CN112308845A - Left ventricle segmentation method and device and electronic equipment - Google Patents
Left ventricle segmentation method and device and electronic equipment Download PDFInfo
- Publication number
- CN112308845A CN112308845A CN202011209809.7A CN202011209809A CN112308845A CN 112308845 A CN112308845 A CN 112308845A CN 202011209809 A CN202011209809 A CN 202011209809A CN 112308845 A CN112308845 A CN 112308845A
- Authority
- CN
- China
- Prior art keywords
- segmentation
- segmentation result
- left ventricular
- left ventricle
- cavity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
-
- 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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Medical Informatics (AREA)
- Quality & Reliability (AREA)
- Radiology & Medical Imaging (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
The application provides a left ventricle segmentation method, a device and electronic equipment, wherein the method comprises the following steps: acquiring cardiac medical image data; determining the apex position and the basal position in the medical image data according to the physiological structural features of the heart; determining a left ventricular cavity segmentation result by adopting a region growing algorithm based on the apex position and the basal position; according to the left ventricle cavity segmentation result, obtaining a left myocardium segmentation result based on level set algorithm segmentation; and combining the left ventricle cavity segmentation result and the left myocardium segmentation result to obtain a left ventricle segmentation result. Because the apex and the cardinal are positioned in the ventricle, a more accurate left ventricle cavity segmentation result can be obtained by adopting a region growing method based on the apex and the cardinal, and because the gray difference between the left ventricle cavity and the left myocardium is large and the gray gradient change is obvious, the level set algorithm is utilized to perform accurate left myocardium segmentation on the basis of the left ventricle cavity segmentation result, so that the evolution speed is high.
Description
Technical Field
The present application relates to the field of medical image technologies, and in particular, to a left ventricle segmentation method and apparatus, and an electronic device.
Background
Heart disease has become the first killer of human health in recent years. How to effectively and accurately diagnose cardiovascular diseases has become the first topic of medical workers. From the cardiac mechanism point of view, the left ventricle, which is responsible for supplying blood to the various organs throughout the body, is the most important part of the heart. Therefore, the abnormality of its morphology and motion is used as an important basis for evaluating the heart lesion. With the development of medical imaging technology, noninvasive left ventricle-based cardiac function analysis has become an important auxiliary tool for clinical diagnosis of heart diseases.
At present, there are many imaging methods for cardiac function analysis, and ultrasound and nuclear magnetic resonance are commonly used clinically. The ultrasonic field of vision is limited, the image is easily subject to the subjective image of an operator, the spatial resolution is poor, the measured value has large deviation and poor repeatability, and the nuclear magnetic resonance has higher time and spatial resolution, but the scanning time is long, the price is high, and the clinical application is limited. With the rapid development of the multi-slice helical CT technology in recent years, the scanning speed, the good spatial and temporal resolution, make its application in the cardiac method become the mainstream imaging technology in clinical diagnosis.
The cardiac function analysis is mainly to observe the morphological structure of the left ventricle through image segmentation, and to obtain the functional analysis of the left ventricle by adopting a motion estimation technology, so as to estimate the overall function of the left ventricle and the local function of the left myocardium, which is the current non-invasive cardiac function analysis main technology. Currently, multilayer spiral CT has few studies on cardiac function analysis, and clinically, the endocardial and epicardial contours of the left ventricle are often manually outlined, and then the system automatically calculates the parameters of the marked region. The fully automatic left ventricle and left myocardium segmentation based on the multi-layer helical CT is a development trend and research hotspot for assisted clinical application.
There are many algorithms for left ventricle segmentation at present, but most of them are based on left ventricle segmentation of ultrasound and nuclear magnetic resonance, and few are based on CT images. These segmentation algorithms are mainly classified into the following categories: traditional image segmentation algorithms, model-based segmentation methods, level set-based segmentation algorithms, clustering, and graph-based segmentation methods.
The conventional segmentation method mainly comprises the following steps: a threshold segmentation method, a region growing method and an edge detection method. The threshold segmentation method considers the gray value of an image and does not consider the spatial distribution of the image, so that the threshold segmentation method is very sensitive to noise. The region growing method is limited by the contrast of the image and is prone to under-segmentation or over-segmentation. Edge detection of images has a poor effect on blurred edges and is often undetectable.
Model-based segmentation algorithms, such as active shape models and active surface models. By using the prior knowledge of the left ventricle to build the model, the accuracy of the segmentation result depends heavily on the training model, is influenced by the subjective delineation training model, and is time-consuming and labor-consuming.
The level set-based segmentation algorithm can better perform edge segmentation on noisy data, and is more mature in describing the boundary of the left myocardium. However, there still exist some problems and difficulties in implementation, such as high computational complexity, initialization of zero level set, several parameters, and much time spent in the evolution process, and easily selecting wrong local minima in the heterogeneous region, especially leakage at low gray level gradient change.
The fuzzy clustering algorithm allows clusters to grow into natural shapes, and can be used for any latitude and any resolution of an image sequence. The main drawback of this method is the difficulty in handling outliers, the membership of data points in a cluster directly depends on the membership values of other cluster centers, and sometimes produces unreliable results.
The graph-based segmentation method does not need to rely heavily on or learn explicitly a priori knowledge, can obtain arbitrary segmentation through sufficient user interaction, and is easier to expand into 3D and higher dimension segmentation. However, the algorithm has some drawbacks: if multiple interactions by the user are required, it is susceptible to the "minimal cut" problem in weak boundary situations.
Therefore, how to accurately segment the left ventricle becomes an urgent technical problem to be solved.
Disclosure of Invention
The application provides a left ventricle segmentation method, a left ventricle segmentation device and electronic equipment, which are used for solving the technical problem that the left ventricle is difficult to be accurately segmented in the related art.
According to an aspect of an embodiment of the present application, there is provided a left ventricle segmentation method including: acquiring cardiac medical image data; determining the apex position and the basal position in the medical image data according to the physiological structural features of the heart; determining a left ventricular cavity segmentation result based on the apex position and the cardinal position by adopting a region growing algorithm; obtaining a left cardiac muscle segmentation result by segmentation based on a level set algorithm according to the left ventricular cavity segmentation result; and combining the left ventricle cavity segmentation result and the left myocardium segmentation result to obtain a left ventricle segmentation result.
Optionally, the cardiac physiology comprises the ascending aorta from the left ventricle; the determining the apex position and the basal position according to the physiological structural features of the heart comprises the following steps: obtaining an ascending aorta segmentation result, wherein the ascending aorta segmentation result comprises ascending aorta position information; taking the joint of the ascending aorta and the left ventricle as a first growth seed point; on the basis of the first growth seed point, performing region growth according to a first preset growth condition based on a first region growth algorithm to obtain a left ventricle interested region, wherein the first preset growth condition is related to the segmentation result of the ascending aorta; determining the apex location and the base location from the left ventricular region of interest and the ascending aorta location information.
Optionally, the determining the apex location and the base location from the left ventricular region of interest and the ascending aorta location information comprises: obtaining the enclosing area of the lowest end plane of the ascending aorta in the Z-axis direction; determining a relative direction of a left ventricle and the ascending aorta based on the enclosed region; finding boundary points of the region of interest of the left ventricle in the opposite direction in the plane of the bounding region; determining the cardinal location based on the boundary points; determining the apex location based on the left ventricular region of interest, the relative orientation, and the cardinal location.
Optionally, the determining the left ventricular chamber segmentation result based on the apex location and the basal location using a region growing algorithm comprises: determining the apex location and the base location as a left ventricular cavity central axis; selecting a second growth seed point on the central axis of the left ventricle cavity; and performing region growth according to a second preset growth condition based on a second region growth algorithm on the basis of the second growth seed point to obtain a first left ventricle cavity segmentation result, wherein the second preset growth condition is related to the growth seed point.
Optionally, the determining the left ventricular chamber segmentation result based on the apex location and the basal location using a region growing algorithm further comprises: dividing the first left ventricular cavity segmentation result into a plurality of first segmentation planes along the direction of the central axis of the left ventricular cavity, wherein the first segmentation planes are vertical to the central axis of the left ventricular cavity; and on the basis of the segmentation plane, performing region growth according to a third preset growth condition based on a second region growth algorithm to obtain a second left ventricular cavity segmentation result, wherein the third preset growth condition is related to the first left ventricular cavity segmentation result.
Optionally, the segmenting based on a level set algorithm from the left ventricular chamber segmentation result to obtain a left myocardial segmentation result comprises: taking the segmentation boundary of the left ventricular cavity segmentation result as a zero level set; and controlling the contour lines in the level set to expand by taking the zero level set as a reference to obtain a left myocardial segmentation result.
Optionally, the merging the left ventricular cavity segmentation result and the left myocardial segmentation result to obtain a left ventricular segmentation result includes: correcting for a cardinal position based on an area of the ascending aorta; determining a left ventricular cavity two-dimensional segmentation result and a left myocardial two-dimensional segmentation result based on the corrected cardiac base position; and projecting the two-dimensional segmentation result of the left ventricle cavity and the two-dimensional segmentation result of the left myocardium to a three-dimensional space to obtain a left ventricle segmentation result.
According to a second aspect, an embodiment of the present invention provides a left ventricular segmentation apparatus, including: an acquisition module for acquiring cardiac medical image data; the first determination module is used for determining the apex position and the basal position in the medical image data according to the physiological structural characteristics of the heart; a second determination module for determining a left ventricular chamber segmentation result based on the apex location and the cardinal location using a region growing algorithm; the left cardiac muscle segmentation module is used for obtaining a left cardiac muscle segmentation result by segmentation based on a level set algorithm according to the left ventricular cavity segmentation result; and the merging module is used for merging the left ventricle cavity segmentation result and the left myocardium segmentation result to obtain a left ventricle segmentation result.
According to a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete mutual communication through the communication bus, and the memory is used for storing a computer program; the processor is configured to execute the steps of the left ventricular segmentation method according to any one of the first aspect above by executing the computer program stored in the memory.
According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to, when executed, perform the steps of the left ventricle segmentation method described in any one of the above first aspects.
In an embodiment of the present application, cardiac medical image data is acquired; determining the apex position and the basal position in the medical image data according to the physiological structural features of the heart; determining a left ventricular cavity segmentation result based on the apex position and the cardinal position by adopting a region growing algorithm; according to the left ventricle cavity segmentation result, obtaining a left myocardium segmentation result based on level set algorithm segmentation; and combining the left ventricle cavity segmentation result and the left myocardium segmentation result to obtain a left ventricle segmentation result. The method comprises the steps of firstly determining the position of an apex and the position of a cardinal, obtaining a relatively accurate ventricular cavity segmentation result by adopting a region growing method based on the apex and the cardinal because the apex and the cardinal are positioned in a left ventricle, accurately segmenting the left myocardium because the gray difference between the left ventricular cavity and the left myocardium is large and the gray gradient change is obvious, and accurately segmenting the left myocardium by using a level set algorithm on the basis of the left ventricular cavity, wherein the evolution speed is high. The system helps clinical workers to obtain accurate structures of the left ventricle and the left myocardium quickly, reduces interaction times, and thus greatly improves diagnosis efficiency of cardiac function analysis.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a schematic diagram of a hardware environment for an alternative left ventricular segmentation method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram of an alternative left ventricular segmentation method according to an embodiment of the present application;
FIG. 3 is a schematic illustration of an aortic enclosure region in an alternative left ventricular segmentation in accordance with embodiments of the present application;
FIG. 4 is a schematic illustration of another alternative heart-based location selection in accordance with an embodiment of the present application;
FIG. 5 is a schematic illustration of segmentation plane selection in an alternative left ventricular segmentation in accordance with an embodiment of the present application;
FIG. 6 is a diagram illustrating two-dimensional planar left myocardium segmentation results in another alternative left ventricle segmentation according to an embodiment of the present application
FIG. 7 is a block diagram of an alternative left ventricular segmentation device in accordance with an embodiment of the present application;
fig. 8 is a block diagram of an alternative electronic device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, the present application proposes a left ventricle segmentation method, which may be optionally applied in the hardware environment formed by the terminal 102 and the server 104 shown in fig. 1 in this embodiment. As shown in fig. 1, the server 104 is connected to the terminal 102 through a network, and may be configured to provide services (such as game services, application services, and the like) for the terminal or a client installed on the terminal, set a database on the server or independent of the server, provide data storage services for the server 104, and process cloud services, where the network includes but is not limited to: the terminal 102 is not limited to a PC, a mobile phone, a tablet computer, etc. the terminal may be a wide area network, a metropolitan area network, or a local area network. The left ventricle segmentation method according to the embodiment of the present application may be executed by the server 104, the terminal 102, or both the server 104 and the terminal 102. The terminal 102 may perform the left ventricle segmentation method according to the embodiment of the present application, or may perform the left ventricle segmentation method by a client installed thereon.
Taking the left ventricle segmentation method in the present embodiment as an example, executed by the server 104, fig. 2 is a schematic flowchart of an alternative left ventricle segmentation method according to an embodiment of the present application, and as shown in fig. 2, the flowchart of the method may include the following steps:
step S202, acquiring cardiac medical image data;
step S204, determining the apex position and the basal position in the medical image data according to the physiological structural features of the heart;
step S206, determining a left ventricle cavity segmentation result by adopting a region growing algorithm based on the apex position and the basal position;
step S208, obtaining a left cardiac muscle segmentation result by segmentation based on a level set algorithm according to the left ventricular cavity segmentation result;
and step S210, combining the left ventricle cavity segmentation result and the left myocardium segmentation result to obtain a left ventricle segmentation result.
Acquiring cardiac medical image data; determining the apex position and the basal position in the medical image data according to the physiological structural features of the heart; determining a left ventricular cavity segmentation result based on the apex position and the cardinal position by adopting a region growing algorithm; obtaining a left cardiac muscle segmentation result by segmentation based on a level set algorithm according to the left ventricular cavity segmentation result; and combining the left ventricle cavity segmentation result and the left myocardium segmentation result to obtain a left ventricle segmentation result. The heart apex position and the heart base position are determined firstly, and the heart apex and the heart base are positioned in the heart chamber, so that a relatively accurate heart chamber segmentation result can be obtained by adopting a region growing method based on the heart apex and the heart base. The system helps clinical workers to obtain accurate structures of the left ventricle and the left myocardium quickly, reduces interaction times, and thus greatly improves diagnosis efficiency of cardiac function analysis.
In the technical solution of step S202, as an exemplary embodiment, the medical image data of the heart may be a CT image, that is, a cross-sectional scan image of the heart, and specifically, the medical image data may include a multi-slice cross-sectional scan image.
In the solution of step S204, after obtaining the cardiac medical image data, the apex position and the base position may be determined based on the physiological structural features of the heart, for example, based on the physiological structural features of the left ventricle, the ascending aorta and the left atrium. Wherein, the apex is the apex of the heart, which is the tip part of the heart with a conical shape at the left lower part and is composed of a left ventricle. The heart base is the position where the heart is accessed by the great vessels, namely the position where the aorta is accessed, and is positioned at the annular coronary sulcus which separates the upper atrium and the lower left ventricle, namely the apex position is the top end of the left ventricle, and the heart base position is the bottom end of the left ventricle.
In this embodiment, the ascending aorta may be obtained by segmentation based on the existing segmentation algorithm, and the segmentation result may be obtained by equally dividing the ascending aorta by, for example, a threshold segmentation method, a region growing method, and an edge detection method. Since the heart base position is located at the aorta access position, i.e. the junction of the ascending aorta and the left ventricle, the heart base position can be determined based on the ascending aorta position, and the heart apex position can be determined based on the heart base position and the CT image based on the priori knowledge.
As an alternative embodiment, the region of interest of the left ventricle, which may include the left ventricle, a portion of the left atrium, and possibly other highlighted tissue, i.e., the approximate region of the left ventricle, may be determined prior to determining the location of the base of the heart and the location of the apex of the heart. For example, an ascending aorta segmentation result may be obtained, the ascending aorta segmentation result including ascending aorta position information; taking the joint of the ascending aorta and the left ventricle as a first growth seed point; on the basis of the first growth seed point, performing region growth according to a first preset growth condition based on a first region growth algorithm to obtain a left ventricle interested region, wherein the first preset growth condition is related to the segmentation result of the ascending aorta. Specifically, on the basis of ascending aorta segmentation, the Mean, variance, Std, and minimum MinCTThr of CT values of the aorta are obtained. Considering that the aorta is connected with the left ventricle, a connecticut Z-level first region growing point at the position where the aorta ascends is connected with the left ventricle can be found according to the result of the ascending aorta segmentation, and the left ventricle and a part of the left atrium region connected with the left ventricle are segmented from connecticut Z downwards along the Z-axis direction (towards the apex of the heart from the connecting position of the left ventricle and the aorta ascends). The segmentation method adopts a first region growing algorithm, wherein the first preset growing condition can be that a seed point (pixel or region) satisfying at least one of the following conditions is used as a growing seed point (pixel or region) of the region of interest of the left ventricle:
1) CT value greater than Mean
2) CT value is greater than MinCTThr, and CT value is greater than Mean-2 × Std, less than Mean +2 × Std
The CT value of the current seed point satisfies any of the above conditions, i.e., belongs to the region of interest R1 of the left ventricle.
Determining the apex location and the base location from the left ventricular region of interest and the ascending aorta location information. Specifically, on the basis of the results of R1 and aorta segmentation, the position of the heart base is determined according to the position of the aorta, and the specific steps are as follows: obtaining the enclosing area of the lowest end plane of the ascending aorta in the Z-axis direction; determining a relative direction of a left ventricle and the ascending aorta based on the enclosed region; finding boundary points of the region of interest of the left ventricle in the opposite direction in the plane of the bounding region; determining the cardinal location based on the boundary points; determining the apex location based on the left ventricular region of interest, the relative orientation, and the cardinal location. Illustratively, the enclosing region of the 2D plane of the lowest aorta in the Z-axis direction is obtained, as shown in FIG. 3. Wherein (MinX, MinY) is the plane minimum point coordinate, (MaxX, MaxY) is the plane maximum point coordinate, the point B coordinate is ((MaxX + MinX) × 0.5, MaxY), passing through the point A and the point B, a vector can be obtained
Along a vector on the current bounding region plane
And searching for the interest region point of the segmented left ventricle, obtaining two boundary points of C and D, and taking the middle point between C and D as a heart base point.
Take the Z axis and
the plane formed by the two vectors is taken as the interception plane P1, the area left and below the interception plane P1 is removed in the area R1, the area R2 right and above the interception plane P1 is reserved, and the farthest point of the centrifugal base is found in the area R2 to be taken as the apical point.
In the technical solution in step S206, since the base position and the apex position and the middle region thereof are all in the left ventricle cavity after the base position and the apex position are obtained, the base position and the apex position and the middle region thereof are similar to the CT value inside the left ventricle cavity, and therefore, region growth can be performed based on the base position and the apex position and the middle region thereof as growth points, contrast of images can be divided more clearly, accuracy of region growth is improved, and specifically, the apex position and the base position are used to determine the central axis of the left ventricle cavity; selecting a second growth seed point on the central axis of the left ventricle cavity; and performing region growth according to a second preset growth condition based on a second region growth sub-algorithm on the basis of the second growth seed point to obtain a first left ventricle cavity segmentation result, wherein the second preset growth condition is related to the growth seed point. Specifically, referring to FIG. 5, the apex CA and the cardinal CB can be crossed to obtain the central axis
As shown in fig. 4, along the central axis
Sampling at an interval M (e.g., M ═ 3 mm) from CA to CB may obtain N points, where the N points are seed points for region growing, and specifically, the second preset growing condition may be that a seed point satisfying at least one of the following conditions is used as a left ventricle cavity regionThe seed points of (a):
1) CT value greater than Mean
2) CT value is greater than MinCTThr and greater than Mean-2 Std, less than Mean +2 Std
The CT value of the current seed point satisfies any of the above conditions, i.e., belongs to the left ventricular chamber region R3. Where Mean is the average CT value of N points on the central axis, MinCTThr is the minimum of the CT values of the ascending aorta, and Std is the variance of the CT values of the ascending aorta.
As an exemplary embodiment, in order to further precisely segment the left ventricular cavity region so as to obtain a clearer boundary of the left ventricular cavity, i.e. the left ventricular endocardium, the left ventricle may be re-segmented based on the first left ventricular cavity segmentation result, and since most of the first left ventricular cavity segmentation result is the correct segmentation result, the CT value in the first left ventricular cavity segmentation result may be used as the region growing condition for re-segmentation, so that the accuracy of the segmentation of the left ventricular cavity may be further improved. Illustratively, the first left ventricular chamber division result is divided into a plurality of first division planes along the direction of the central axis of the left ventricular chamber, and the first division planes are perpendicular to the central axis of the left ventricular chamber; and on the basis of the segmentation plane, performing region growth according to a third preset growth condition based on a second region growth sub-algorithm to obtain a second left ventricle cavity segmentation result, wherein the third preset growth condition is related to the first left ventricle cavity segmentation result. See fig. 5, with the central axis of the left ventricle
For the vertical axis, sampling at intervals M (e.g., M3 mm) from CA to CB may yield N raw data planes centered at points on the central axis and sized to completely encompass the left ventricular cross-section (e.g., 256 × 256), of which N segmentation planes the first left ventricular chamber segmentation result is contained. On the basis of each division plane, 2-dimensional region growth is respectively carried out, and the conditions of the region growth are as follows:
CT value greater than Mean
CT value is greater than MinCTThr and greater than Mean-2 Std, less than Mean +2 Std
The CT value of the current seed point satisfies any of the above conditions, i.e., belongs to the second left ventricular cavity region R4. Where Mean is the CT Mean of the segmentation result of the first left ventricular chamber, MinCTThr is the minimum of the CT values of the ascending aorta, and Std is the variance of the CT values of the ascending aorta.
For the solution in step S208, since the CT value of the left myocardium is much lower than that of the ventricle, but is close to other surrounding tissues, and the edge is weak, the left myocardium is segmented by using the level set method. Specifically, the segmentation boundary of the left ventricular cavity segmentation result is used as a zero level set; and controlling the contour lines in the level set to expand by taking the zero level set as a reference to obtain a left myocardial segmentation result. Illustratively, the zero level set is the segmentation boundary of the ventricle, i.e., the endocardium. Since the myocardium is always ventricular and has smooth boundary, the contour lines in the horizontal set can be controlled to expand outwards at each iteration and maintain a greater smoothness, so that the complete left myocardium, i.e. epicardium, can be segmented as shown in fig. 6.
For the technical solution in step S210, the obtained segmentation result of the left ventricular cavity and the segmentation result of the left myocardium are combined and projected to a three-dimensional space, so that a complete left ventricular segmentation can be obtained. Since the heart base position and the heart apex position are determined based on the physiological structure of the heart and the region of interest of the left ventricle including the left atrium, other tissues and the left ventricle, there may be a deviation in the heart base position, and therefore, the heart base position needs to be corrected, specifically, the heart base position is corrected based on the area of the ascending aorta; determining a left ventricular cavity two-dimensional segmentation result and a left myocardial two-dimensional segmentation result based on the corrected cardiac base position; and projecting the two-dimensional segmentation result of the left ventricle cavity and the two-dimensional segmentation result of the left myocardium to a three-dimensional space to obtain a left ventricle segmentation result. Illustratively, correcting the cardinal position based on the area of the ascending aorta comprises: dividing the heart apex position to the heart base position into a plurality of second division planes in a direction from the heart apex position to the heart base position, wherein the second division planes are perpendicular to the direction from the heart apex position to the heart base position; sequentially calculating the area of the ascending aorta in the segmentation plane along the direction from the apex position to the base position; and taking the central point of the left ventricular cavity region in the left ventricular cavity segmentation result in the second segmentation plane with the area of the ascending aorta larger than the preset value as the corrected heart base position. As an exemplary embodiment, N segmentation planes are arranged in the order from CA to CB, the area of the ascending aorta in the segmentation planes is determined layer by layer, when the area of the ascending aorta is larger than a certain value (for example, 50 square millimeters), it is considered that the left atrium is reached, the two-dimensional plane is the new interception plane P2, all plane sets PR from the CA point to the interception plane P2 are reserved, all planes after the interception plane are removed, the central point of the ventricular region obtained by segmentation in the interception plane P2 is the new cardiac base point, and the plane set PR is the complete segmentation result of the left ventricle and the left myocardium.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., a ROM (Read-Only Memory)/RAM (Random Access Memory), a magnetic disk, an optical disk) and includes several instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the methods of the embodiments of the present application.
According to another aspect of the embodiments of the present application, there is also provided a left ventricle segmentation apparatus for implementing the above left ventricle segmentation method. Fig. 7 is a schematic diagram of an alternative left ventricular segmentation apparatus according to an embodiment of the present application, which may include, as shown in fig. 7:
an acquisition module 701, configured to acquire cardiac medical image data;
a first determining module 702, configured to determine an apex position and a base position in the medical image data according to the cardiac physiological structure feature;
a second determining module 703 for determining a left ventricular cavity segmentation result based on the apex position and the basal position by using a region growing algorithm;
a left myocardium segmentation module 704, configured to segment based on a level set algorithm according to the left ventricle cavity segmentation result to obtain a left myocardium segmentation result;
a merging module 705, configured to merge the left ventricular cavity segmentation result and the left myocardial segmentation result to obtain a left ventricular segmentation result.
It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments. It should be noted that the modules described above as a part of the apparatus may be operated in a hardware environment as shown in fig. 1, and may be implemented by software, or may be implemented by hardware, where the hardware environment includes a network environment.
According to yet another aspect of the embodiments of the present application, there is also provided an electronic device for implementing the left ventricle segmentation method described above, which may be a server, a terminal, or a combination thereof.
Fig. 8 is a block diagram of an alternative electronic device according to an embodiment of the present application, as shown in fig. 8, including a processor 802, a communication interface 804, a memory 806, and a communication bus 808, where the processor 802, the communication interface 804, and the memory 806 are in communication with each other via the communication bus 808, where,
a memory 806 for storing a computer program;
the processor 802, when executing the computer program stored in the memory 806, performs the following steps:
s1, acquiring cardiac medical image data;
s2, determining the apex position and the basal position in the medical image data according to the physiological structure characteristics of the heart;
s3, determining a left ventricle cavity segmentation result by adopting a region growing algorithm based on the apex position and the basal position;
s4, obtaining a left myocardium segmentation result by segmentation based on a level set algorithm according to the left ventricle cavity segmentation result;
and S5, combining the left ventricle cavity segmentation result and the left myocardium segmentation result to obtain a left ventricle segmentation result.
Alternatively, in this embodiment, the communication bus may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The memory may include RAM, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory. Alternatively, the memory may be at least one memory device located remotely from the processor.
The processor may be a general-purpose processor, and may include but is not limited to: a CPU (Central Processing Unit), an NP (Network Processor), and the like; but also a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
It will be understood by those skilled in the art that the structure shown in fig. 8 is only an illustration, and the device implementing the left ventricle segmentation method may be a terminal device, and the terminal device may be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 8 is a diagram illustrating a structure of the electronic device. For example, the terminal device may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 8, or have a different configuration than shown in FIG. 8.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disk, ROM, RAM, magnetic or optical disk, and the like.
According to still another aspect of an embodiment of the present application, there is also provided a storage medium. Alternatively, in the present embodiment, the storage medium may be used for program codes for executing the left ventricle segmentation method.
Optionally, in this embodiment, the storage medium may be located on at least one of a plurality of network devices in a network shown in the above embodiment.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps:
s1, acquiring cardiac medical image data;
s2, determining the apex position and the basal position in the medical image data according to the physiological structure characteristics of the heart;
s3, determining a left ventricle cavity segmentation result by adopting a region growing algorithm based on the apex position and the basal position;
s4, obtaining a left myocardium segmentation result by segmentation based on a level set algorithm according to the left ventricle cavity segmentation result;
and S5, combining the left ventricle cavity segmentation result and the left myocardium segmentation result to obtain a left ventricle segmentation result.
Optionally, the specific example in this embodiment may refer to the example described in the above embodiment, which is not described again in this embodiment.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a U disk, a ROM, a RAM, a removable hard disk, a magnetic disk, or an optical disk. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including instructions for causing one or more computer devices (which may be personal computers, servers, network devices, or the like) to execute all or part of the steps of the method described in the embodiments of the present application.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit is merely a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, and may also be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution provided in the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A left ventricular segmentation method, comprising:
acquiring cardiac medical image data;
determining the apex position and the basal position in the medical image data according to the physiological structural features of the heart;
determining a left ventricular cavity segmentation result based on the apex position and the cardinal position by adopting a region growing algorithm;
obtaining a left cardiac muscle segmentation result by segmentation based on a level set algorithm according to the left ventricular cavity segmentation result;
and combining the left ventricle cavity segmentation result and the left myocardium segmentation result to obtain a left ventricle segmentation result.
2. A left ventricular segmentation method as claimed in claim 1, characterized in that the cardiac physiological structure includes an ascending aorta emanating from the left ventricle;
the determining the apex position and the basal position according to the physiological structural features of the heart comprises the following steps:
obtaining an ascending aorta segmentation result, wherein the ascending aorta segmentation result comprises ascending aorta position information;
taking the joint of the ascending aorta and the left ventricle as a first growth seed point;
on the basis of the first growth seed point, performing region growth according to a first preset growth condition based on a first region growth algorithm to obtain a left ventricle interested region, wherein the first preset growth condition is related to the segmentation result of the ascending aorta;
determining the apex location and the base location from the left ventricular region of interest and the ascending aorta location information.
3. A left ventricular segmentation method as claimed in claim 2, wherein the determining the apex location and the base location from the left ventricular region of interest and the ascending aorta location information comprises:
obtaining the enclosing area of the lowest end plane of the ascending aorta in the Z-axis direction;
determining a relative direction of a left ventricle and the ascending aorta based on the enclosed region;
finding boundary points of the region of interest of the left ventricle in the opposite direction in the plane of the bounding region;
determining the cardinal location based on the boundary points;
determining the apex location based on the left ventricular region of interest, the relative orientation, and the cardinal location.
4. A left ventricular segmentation method as claimed in claim 1 wherein determining left ventricular chamber segmentation results using a region growing algorithm based on the apex location and the base location comprises:
determining the apex location and the base location as a left ventricular cavity central axis;
selecting a second growth seed point on the central axis of the left ventricle cavity;
and performing region growth according to a second preset growth condition based on a second region growth algorithm on the basis of the second growth seed point to obtain a first left ventricle cavity segmentation result, wherein the second preset growth condition is related to the growth seed point.
5. A left ventricular segmentation method as claimed in claim 4 wherein determining a left ventricular cavity segmentation result using a region growing algorithm based on the apex location and the base location further comprises:
dividing the first left ventricular cavity segmentation result into a plurality of first segmentation planes along the direction of the central axis of the left ventricular cavity, wherein the first segmentation planes are vertical to the central axis of the left ventricular cavity;
and on the basis of the segmentation plane, performing region growth according to a third preset growth condition based on a second region growth algorithm to obtain a second left ventricular cavity segmentation result, wherein the third preset growth condition is related to the first left ventricular cavity segmentation result.
6. A left ventricular segmentation method as claimed in claim 1, wherein the left myocardial segmentation based on level set algorithm based segmentation from the left ventricular cavity segmentation result comprises:
taking the segmentation boundary of the left ventricular cavity segmentation result as a zero level set;
and controlling the contour lines in the level set to expand by taking the zero level set as a reference to obtain a left myocardial segmentation result.
7. A left ventricular segmentation method as claimed in claim 1, wherein the merging the left ventricular cavity segmentation result and the left myocardial segmentation result to obtain a left ventricular segmentation result comprises:
correcting for a cardinal position based on an area of the ascending aorta;
determining a left ventricular cavity two-dimensional segmentation result and a left myocardial two-dimensional segmentation result based on the corrected cardiac base position;
and projecting the two-dimensional segmentation result of the left ventricle cavity and the two-dimensional segmentation result of the left myocardium to a three-dimensional space to obtain a left ventricle segmentation result.
8. A left ventricular segmentation device, comprising:
an acquisition module for acquiring cardiac medical image data;
the first determination module is used for determining the apex position and the basal position in the medical image data according to the physiological structural characteristics of the heart;
a second determination module for determining a left ventricular chamber segmentation result based on the apex location and the cardinal location using a region growing algorithm;
the left cardiac muscle segmentation module is used for obtaining a left cardiac muscle segmentation result by segmentation based on a level set algorithm according to the left ventricular cavity segmentation result;
and the merging module is used for merging the left ventricle cavity segmentation result and the left myocardium segmentation result to obtain a left ventricle segmentation result.
And combining the first blood vessel and the second blood vessel to obtain a blood vessel segmentation result.
9. An electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein said processor, said communication interface and said memory communicate with each other via said communication bus,
the memory for storing a computer program;
the processor for performing the left ventricular segmentation method steps of any one of claims 1 to 7 by executing the computer program stored on the memory.
10. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to carry out the steps of the left ventricular segmentation method as claimed in any one of claims 1 to 7 when executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011209809.7A CN112308845B (en) | 2020-11-03 | 2020-11-03 | Left ventricle segmentation method and device and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011209809.7A CN112308845B (en) | 2020-11-03 | 2020-11-03 | Left ventricle segmentation method and device and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112308845A true CN112308845A (en) | 2021-02-02 |
| CN112308845B CN112308845B (en) | 2021-07-02 |
Family
ID=74332520
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011209809.7A Active CN112308845B (en) | 2020-11-03 | 2020-11-03 | Left ventricle segmentation method and device and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112308845B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114272522A (en) * | 2021-10-19 | 2022-04-05 | 中南大学湘雅二医院 | Radiation treatment method and equipment for treating cardiomyopathy based on myocardial positioning |
| CN114419032A (en) * | 2022-03-14 | 2022-04-29 | 深圳科亚医疗科技有限公司 | Method and device for segmenting the endocardium and/or the epicardium of the left ventricle of the heart |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1806758A (en) * | 2005-01-21 | 2006-07-26 | 西门子公司 | Method for automatically determining left ventricle position and orientation in 3-d data of heart |
| US20100215238A1 (en) * | 2009-02-23 | 2010-08-26 | Yingli Lu | Method for Automatic Segmentation of Images |
| CN101849240A (en) * | 2007-05-21 | 2010-09-29 | 康奈尔大学 | Method for segmenting objects in images |
| CN102397070A (en) * | 2011-01-26 | 2012-04-04 | 无锡宙示医学软件开发有限公司 | Method for fully-automatically segmenting and quantifying left ventricle of cardiac magnetic resonance image |
| EP2618307A1 (en) * | 2012-01-17 | 2013-07-24 | Consiglio Nazionale Delle Ricerche | Method and system for determining the volume of epicardial fat from volumetric images, and corresponding computer program |
| CN105741310A (en) * | 2016-03-21 | 2016-07-06 | 东北大学 | Heart's left ventricle image segmentation system and method |
| US20180005388A1 (en) * | 2015-01-26 | 2018-01-04 | Biosensors International Group, Ltd. | Systems and methods for medical image registration |
| CN108876769A (en) * | 2018-05-31 | 2018-11-23 | 厦门大学 | A kind of left auricle of heart CT image partition method |
| CN109272512A (en) * | 2018-09-25 | 2019-01-25 | 南昌航空大学 | A kind of method of outer membrane in automatic segmentation left ventricle |
| CN109427417A (en) * | 2017-08-30 | 2019-03-05 | 西门子保健有限责任公司 | Method for dividing the organ structure of check object in medical image |
| CN110335236A (en) * | 2019-04-29 | 2019-10-15 | 上海依智医疗技术有限公司 | Processing method, processing unit, processing system and the medium of cardiologic medical image |
| CN111028254A (en) * | 2019-12-09 | 2020-04-17 | 上海交通大学医学院附属上海儿童医学中心 | Left myocardium segmentation method based on cardiac cine magnetic resonance image |
| CN111383259A (en) * | 2020-03-02 | 2020-07-07 | 联影智能医疗科技(北京)有限公司 | Image analysis method, computer device, and storage medium |
| CN111784696A (en) * | 2020-06-28 | 2020-10-16 | 深圳大学 | Method and system for training right ventricle segmentation model and right ventricle segmentation |
| CN111815597A (en) * | 2020-07-01 | 2020-10-23 | 杭州脉流科技有限公司 | Left ventricle long and short axis tangent plane extraction method and device based on CT image, computer equipment and storage medium |
-
2020
- 2020-11-03 CN CN202011209809.7A patent/CN112308845B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1806758A (en) * | 2005-01-21 | 2006-07-26 | 西门子公司 | Method for automatically determining left ventricle position and orientation in 3-d data of heart |
| CN101849240A (en) * | 2007-05-21 | 2010-09-29 | 康奈尔大学 | Method for segmenting objects in images |
| US20100215238A1 (en) * | 2009-02-23 | 2010-08-26 | Yingli Lu | Method for Automatic Segmentation of Images |
| CN102397070A (en) * | 2011-01-26 | 2012-04-04 | 无锡宙示医学软件开发有限公司 | Method for fully-automatically segmenting and quantifying left ventricle of cardiac magnetic resonance image |
| EP2618307A1 (en) * | 2012-01-17 | 2013-07-24 | Consiglio Nazionale Delle Ricerche | Method and system for determining the volume of epicardial fat from volumetric images, and corresponding computer program |
| US20180005388A1 (en) * | 2015-01-26 | 2018-01-04 | Biosensors International Group, Ltd. | Systems and methods for medical image registration |
| CN105741310A (en) * | 2016-03-21 | 2016-07-06 | 东北大学 | Heart's left ventricle image segmentation system and method |
| CN109427417A (en) * | 2017-08-30 | 2019-03-05 | 西门子保健有限责任公司 | Method for dividing the organ structure of check object in medical image |
| CN108876769A (en) * | 2018-05-31 | 2018-11-23 | 厦门大学 | A kind of left auricle of heart CT image partition method |
| CN109272512A (en) * | 2018-09-25 | 2019-01-25 | 南昌航空大学 | A kind of method of outer membrane in automatic segmentation left ventricle |
| CN110335236A (en) * | 2019-04-29 | 2019-10-15 | 上海依智医疗技术有限公司 | Processing method, processing unit, processing system and the medium of cardiologic medical image |
| CN111028254A (en) * | 2019-12-09 | 2020-04-17 | 上海交通大学医学院附属上海儿童医学中心 | Left myocardium segmentation method based on cardiac cine magnetic resonance image |
| CN111383259A (en) * | 2020-03-02 | 2020-07-07 | 联影智能医疗科技(北京)有限公司 | Image analysis method, computer device, and storage medium |
| CN111784696A (en) * | 2020-06-28 | 2020-10-16 | 深圳大学 | Method and system for training right ventricle segmentation model and right ventricle segmentation |
| CN111815597A (en) * | 2020-07-01 | 2020-10-23 | 杭州脉流科技有限公司 | Left ventricle long and short axis tangent plane extraction method and device based on CT image, computer equipment and storage medium |
Non-Patent Citations (5)
| Title |
|---|
| MOSTAFA GHELICH OGHLI 等: "Left Ventricle Segmentation Using a Combination of Region Growing and Graph Based Method", 《IRANIAN JOURNAL OF RADIOLOGY》 * |
| NIKOS PARAGIOS: "A level set approach for shape-driven segmentation and tracking of the left ventricle", 《IEEE TRANSACTIONS ON MEDICAL IMAGING》 * |
| 唐思源 等: "区域生长和水平集相融合的肺部CT图像分割", 《电子技术应用》 * |
| 徐胜舟 等: "基于空洞卷积密集连接网络的左心室MR图像分割方法", 《中南民族大学学报（自然科学版)》 * |
| 范梦怡: "基于解剖学知识的心脏图像分割算法研究", 《中国优秀硕士学位论文全文数据库 医药卫生科技辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114272522A (en) * | 2021-10-19 | 2022-04-05 | 中南大学湘雅二医院 | Radiation treatment method and equipment for treating cardiomyopathy based on myocardial positioning |
| CN114272522B (en) * | 2021-10-19 | 2022-08-26 | 中南大学湘雅二医院 | Myocardial positioning based radiation therapy method and equipment for treating cardiomyopathy |
| CN114419032A (en) * | 2022-03-14 | 2022-04-29 | 深圳科亚医疗科技有限公司 | Method and device for segmenting the endocardium and/or the epicardium of the left ventricle of the heart |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112308845B (en) | 2021-07-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9875581B2 (en) | Automated 3D reconstruction of the cardiac chambers from MRI or ultrasound | |
| US7822246B2 (en) | Method, a system and a computer program for integration of medical diagnostic information and a geometric model of a movable body | |
| CN108122616B (en) | Method for generating individual-specific cardiovascular models and use thereof | |
| JP2006055631A (en) | Method and device for visualizing biological structure using three-dimensional positional information resulted from segmentation | |
| WO2013131421A1 (en) | Device and method for determining physiological parameters based on 3d medical images | |
| WO2008091583A2 (en) | Image-based extraction for vascular trees | |
| CN112308845B (en) | Left ventricle segmentation method and device and electronic equipment | |
| CN110706241B (en) | Three-dimensional focus region extraction method and device | |
| Juang et al. | Automatic segmentation of the left-ventricular cavity and atrium in 3D ultrasound using graph cuts and the radial symmetry transform | |
| WO2018133098A1 (en) | Vascular wall stress-strain state acquisition method and system | |
| CN109410181B (en) | Heart image segmentation method and device | |
| US8064670B2 (en) | Analysis of multidimensional data | |
| JP5669977B1 (en) | Myocardial contour extraction technology | |
| CN112085730A (en) | Region-of-interest component analysis method, device, electronic device and medium | |
| CN106910182B (en) | Segmentation method of blood pool in end diastole image in cardiac function magnetic resonance image | |
| CN112258476A (en) | Echocardiography myocardial abnormal motion mode analysis method, system and storage medium | |
| CN111429457A (en) | Intelligent evaluation method, device, equipment and medium for brightness of local area of image | |
| CN112862827B (en) | Method, device, terminal and storage medium for determining opening parameters of left atrial appendage | |
| Goyal et al. | MRI image based patient specific computational model reconstruction of the left ventricle cavity and myocardium | |
| CN113362327B (en) | Region segmentation method, device, terminal and storage medium in chest image | |
| Medina et al. | Left ventricle myocardium segmentation in multi-slice computerized tomography | |
| Pluempitiwiriyawej et al. | Active contours with automatic initialization for myocardial perfusion analysis | |
| Jodoin et al. | Left-ventricle segmentation of SPECT images of rats | |
| KR102000615B1 (en) | A method for automatically extracting a starting point of coronary arteries, and an apparatus thereof | |
| CN113139961B (en) | Method and device for determining dominant type of coronary artery |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 100176 1st floor, building 8, 11 Kangding street, Beijing Economic and Technological Development Zone, Daxing District, Beijing Applicant after: Sinovision Technology (Beijing) Co.,Ltd. Address before: 1 / F, building 8, No. 11, Kangding street, Daxing Economic and Technological Development Zone, Beijing 100176 Applicant before: SAINUO WEISHENG TECHNOLOGY (BEIJING) Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |