the face parted pattern pre-established into Row training, obtains trained face parted pattern, so that the obtained face parted pattern of training is more accurate.
It, can also be directly according to history 3 d image data and history three-dimensional figure in another embodiment of the invention The plane parameter as corresponding to data determines that model is trained to the plane pre-established；When needs are scanned positioning When, it is directly that the coordinates matrix of position 3 d image data to be positioned and the x, y, z axis of position 3 d image data to be positioned is defeated Enter to trained plane and determine in model, obtains the plane parameter that plane determines the plane to be positioning of model output.Optionally, Above-described embodiment can be found in the training method and data processing method of areal model, details are not described herein.
Embodiment six
Fig. 6 is the structural schematic diagram of Scan orientation device provided by the embodiment of the present invention six.The Scan orientation device can It is realized in a manner of using software and/or hardware, such as the Scan orientation device can be configured in computer equipment, such as Fig. 6 Shown, described device includes: data acquisition module 610, characteristic module 620 and plane determining module 630, in which:
Data acquisition module 610, for obtaining the 3 d image data at position to be positioned；
Characteristic module 620, for the 3 d image data to be input to preparatory trained location point character modules In type, each position point feature data of the location point characteristic model output are obtained；
Plane determining module 630, for determining plane fitting point according to each position point feature data, and according to plane fitting Point determines plane equation to be positioned.
The embodiment of the present invention obtains the 3 d image data at position to be positioned by data acquisition module；Characteristic module The 3 d image data is input in preparatory trained location point characteristic model, it is defeated to obtain the location point characteristic model Each position point feature data out；Plane determining module determines plane fitting point according to each position point feature data, and according to flat Face match point determines plane equation to be positioned, and Scan orientation method provided by the embodiment of the present invention only passes through position to be positioned 3 d image data the positioning of the plane of scanning motion can be realized, be applicable to through different scanning mode to different scanning portions It is scanned situation when positioning, so that the positioning of the plane of scanning motion is more quickly, accurately, and then scanning process is reduced and is taken Between, the accuracy of scan image is improved, and overcome the inaccuracy positioned according to anatomic landmarks point, swept in advance It retouches that range is not full-time, remains to the positioning for preferably realizing the plane of scanning motion.
On the basis of above scheme, the characteristic module 620 is specifically used for:
The 3 d image data is input in preparatory trained distance field computation model, the distance field meter is obtained The distance matrix of model output is calculated, the distance matrix is made of the distance value of each location point and the plane to be positioning.
On the basis of above scheme, the plane determining module 630 is specifically used for:
Each location point is screened according to pre-determined distance range and each distance value, fitting condition will be met Location point determines plane equation to be positioned as plane fitting point, and according to the plane fitting point.
On the basis of above scheme, described device further include:
Historical data acquiring unit, for it is three-dimensional to obtain history before the 3 d image data for obtaining position to be positioned The plane parameter of image data and the corresponding plane to be positioning of the history 3 d image data；
Distance matrix determination unit, it is described for being calculated according to the history 3 d image data and the plane parameter The corresponding history distance matrix of history 3 d image data, the distance matrix by each position point in history 3 d image data with The distance value of the plane to be positioning forms；
Distance field model training unit, for being generated based on the history 3 d image data and the history distance matrix Training sample set is trained the distance field computation model pre-established using the training sample set, is trained Distance field computation model.
On the basis of above scheme, the characteristic module 620 is specifically used for:
The 3 d image data is input in preparatory trained face parted pattern, it is defeated to obtain the face parted pattern Out apart from subdivision matrix, it is described to be made of apart from subdivision matrix each location point partition value at a distance from plane to be positioning.
On the basis of above scheme, the plane determining module 630 includes:
Match point determination unit is preset the first segmentation threshold for basis and is respectively sieved apart from partition value to each position point Choosing, will meet the location point of segmentation condition as plane fitting point；
Plane fitting unit obtains to be positioned put down for being fitted each plane fitting point by preset fitting algorithm Face equation.
On the basis of above scheme, described device further include:
Historical data acquiring unit, for it is three-dimensional to obtain history before the 3 d image data for obtaining position to be positioned The plane parameter of image data and the corresponding plane to be positioning of the history 3 d image data；
Distance matrix determination unit, it is described for being calculated according to the history 3 d image data and the plane parameter The corresponding history distance matrix of history 3 d image data, the history distance matrix is by each in the history 3 d image data The distance value of location point and the plane to be positioning forms；
Subdivision matrix determination unit, for being divided according to preset second segmentation threshold the history distance matrix It cuts, obtains history apart from subdivision matrix, the history is apart from subdivision matrix by each position point in the history 3 d image data It is formed with the segmentation distance value of the plane to be positioning；
Face parted pattern training unit, for dividing distance matrix based on the history 3 d image data and the history Training sample set is generated, the face parted pattern pre-established is trained using the training sample set, is trained Face parted pattern.
Scan orientation method provided by any embodiment can be performed in Scan orientation device provided by the embodiment of the present invention, Have the corresponding functional module of execution method and beneficial effect.
Embodiment seven
Fig. 7 is the structural schematic diagram of computer equipment provided by the embodiment of the present invention seven.Fig. 7, which is shown, to be suitable for being used in fact The block diagram of the exemplary computer device 712 of existing embodiment of the present invention.The computer equipment 712 that Fig. 7 is shown is only one Example, should not function to the embodiment of the present invention and use scope bring any restrictions.
As shown in fig. 7, computer equipment 712 is showed in the form of universal computing device.The component of computer equipment 712 can To include but is not limited to: one or more processor 716, system storage 728 connect different system components (including system Memory 728 and processor 716) bus 718.
Bus 718 indicates one of a few class bus structures or a variety of, including memory bus or Memory Controller, Peripheral bus, graphics acceleration port, processor 716 or total using the local of any bus structures in a variety of bus structures Line.For example, these architectures include but is not limited to industry standard architecture (ISA) bus, microchannel architecture (MAC) bus, enhanced isa bus, Video Electronics Standards Association (VESA) local bus and peripheral component interconnection (PCI) are total Line.
Computer equipment 712 typically comprises a variety of computer system readable media.These media can be it is any can The usable medium accessed by computer equipment 712, including volatile and non-volatile media, moveable and immovable Jie Matter.
System storage 728 may include the computer system readable media of form of volatile memory, such as deposit at random Access to memory (RAM) 730 and/or cache memory 732.Computer equipment 712 may further include it is other it is removable/ Immovable, volatile/non-volatile computer system storage medium.Only as an example, storage device 734 can be used for reading Write immovable, non-volatile magnetic media (Fig. 7 do not show, commonly referred to as " hard disk drive ").Although being not shown in Fig. 7, The disc driver for reading and writing to removable non-volatile magnetic disk (such as " floppy disk ") can be provided, and non-easy to moving The CD drive that the property lost CD (such as CD-ROM, DVD-ROM or other optical mediums) is read and write.In these cases, each Driver can be connected by one or more data media interfaces with bus 718.Memory 728 may include at least one Program product, the program product have one group of (for example, at least one) program module, these program modules are configured to perform this Invent the function of each embodiment.
Program/utility 740 with one group of (at least one) program module 742, can store in such as memory In 728, such program module 742 includes but is not limited to operating system, one or more application program, other program modules And program data, it may include the realization of network environment in each of these examples or certain combination.Program module 742 Usually execute the function and/or method in embodiment described in the invention.
Computer equipment 712 can also be with one or more external equipments 714 (such as keyboard, sensing equipment, display 724 etc.) it communicates, the equipment interacted with the computer equipment 712 communication can be also enabled a user to one or more, and/or (such as network interface card is adjusted with any equipment for enabling the computer equipment 712 to be communicated with one or more of the other calculating equipment Modulator-demodulator etc.) communication.This communication can be carried out by input/output (I/O) interface 722.Also, computer equipment 712 can also by network adapter 720 and one or more network (such as local area network (LAN), wide area network (WAN) and/or Public network, such as internet) communication.As shown, network adapter 720 passes through its of bus 718 and computer equipment 712 The communication of its module.It should be understood that although not shown in the drawings, other hardware and/or software can be used in conjunction with computer equipment 712 Module, including but not limited to: microcode, device driver, redundant processing unit, external disk drive array, RAID system, magnetic Tape drive and data backup storage system etc..
Processor 716 by the program that is stored in system storage 728 of operation, thereby executing various function application and Data processing, such as realize Scan orientation method provided by the embodiment of the present invention, this method comprises:
Obtain the 3 d image data at position to be positioned；
The 3 d image data is input in preparatory trained location point characteristic model, it is special to obtain the location point Levy each position point feature data of model output；
Plane fitting point is determined according to each position point feature data, and plane side to be positioned is determined according to plane fitting point Journey.
Certainly, it will be understood by those skilled in the art that processor can also realize it is provided by any embodiment of the invention The technical solution of Scan orientation method.
Embodiment eight
The embodiment of the present invention eight additionally provides a kind of computer readable storage medium, is stored thereon with computer program, should The Scan orientation method as provided by the embodiment of the present invention is realized when program is executed by processor, this method comprises:
Obtain the 3 d image data at position to be positioned；
The 3 d image data is input in preparatory trained location point characteristic model, it is special to obtain the location point Levy each position point feature data of model output；
Plane fitting point is determined according to each position point feature data, and plane side to be positioned is determined according to plane fitting point Journey.
Certainly, a kind of computer readable storage medium provided by the embodiment of the present invention, the computer program stored thereon The method operation being not limited to the described above, can also be performed the phase in Scan orientation method provided by any embodiment of the invention Close operation.
The computer storage medium of the embodiment of the present invention, can be using any of one or more computer-readable media Combination.Computer-readable medium can be computer-readable signal media or computer readable storage medium.It is computer-readable Storage medium for example may be-but not limited to-the system of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, device or Device, or any above combination.The more specific example (non exhaustive list) of computer readable storage medium includes: tool There are electrical connection, the portable computer diskette, hard disk, random access memory (RAM), read-only memory of one or more conducting wires (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD- ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.In this document, computer-readable storage Medium can be any tangible medium for including or store program, which can be commanded execution system, device or device Using or it is in connection.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal, Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can Any computer-readable medium other than storage medium is read, which can send, propagates or transmit and be used for By the use of instruction execution system, device or device or program in connection.
The program code for including on computer-readable medium can transmit with any suitable medium, including --- but it is unlimited In wireless, electric wire, optical cable, RF etc. or above-mentioned any appropriate combination.
The computer for executing operation of the present invention can be write with one or more programming languages or combinations thereof Program code, described program design language include object oriented program language-such as Java, Smalltalk, C++, It further include conventional procedural programming language-such as " C " language or similar programming language.Program code can be with It fully executes, partly execute on the user computer on the user computer, being executed as an independent software package, portion Divide and partially executes or executed on a remote computer or server completely on the remote computer on the user computer.? Be related in the situation of remote computer, remote computer can pass through the network of any kind --- including local area network (LAN) or Wide area network (WAN)-be connected to subscriber computer, or, it may be connected to outer computer (such as mentioned using Internet service It is connected for quotient by internet).
Note that the above is only a better embodiment of the present invention and the applied technical principle.It will be appreciated by those skilled in the art that The invention is not limited to the specific embodiments described herein, be able to carry out for a person skilled in the art it is various it is apparent variation, It readjusts and substitutes without departing from protection scope of the present invention.Therefore, although being carried out by above embodiments to the present invention It is described in further detail, but the present invention is not limited to the above embodiments only, without departing from the inventive concept, also It may include more other equivalent embodiments, and the scope of the invention is determined by the scope of the appended claims.

Claims (10)

1. a kind of Scan orientation method characterized by comprising
Obtain the 3 d image data at position to be positioned；
The 3 d image data is input in preparatory trained location point characteristic model, the location point character modules are obtained The each position point feature data of type output；
Plane fitting point is determined according to each position point feature data, and plane equation to be positioned is determined according to plane fitting point.
2. the method according to claim 1, wherein described be input to preparatory training for the 3 d image data In good location point characteristic model, each position point feature data of the location point characteristic model output are obtained, comprising:
The 3 d image data is input in preparatory trained distance field computation model, is obtained described apart from field computation mould The distance matrix of type output, the distance matrix are made of the distance value of each location point and the plane to be positioning.
3. according to the method described in claim 2, it is characterized in that, described determine plane fitting according to each position point feature data Point, and plane equation to be positioned is determined according to plane fitting point, comprising:
Each location point is screened according to pre-determined distance range and each distance value, the position of fitting condition will be met Point is used as plane fitting point, and determines plane equation to be positioned according to the plane fitting point.
4. according to the method described in claim 2, it is characterized in that, before the 3 d image data for obtaining position to be positioned, Further include:
Obtain the plane parameter of history 3 d image data and the corresponding plane to be positioning of the history 3 d image data；
Calculate that the history 3 d image data is corresponding to be gone through according to the history 3 d image data and the plane parameter History distance matrix, the distance matrix by each position point and the plane to be positioning in history 3 d image data distance value group At；
Training sample set is generated based on the history 3 d image data and the history distance matrix, uses the training sample Collection is trained the distance field computation model pre-established, obtains trained distance field computation model.
5. the method according to claim 1, wherein described be input to preparatory training for the 3 d image data In good location point characteristic model, each position point feature data of the location point characteristic model output are obtained, comprising:
The 3 d image data is input in preparatory trained face parted pattern, the face parted pattern output is obtained It is described to be made of apart from subdivision matrix each location point partition value at a distance from plane to be positioning apart from subdivision matrix.
6. according to the method described in claim 5, it is characterized in that, described determine plane fitting according to each position point feature data Point, and plane equation to be positioned is determined according to plane fitting point, comprising:
According to default first segmentation threshold and respectively each position point is screened apart from partition value, the position of segmentation condition will be met Point is used as plane fitting point；
Each plane fitting point is fitted by preset fitting algorithm, obtains plane equation to be positioned.
7. according to the method described in claim 5, it is characterized in that, before the 3 d image data for obtaining position to be positioned, Further include:
Obtain the plane parameter of history 3 d image data and the corresponding plane to be positioning of the history 3 d image data；
Calculate that the history 3 d image data is corresponding to be gone through according to the history 3 d image data and the plane parameter History distance matrix, the history distance matrix is by each position point in the history 3 d image data and the plane to be positioning Distance value composition；
The history distance matrix is split according to preset second segmentation threshold, obtains history apart from subdivision matrix, institute State segmentation distance of the history apart from subdivision matrix by each position point and the plane to be positioning in the history 3 d image data Value composition；
Training sample set is generated based on the history 3 d image data and history segmentation distance matrix, uses the training Sample set is trained the face parted pattern pre-established, obtains trained face parted pattern.
8. a kind of Scan orientation device characterized by comprising
Data acquisition module, for obtaining the 3 d image data at position to be positioned；
Characteristic module is obtained for the 3 d image data to be input in preparatory trained location point characteristic model Obtain each position point feature data of the location point characteristic model output；
Plane determining module for determining plane fitting point according to each position point feature data, and is determined according to plane fitting point Plane equation to be positioned.
9. a kind of computer equipment, which is characterized in that the equipment includes:
One or more processors；
Storage device, for storing one or more programs；
When one or more of programs are executed by one or more of processors, so that one or more of processors are real The now Scan orientation method as described in any in claim 1-7.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor The Scan orientation method as described in any in claim 1-7 is realized when execution.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110163857A (en) * 2019-05-24 2019-08-23 上海联影医疗科技有限公司 Detection method, device, storage medium and the x-ray system of image background regions
CN110223352A (en) * 2019-06-14 2019-09-10 浙江明峰智能医疗科技有限公司 A kind of medical image scanning automatic positioning method based on deep learning
CN111443318A (en) * 2019-01-16 2020-07-24 上海联影医疗科技有限公司 Magnetic resonance image processing method, magnetic resonance image processing device, storage medium and magnetic resonance imaging system

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101034480A (en) * 2007-04-05 2007-09-12 上海交通大学 Method for computer-assisted rebuilding heart mitral annulus
CN101315661A (en) * 2008-07-18 2008-12-03 东南大学 Fast three-dimensional face recognition method for reducing expression influence
CN101344373A (en) * 2008-08-14 2009-01-14 中国人民解放军总后勤部军需装备研究所 Standardization processing method based on three-dimensional head and face curved surface modeling
CN101836235A (en) * 2007-08-16 2010-09-15 皇家飞利浦电子股份有限公司 Imaging method for sampling a cross-section plane in a three-dimensional (3d) image data volume
JP2014127788A (en) * 2012-12-26 2014-07-07 Nippon Hoso Kyokai <Nhk> Device of correcting stereoscopic image, and program of the same
CN104166978A (en) * 2013-12-27 2014-11-26 上海联影医疗科技有限公司 Blood vessel extracting method
CN105389813A (en) * 2015-10-30 2016-03-09 上海联影医疗科技有限公司 Medical image organ recognition method and segmentation method
CN105900140A (en) * 2013-11-05 2016-08-24 皇家飞利浦有限公司 Automated segmentation of tri-plane images for real time ultrasonic imaging
CN106204514A (en) * 2015-04-30 2016-12-07 中国科学院深圳先进技术研究院 A kind of liver localization method based on three-dimensional CT image and device

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101034480A (en) * 2007-04-05 2007-09-12 上海交通大学 Method for computer-assisted rebuilding heart mitral annulus
CN101836235A (en) * 2007-08-16 2010-09-15 皇家飞利浦电子股份有限公司 Imaging method for sampling a cross-section plane in a three-dimensional (3d) image data volume
CN101315661A (en) * 2008-07-18 2008-12-03 东南大学 Fast three-dimensional face recognition method for reducing expression influence
CN101344373A (en) * 2008-08-14 2009-01-14 中国人民解放军总后勤部军需装备研究所 Standardization processing method based on three-dimensional head and face curved surface modeling
JP2014127788A (en) * 2012-12-26 2014-07-07 Nippon Hoso Kyokai <Nhk> Device of correcting stereoscopic image, and program of the same
CN105900140A (en) * 2013-11-05 2016-08-24 皇家飞利浦有限公司 Automated segmentation of tri-plane images for real time ultrasonic imaging
CN104166978A (en) * 2013-12-27 2014-11-26 上海联影医疗科技有限公司 Blood vessel extracting method
CN106204514A (en) * 2015-04-30 2016-12-07 中国科学院深圳先进技术研究院 A kind of liver localization method based on three-dimensional CT image and device
CN105389813A (en) * 2015-10-30 2016-03-09 上海联影医疗科技有限公司 Medical image organ recognition method and segmentation method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张波: "基于三维人体骨架模型的动作识别", 《中国优秀硕士学位论文全文数据库 信息科技辑》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111443318A (en) * 2019-01-16 2020-07-24 上海联影医疗科技有限公司 Magnetic resonance image processing method, magnetic resonance image processing device, storage medium and magnetic resonance imaging system
CN111443318B (en) * 2019-01-16 2022-08-02 上海联影智能医疗科技有限公司 Magnetic resonance image processing method, magnetic resonance image processing device, storage medium and magnetic resonance imaging system
CN110163857A (en) * 2019-05-24 2019-08-23 上海联影医疗科技有限公司 Detection method, device, storage medium and the x-ray system of image background regions
CN110223352A (en) * 2019-06-14 2019-09-10 浙江明峰智能医疗科技有限公司 A kind of medical image scanning automatic positioning method based on deep learning

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