CN111862062B - Method, device and equipment for optimizing central line - Google Patents
Method, device and equipment for optimizing central line Download PDFInfo
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Abstract
The embodiment of the specification discloses a method, a device and equipment for optimizing a central line, and belongs to the technical fields of medical images and computers. The method comprises the following steps: acquiring tumor-bearing arterial centerline data of craniocerebral image data to be processed; screening abnormal points in the tumor-bearing artery central line data to obtain first central line data, wherein the first central line data is abnormal central line data; performing interpolation processing on the first central line data, and correcting the first central line data to obtain second central line data, wherein the correction of the first central line data comprises the following steps: correcting positions of abnormal points in the first central line data and radiuses corresponding to the abnormal points; and smoothing the second central line data to optimize the central line data of the aneurysm-carrying artery.
Description
Technical Field
The present disclosure relates to the field of medical imaging and computer technologies, and in particular, to a method, an apparatus, and a device for centerline optimization.
Background
Intracranial aneurysms, also known as cerebral hemangiomas, are the first cause of subarachnoid hemorrhage due to abnormal distension occurring in the wall of the intracranial artery, and in cerebrovascular accidents, they are secondary to cerebral thrombosis and hypertensive cerebral hemorrhage, and are the third. Intracranial aneurysms are classified as non-ruptured aneurysms and ruptured aneurysms, wherein most of the intracranial aneurysms are non-ruptured aneurysms, however, once ruptured, spontaneous subarachnoid hemorrhage can be induced, and the rupture aneurysms become, and the fatal disability rate exceeds 50%, which seriously threatens the life of patients.
The aneurysm-carrying artery is a section of arterial vessel containing an aneurysm in the craniocerebral aneurysm, the aneurysm-carrying artery central line has important significance for the type selection of the stent to be inserted in the blood flow guiding simulation device, the display of the woven mesh after the stent is released and the like, and the accuracy of the aneurysm-carrying artery central line determines the type selection of the stent to be inserted, the display accuracy of the woven mesh after the stent is released and the calculation of a series of parameters based on the aneurysm-carrying artery central line.
However, the study on the centerline of the parent artery has never been conducted, nor has the optimization of the centerline of the parent artery been conducted, considering the abnormal point on the centerline of the parent artery.
Disclosure of Invention
The embodiment of the specification provides a method, a device and equipment for optimizing a central line, which are used for solving the following technical problems: at present, people do not consider abnormal points on the overload artery central line for research on the overload artery central line, and do not study on optimization of the overload artery central line, so that accuracy of calculation of a series of parameters based on the overload artery central line is affected, and accuracy of model selection of a stent to be inserted and display of a woven mesh after stent release are also affected.
In order to solve the above technical problems, the embodiments of the present specification are implemented as follows:
the method for optimizing the central line provided by the embodiment of the specification comprises the following steps:
Acquiring tumor-bearing arterial centerline data of craniocerebral image data to be processed;
screening abnormal points in the tumor-bearing artery central line data to obtain first central line data, wherein the first central line data is abnormal central line data;
Performing interpolation processing on the first central line data, and correcting the first central line data to obtain second central line data, wherein the correction of the first central line data comprises the following steps: correcting positions of abnormal points in the first central line data and radiuses corresponding to the abnormal points;
And smoothing the second central line data to optimize the central line data of the aneurysm-carrying artery.
Further, the interpolating processing is performed on the first centerline data, correcting the first centerline data to obtain second centerline data, and further includes:
Homogenizing the second central line data to obtain third central line data;
And diluting the third central line data to obtain fourth central line data, and taking the fourth central line data as new second central line data so as to carry out smoothing treatment on the new second central line data, thereby realizing optimization of the central line data of the tumor-bearing artery.
Further, the screening the abnormal points in the parent artery centerline data to obtain first centerline data specifically includes:
traversing each point on the data of the aneurysm-carrying artery central line, calculating a tangent plane with a blood vessel by taking the advancing direction of the current point as a normal vector, taking the point of the central line as a circle center, taking the point reaching the tangent plane as a radius, calculating the variance of the radius, and considering the current point as an abnormal point when the variance is larger than a preset value.
Further, the interpolating processing on the first centerline data, correcting the first centerline data, and obtaining second centerline data, specifically includes:
And carrying out interpolation processing on normal points before and after the abnormal point in the first central line data along the advancing direction, thereby correcting the first central line data and obtaining the second central line data.
Further, the homogenizing the second centerline data to obtain third centerline data specifically includes:
homogenizing every two points on the second central line with a step length of a fixed value to obtain the third central line data.
Further, the diluting the third centerline data to obtain fourth centerline data specifically includes:
diluting the third central line data with N as a unit to obtain fourth central line data, wherein the fourth central line data can be used as a scale.
Further, the method further comprises:
And acquiring the slope of the radius change based on the optimized parent artery centerline data and the parent artery centerline data, and carrying out radius correction on the parent artery to acquire the corrected radius.
The embodiment of the specification also provides a device for optimizing a central line, which comprises:
the acquisition module is used for acquiring the data of the central line of the aneurysm-carrying artery of the craniocerebral image data to be processed;
the screening module is used for screening abnormal points in the tumor-bearing artery central line data to obtain first central line data, wherein the first central line data are abnormal central line data, and the first central line data are corrected and comprise the following steps: correcting positions of abnormal points in the first central line data and radiuses corresponding to the abnormal points;
the correction module is used for carrying out interpolation processing on the first central line data, correcting the first central line data and obtaining second central line data;
and the smoothing processing module is used for carrying out smoothing processing on the second central line data to realize optimization of the central line data of the aneurysm-carrying artery.
Further, the interpolating processing is performed on the first centerline data, correcting the first centerline data to obtain second centerline data, and further includes:
The homogenizing module is used for homogenizing the second central line data to obtain third central line data;
And the dilution module is used for diluting the third central line data to obtain fourth central line data, and taking the fourth central line data as new second central line data so as to carry out smoothing treatment on the new second central line data and realize optimization of the central line data of the tumor-bearing artery.
Further, the screening the abnormal points in the parent artery centerline data to obtain first centerline data specifically includes:
traversing each point on the data of the aneurysm-carrying artery central line, calculating a tangent plane with a blood vessel by taking the advancing direction of the current point as a normal vector, taking the point of the central line as a circle center, taking the point reaching the tangent plane as a radius, calculating the variance of the radius, and considering the current point as an abnormal point when the variance is larger than a preset value.
Further, the interpolating processing on the first centerline data, correcting the first centerline data, and obtaining second centerline data, specifically includes:
And carrying out interpolation processing on normal points before and after the abnormal point in the first central line data along the advancing direction, thereby correcting the first central line data and obtaining the second central line data.
Further, the homogenizing the second centerline data to obtain third centerline data specifically includes:
homogenizing every two points on the second central line with a step length of a fixed value to obtain the third central line data.
Further, the diluting the third centerline data to obtain fourth centerline data specifically includes:
diluting the third central line data with N as a unit to obtain fourth central line data, wherein the fourth central line data can be used as a scale.
Further, the apparatus further comprises:
And the radius correction module is used for acquiring the slope of the radius change based on the optimized parent artery centerline data and the parent artery centerline data, and carrying out radius correction on the parent artery to acquire the corrected radius.
The embodiment of the specification also provides an electronic device, including:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
Acquiring tumor-bearing arterial centerline data of craniocerebral image data to be processed;
screening abnormal points in the tumor-bearing artery central line data to obtain first central line data, wherein the first central line data is abnormal central line data;
Performing interpolation processing on the first central line data, and correcting the first central line data to obtain second central line data, wherein the correction of the first central line data comprises the following steps: correcting positions of abnormal points in the first central line data and radiuses corresponding to the abnormal points;
And smoothing the second central line data to optimize the central line data of the aneurysm-carrying artery.
The embodiment of the specification obtains the data of the central line of the aneurysm-carrying artery of the craniocerebral image data to be processed; screening abnormal points in the tumor-bearing artery central line data to obtain first central line data, wherein the first central line data is abnormal central line data; performing interpolation processing on the first central line data, and correcting the first central line data to obtain second central line data, wherein the correction of the first central line data comprises the following steps: correcting positions of abnormal points in the first central line data and radiuses corresponding to the abnormal points; and carrying out smoothing treatment on the second central line data to realize optimization of the central line data of the aneurysm-carrying artery, thereby improving the accuracy of calculation of a series of parameters based on the central line of the aneurysm-carrying artery, or the accuracy of the mesh grid display after the stent to be intervened is selected and released.
Drawings
In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some of the embodiments described in the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a method of centerline optimization provided by embodiments of the present disclosure;
FIG. 2 is a schematic diagram of yet another method of centerline optimization provided by embodiments of the present description;
FIG. 3 is a framework diagram of a centerline optimization provided by embodiments of the present description;
fig. 4 is a schematic diagram of a centerline optimization apparatus according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
Fig. 1 is a schematic diagram of a method for optimizing a center line according to an embodiment of the present disclosure, where the optimizing method includes:
step S101: and obtaining the tumor-bearing arterial centerline data of the craniocerebral image data to be processed.
In the embodiment of the present specification, the craniocerebral image data to be processed is any one of CTA (CT angiography ), MRA (magnetic resonance angiography, magnetic resonance angiography), DSA (Digital subtraction angiography ); the cranium brain image data to be processed can be two-dimensional image data or three-dimensional image data; the craniocerebral image data to be processed needs to be converted into DICOM format so as to be convenient for subsequent processing.
In the embodiment of the present specification, the obtaining of the data of the central line of the parent artery adopts the following method: and extracting blood vessel data from the image data to be processed by a threshold segmentation method, carrying out surface reconstruction on the extracted blood vessel data, and further segmenting the aneurysm to obtain the aneurysm-carrying central line data.
Step S103: and screening abnormal points in the data of the central line of the aneurysm-carrying artery to obtain first central line data, wherein the first central line data is abnormal central line data.
In the embodiment of the present disclosure, the method adopted for screening the first centerline data is: and screening abnormal central line data from the central line data of the aneurysm-carrying artery based on the variance as first central line data. In a specific implementation process, screening first central line data from the acquired central line data of the parent artery specifically includes: traversing each point on the data of the central line of the aneurysm-carrying artery, taking the advancing direction of the current point as a normal vector, calculating a tangent plane with a blood vessel, taking the point of the central line as a circle center, taking the point reaching the tangent plane as a radius, calculating the variance of the radius, and considering the current point as an abnormal point when the variance is larger than a preset value. In a specific implementation process, the preset value may be 0.5. It should be noted that the advancing direction is a direction along the direction of the parent artery, i.e. along the direction from the proximal release point to the distal release point.
Step S105: performing interpolation processing on the first central line data, and correcting the first central line data to obtain second central line data, wherein the correction of the first central line data comprises the following steps: correcting the positions of the abnormal points in the first central line data and the radius corresponding to the abnormal points.
In this embodiment of the present disclosure, the obtaining of the second centerline data specifically includes: and carrying out interpolation processing on normal points before and after the abnormal point in the first central line data along the advancing direction, thereby correcting the first central line data and obtaining second central line data. In the implementation process, the positions of the abnormal points and the radii corresponding to the abnormal points in the first central line data need to be corrected. The radius corresponding to the abnormal point is obtained by interpolation according to the radius or the diameter of the normal point before and after the abnormal point, and the correction of the position of the abnormal point is obtained by interpolation according to the position of the normal point before and after the abnormal point.
Step S107: and smoothing the second central line data to optimize the central line data of the aneurysm-carrying artery.
In the embodiment of the present disclosure, the smoothing process may use a Sinc smoothing function, or may use other smoothing methods, and the specific manner of smoothing is not limited to the present disclosure.
By adopting the optimization method of the central line of the carrying aneurysm artery, which is provided by the embodiment of the specification, abnormal points on the central line of the carrying aneurysm artery can be eliminated, and the accuracy of calculation of a series of parameters based on the central line of the carrying aneurysm artery, or the accuracy of the selection of the stent to be inserted and the display of the woven mesh after the stent is released can be improved.
FIG. 2 is a schematic diagram of yet another method of centerline optimization provided by an embodiment of the present disclosure, the optimization method comprising:
step S201: and screening abnormal points in the data of the central line of the aneurysm-carrying artery to obtain first central line data, wherein the first central line data is abnormal central line data.
Step S203: and carrying out interpolation processing on the first central line data, and correcting the first central line data to obtain second central line data.
Step S205: and homogenizing the second central line data to obtain third central line data.
The second center line data obtained by the correction processing in the foregoing step further requires a homogenization processing. Specifically, the third centerline data is obtained by homogenizing between every two points on the second centerline in a fixed-value step size. In a specific implementation, the step size of the fixed value is preferably 0.01.
Step S207: and diluting the third central line data to obtain fourth central line data, and taking the fourth central line data as new second central line data so as to carry out smoothing treatment on the new second central line data, thereby realizing optimization of the central line data of the tumor-bearing artery.
Step S209: and carrying out smoothing treatment on the new second central line data to realize optimization of the central line data of the aneurysm-carrying artery.
By adopting the optimization method of the central line of the carrying aneurysm provided by the embodiment of the specification, abnormal points on the central line of the carrying aneurysm can be eliminated, and the accuracy of calculation of a series of parameters based on the central line of the carrying aneurysm, or the accuracy of the selection of the stent to be inserted and the display of the woven mesh after the stent is released can be improved; and the center line data after homogenization and dilution can be used as a data graduated scale for subsequent research.
The data of the central line of the aneurysm-carrying artery optimized by the embodiment of the specification is further used for correcting the radius/diameter of the aneurysm-carrying artery.
Step S211: and acquiring the slope of the radius change based on the optimized parent artery centerline data and the parent artery centerline data, and carrying out radius correction on the parent artery to acquire the corrected radius.
In the present embodiment, the slope of the radius change= (radius of the current point-radius of the previous point)/radius of the previous point.
In the embodiment of the present specification, the corrected radius is obtained based on the cycle judgment. Specifically, the cycle is ended when the slope of the radius change is >0.1, and the corrected radius= (radius of the current point+radius of the previous point)/2.
In one embodiment of the present disclosure, the radius of each point on the centerline is defined as the diameter of each point on the centerline by the selected point P and its corresponding maximum radius P1, and by a vertical line perpendicular to the P1P vector.
For further understanding of the method for optimizing the center line provided in the embodiment of the present disclosure, the present disclosure further provides a frame diagram for optimizing the center line, as shown in fig. 3. FIG. 3 is a frame diagram for centerline optimization provided by an embodiment of the present description, the frame diagram comprising:
in one embodiment of the present disclosure, abnormal points are selected from the centerline data of the parent artery, the abnormal points are repaired, the centerline data is further homogenized, diluted, and finally smoothed, thereby optimizing the centerline data of the parent artery. The optimized data of the central line of the parent artery can be further used for radius correction of the parent artery to obtain corrected radius.
In one embodiment of the present disclosure, outliers are selected from the parent artery centerline data, outliers are repaired, and finally smoothing is performed to optimize the parent artery centerline data.
The foregoing details a method for centerline optimization, and accordingly, the present disclosure also provides a centerline optimization apparatus, as shown in fig. 4. FIG. 4 is a schematic diagram of a centerline optimization apparatus according to an embodiment of the present disclosure, the apparatus comprising:
the acquisition module 401 acquires the data of the central line of the aneurysm-carrying artery of the craniocerebral image data to be processed;
The screening module 403 screens abnormal points in the parent artery centerline data to obtain first centerline data, where the first centerline data is abnormal centerline data, and the correcting the first centerline data includes: correcting positions of abnormal points in the first central line data and radiuses corresponding to the abnormal points;
A correction module 405, configured to perform interpolation processing on the first centerline data, and correct the first centerline data to obtain second centerline data;
and the smoothing processing module 407 performs smoothing processing on the second central line data to optimize the central line data of the aneurysm-carrying artery.
Further, the interpolating processing is performed on the first centerline data, correcting the first centerline data to obtain second centerline data, and further includes:
homogenizing module 409, performing homogenization treatment on the second centerline data to obtain third centerline data;
The dilution module 411 dilutes the third centerline data to obtain fourth centerline data, and uses the fourth centerline data as new second centerline data, so as to smooth the new second centerline data, and optimize the centerline data of the parent artery.
Further, the screening the abnormal points in the parent artery centerline data to obtain first centerline data specifically includes:
traversing each point on the data of the aneurysm-carrying artery central line, calculating a tangent plane with a blood vessel by taking the advancing direction of the current point as a normal vector, taking the point of the central line as a circle center, taking the point reaching the tangent plane as a radius, calculating the variance of the radius, and considering the current point as an abnormal point when the variance is larger than a preset value.
Further, the interpolating processing on the first centerline data, correcting the first centerline data, and obtaining second centerline data, specifically includes:
And carrying out interpolation processing on normal points before and after the abnormal point in the first central line data along the advancing direction, thereby correcting the first central line data and obtaining the second central line data.
Further, the homogenizing the second centerline data to obtain third centerline data specifically includes:
homogenizing every two points on the second central line with a step length of a fixed value to obtain the third central line data.
Further, the diluting the third centerline data to obtain fourth centerline data specifically includes:
diluting the third central line data with N as a unit to obtain fourth central line data, wherein the fourth central line data can be used as a scale.
Further, the apparatus further comprises:
The radius correction module 413 obtains a slope of the radius change based on the optimized parent artery centerline data and the parent artery centerline data, and performs radius correction of the parent artery to obtain a corrected radius.
The embodiment of the specification also provides an electronic device, including:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
Acquiring tumor-bearing arterial centerline data of craniocerebral image data to be processed;
screening abnormal points in the tumor-bearing artery central line data to obtain first central line data, wherein the first central line data is abnormal central line data;
Performing interpolation processing on the first central line data, and correcting the first central line data to obtain second central line data, wherein the correction of the first central line data comprises the following steps: correcting positions of abnormal points in the first central line data and radiuses corresponding to the abnormal points;
And smoothing the second central line data to optimize the central line data of the aneurysm-carrying artery.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, electronic devices, non-volatile computer storage medium embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to the description of the method embodiments.
The apparatus, the electronic device, the nonvolatile computer storage medium and the method provided in the embodiments of the present disclosure correspond to each other, and therefore, the apparatus, the electronic device, the nonvolatile computer storage medium also have similar beneficial technical effects as those of the corresponding method, and since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, the electronic device, the nonvolatile computer storage medium are not described here again.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable GATE ARRAY, FPGA)) is an integrated circuit whose logic functions are determined by user programming of the device. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented with "logic compiler (logic compiler)" software, which is similar to the software compiler used in program development and writing, and the original code before being compiled is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but HDL is not just one, but a plurality of kinds, such as ABEL(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby Hardware Description Language), and VHDL (Very-High-SPEED INTEGRATED Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application SPECIFIC INTEGRATED Circuits (ASICs), programmable logic controllers, and embedded microcontrollers, examples of controllers include, but are not limited to, the following microcontrollers: ARC625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing one or more embodiments of the present description.
It will be appreciated by those skilled in the art that the present description may be provided as a method, system, or computer program product. Accordingly, the present specification embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description embodiments may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing description is by way of example only and is not intended as limiting the application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (11)
1. A method of centerline optimization, the method comprising:
Acquiring tumor-bearing arterial centerline data of craniocerebral image data to be processed;
screening abnormal points in the tumor-bearing artery central line data to obtain first central line data, wherein the first central line data is abnormal central line data;
Interpolation processing is performed on normal points before and after an abnormal point in the first central line data along the advancing direction, the first central line data is corrected, and second central line data is obtained, wherein the correction of the first central line data comprises the following steps: correcting positions of abnormal points in the first central line data and radiuses corresponding to the abnormal points; the radius corresponding to the abnormal point is obtained by interpolation according to the radius or the diameter of the normal point before and after the abnormal point, and the position of the abnormal point is obtained by interpolation according to the position of the normal point before and after the abnormal point;
smoothing the second central line data to optimize the central line data of the aneurysm-carrying artery;
The method further comprises:
Acquiring the slope of radius change based on the optimized parent artery centerline data and the parent artery centerline data, and carrying out radius correction on the parent artery to acquire corrected radius;
Wherein the corrected radius is obtained based on a cyclic judgment: the cycle is ended when the slope of the radius change >0.1, corrected radius= (radius of current point + radius of previous point)/2.
2. The method of claim 1, wherein interpolating the first centerline data, correcting the first centerline data, and obtaining second centerline data, further comprises:
Homogenizing the second central line data to obtain third central line data;
And diluting the third central line data to obtain fourth central line data, and taking the fourth central line data as new second central line data so as to carry out smoothing treatment on the new second central line data, thereby realizing optimization of the central line data of the tumor-bearing artery.
3. The method of claim 1, wherein said screening for outliers in said parent artery centerline data obtains first centerline data, comprising:
traversing each point on the data of the aneurysm-carrying artery central line, calculating a tangent plane with a blood vessel by taking the advancing direction of the current point as a normal vector, taking the point of the central line as a circle center, taking the point reaching the tangent plane as a radius, calculating the variance of the radius, and considering the current point as an abnormal point when the variance is larger than a preset value.
4. The method of claim 2, wherein homogenizing the second centerline data to obtain third centerline data, specifically comprises:
homogenizing every two points on the second central line with a step length of a fixed value to obtain the third central line data.
5. The method of claim 2, wherein diluting the third centerline data to obtain fourth centerline data, specifically comprises:
diluting the third central line data with N as a unit to obtain fourth central line data, wherein the fourth central line data can be used as a scale.
6. A centerline optimization apparatus, the apparatus comprising:
the acquisition module is used for acquiring the data of the central line of the aneurysm-carrying artery of the craniocerebral image data to be processed;
The screening module is used for screening abnormal points in the tumor-bearing arterial centerline data to obtain first centerline data, wherein the first centerline data is abnormal centerline data;
The correction module is used for carrying out interpolation processing on normal points before and after an abnormal point in the first central line data along the advancing direction, correcting the first central line data to obtain second central line data, wherein the correction of the first central line data comprises the following steps: correcting positions of abnormal points in the first central line data and radiuses corresponding to the abnormal points; the radius corresponding to the abnormal point is obtained by interpolation according to the radius or the diameter of the normal point before and after the abnormal point, and the position of the abnormal point is obtained by interpolation according to the position of the normal point before and after the abnormal point;
the smoothing processing module is used for carrying out smoothing processing on the second central line data to realize optimization of the central line data of the aneurysm-carrying artery;
The device further comprises:
The radius correction module is used for acquiring the slope of the radius change based on the optimized parent artery centerline data and the parent artery centerline data, and carrying out radius correction on the parent artery to acquire the corrected radius;
Wherein the corrected radius is obtained based on a cyclic judgment: the cycle is ended when the slope of the radius change >0.1, corrected radius= (radius of current point + radius of previous point)/2.
7. The apparatus of claim 6, wherein the interpolating the first centerline data, correcting the first centerline data, and obtaining second centerline data, further comprises:
The homogenizing module is used for homogenizing the second central line data to obtain third central line data;
And the dilution module is used for diluting the third central line data to obtain fourth central line data, and taking the fourth central line data as new second central line data so as to carry out smoothing treatment on the new second central line data and realize optimization of the central line data of the tumor-bearing artery.
8. The apparatus of claim 6, wherein said screening for outliers in said parent artery centerline data obtains first centerline data, comprising:
traversing each point on the data of the aneurysm-carrying artery central line, calculating a tangent plane with a blood vessel by taking the advancing direction of the current point as a normal vector, taking the point of the central line as a circle center, taking the point reaching the tangent plane as a radius, calculating the variance of the radius, and considering the current point as an abnormal point when the variance is larger than a preset value.
9. The apparatus of claim 7, wherein said homogenizing said second centerline data to obtain third centerline data, comprises:
homogenizing every two points on the second central line with a step length of a fixed value to obtain the third central line data.
10. The apparatus of claim 7, wherein the diluting the third centerline data to obtain fourth centerline data, specifically comprises:
diluting the third central line data with N as a unit to obtain fourth central line data, wherein the fourth central line data can be used as a scale.
11. An electronic device, comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
Acquiring tumor-bearing arterial centerline data of craniocerebral image data to be processed;
screening abnormal points in the tumor-bearing artery central line data to obtain first central line data, wherein the first central line data is abnormal central line data;
Interpolation processing is carried out on normal points before and after the abnormal point in the first central line data along the advancing direction, and the first central line data is corrected to obtain second central line data; wherein the correcting of the first centerline data includes: correcting positions of abnormal points in the first central line data and radiuses corresponding to the abnormal points; the radius corresponding to the abnormal point is obtained by interpolation according to the radius or the diameter of the normal point before and after the abnormal point, and the position of the abnormal point is obtained by interpolation according to the position of the normal point before and after the abnormal point;
smoothing the second central line data to optimize the central line data of the aneurysm-carrying artery;
Acquiring the slope of radius change based on the optimized parent artery centerline data and the parent artery centerline data, and carrying out radius correction on the parent artery to acquire corrected radius;
Wherein the corrected radius is obtained based on a cyclic judgment: the cycle is ended when the slope of the radius change >0.1, corrected radius= (radius of current point + radius of previous point)/2.
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