CN117372399A - Postoperative detection method, device and equipment for aortic dissection and storage medium - Google Patents
Postoperative detection method, device and equipment for aortic dissection and storage medium Download PDFInfo
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Abstract
The invention discloses a postoperative detection method, device and equipment for aortic dissection and a storage medium. The method comprises the following steps: acquiring images to be detected in a plurality of periods after aortic dissection operation; inputting the image to be detected into an aortic dissection model for prediction to obtain aortic dissection segmentation results in each period; and for any period, determining morphological data corresponding to the aortic dissection segmentation result based on the aortic dissection segmentation result and the voxels of the image to be detected. According to the invention, morphological data of each period can be compared and analyzed according to the time dimension, so that the change condition of the morphological data of each period in the time dimension is obtained, and the change conditions of different periods are simultaneously combined, thereby improving the accuracy of the aortic dissection postoperative evaluation.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for detecting aortic dissection after operation.
Background
The post aortic dissection re-diagnosis refers to that after the patient receives aortic dissection operation, the patient needs to regularly review and evaluate the treatment effect by a doctor. The purpose of the re-diagnosis is to monitor the patient's recovery and to discover and treat any signs of complications or recurrence in time.
Currently, the existing prognosis evaluation method for aortic dissection is to evaluate the morphological change of dissection from a single section, and the method loses more useful information and is difficult to combine the change conditions in different periods.
Disclosure of Invention
The invention provides a post-operation detection method, device and equipment for aortic dissection and a storage medium, which are used for improving the accuracy of post-operation prognosis evaluation of aortic dissection.
According to an aspect of the present invention, there is provided a method for post-operative detection of aortic dissection, comprising:
acquiring images to be detected in a plurality of periods after aortic dissection operation;
inputting the image to be detected into an aortic dissection model for prediction to obtain aortic dissection segmentation results in each period;
and for any period, determining morphological data corresponding to the aortic dissection segmentation result based on the aortic dissection segmentation result and the voxels of the image to be detected.
According to another aspect of the present invention, there is provided a post-operative detection apparatus for aortic dissection, comprising:
the to-be-detected image acquisition module is used for acquiring to-be-detected images of a plurality of periods after aortic dissection operation;
the aortic dissection segmentation module is used for inputting the image to be detected into an aortic dissection model for prediction to obtain aortic dissection segmentation results in each period;
and the morphological data determining module is used for determining morphological data corresponding to the aortic dissection segmentation result based on the aortic dissection segmentation result and the voxels of the image to be detected at any time.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method for post-operative detection of aortic dissection according to any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the method for post-operative detection of aortic dissection according to any embodiment of the present invention.
According to the technical scheme, images to be detected in multiple periods after aortic dissection operation are obtained; inputting the image to be detected into an aortic dissection model for prediction to obtain aortic dissection segmentation results in each period; and for any period, determining morphological data corresponding to the aortic dissection segmentation result based on the aortic dissection segmentation result and the voxels of the image to be detected. The morphological data of each period can be compared and analyzed according to the time dimension, so that the change condition of the morphological data of each period in the time dimension is obtained, and the change conditions of different periods are combined, thereby improving the accuracy of the aortic dissection postoperative evaluation.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for post-operative detection of aortic dissection according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for detecting aortic dissection after operation according to the second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a post-operation detection device for aortic dissection according to the third embodiment of the invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise 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.
Example 1
Fig. 1 is a flowchart of a method for detecting an aortic dissection after operation according to an embodiment of the present invention, where the method may be performed by a device for detecting an aortic dissection after operation, the device for detecting an aortic dissection after operation may be implemented in hardware and/or software, and the device for detecting an aortic dissection after operation may be configured in an aortic dissection detection apparatus. As shown in fig. 1, the method includes:
s110, obtaining images to be detected in a plurality of periods after aortic dissection operation.
The image to be detected refers to an inspection image of an aortic dissection postoperative operation site in different periods, specifically, the operation site comprises but is not limited to a head, a thoracic cavity and other sites, and the image to be detected comprises but is not limited to an echocardiography (computed tomography angiography, CTA) image of an aorta, magnetic resonance imaging and the like, and is not limited herein. In this embodiment, multiple rechecks are required after aortic dissection to evaluate the treatment effect, so that aortic images of the surgical site in different rechecking periods can be obtained as images to be detected.
S120, inputting the image to be detected into an aortic dissection model for prediction, and obtaining aortic dissection segmentation results in each period.
In the embodiment, for the images to be detected in each period, inputting the images to be detected into an aortic dissection segmentation model for predictive segmentation to obtain aortic dissection segmentation results in each period; the aortic dissection results comprise a true lumen, a false lumen and an aorta.
On the basis of the foregoing embodiment, optionally, inputting the image to be detected into an aortic dissection model for prediction, to obtain aortic dissection segmentation results in each period, includes: according to the time dimension, sequentially inputting the images to be detected into an aortic dissection segmentation model to predict, and respectively obtaining aortic dissection segmentation results in each period; or inputting the images to be detected in each period into the aortic dissection model for prediction to obtain aortic dissection results in each period.
In this embodiment, the images to be detected may be sequentially input to the aortic dissection segmentation model according to the time dimension of the images to be detected, so as to obtain aortic dissection segmentation results of different periods in the time dimension respectively; alternatively, all the images to be detected in each period may be input to the aortic dissection model to obtain aortic dissection results in each period, and aortic dissection results in each period in the time dimension may be obtained.
On the basis of the foregoing embodiment, optionally, the training method of the aortic dissection model includes: acquiring a training data set, wherein the training data set comprises a sample image of an aortic region after aortic dissection operation and aortic dissection annotation data; the following processes are iteratively executed until the training ending condition is met, and a trained aortic dissection model is obtained: inputting the sample image into an aortic dissection segmentation model to be trained to obtain a prediction result; determining a target loss based on the segmentation result and aortic dissection annotation data; and carrying out parameter adjustment on the aortic dissection model based on the target loss.
The training data set is a training sample set for training an aortic dissection segmentation model, specifically, the training data set comprises a large number of sample images of aortic regions after aortic dissection operation and aortic dissection labeling data, the sample images are aortic images, and the categories of the aortic dissection labeling data comprise a true cavity, a false cavity and a background.
The training process of the aortic dissection model is as follows:
1. acquiring a training data set;
2. inputting a sample image in the training data set into an aortic dissection model to be trained to obtain a prediction result;
3. calculating a target loss between the predicted result and aortic dissection annotation data based on the target loss function;
4. performing parameter adjustment on the aortic dissection model based on the target loss to update the model;
5. and (3) iteratively executing the step (2-4) until the training ending condition is met, and obtaining the aortic segmentation model after training.
Wherein the objective loss function includes a cross entropy loss and a Dice loss, and exemplary cross entropy loss functions are as follows:
dice＝-(1-P t ) γ log(P t )
order the
Wherein loss is focal Represents cross entropy loss, P t To predict probability, represent the proximity to the true annotation data, P t The larger the description the closer, y ε {0,1} is the sample tag and γ > 0 is the adjustable factor.
The Dice loss function is as follows:
loss dice ＝(2*TP)/(2*TP+FP+FN)
wherein loss is dice Indicating the Dice loss, TP indicating true positive, prediction being true, and true; TN represents true negative, the prediction is negative, and the prediction is true, and the actual case is negative; FP represents false positive, mispredicted, and actually negative; FN indicates false negative, mispredicted, positive example.
Loss total ＝α*loss focal +β*loss dice
Wherein, loss total Representing target loss, α and β representing weights for cross entropy loss and Dice loss, respectively; illustratively, α may be 0.7 and β may be 0.3.
Among them, the Network structure of the aortic dissection model includes, but is not limited to, a vector machine algorithm (Support Vector Machine, SVM), a Long Short-Term Memory (LSTM), a logistic regression model (Logistics Regression, LR), a full convolution Network (Fully Convolutional Networks, FCN), a cyclic convolution Network (Recurrent Neural Network, RNN), a Residual Network (ResNet), and the like, without limitation.
And S130, for any period, determining morphological data corresponding to the aortic dissection segmentation result based on the aortic dissection segmentation result and voxels of the image to be detected.
The morphology data refers to morphology data of each segmented region in the aortic dissection segmentation result, for example, the volume of the segmented region. In this embodiment, for the aortic dissection segmentation result at any time, the morphological data of each segmented region may be determined according to the aortic dissection segmentation result and the voxels of the image to be detected at that time.
On the basis of the above embodiment, optionally, the aortic dissection segmentation result includes the determining morphological data of each period based on the aortic dissection segmentation result and the voxel of the image to be detected, including: for the aortic dissection segmentation result in any period, multiplying the true lumen, the false lumen and the aorta with voxels of the image to be detected respectively to obtain morphological data of each segmentation area; wherein the morphology data comprises a true lumen volume, a false lumen volume, and an aortic volume.
In this embodiment, the aortic dissection segmentation result includes a true lumen, a false lumen and an aorta, and for the aortic dissection segmentation result in any period, the true lumen, the false lumen and the aorta can be respectively threaded with voxels of an image to be detected in the period to obtain a true lumen volume, a false lumen volume and an aortic volume in the period; thereby obtaining the vacuum cavity volume, the false cavity volume and the aortic volume in each period.
On the basis of the above embodiment, optionally, the method further includes: and carrying out time sequence on the morphological data of each period based on the time dimension, and carrying out data visualization based on the time sequence morphological data to obtain a first visualization chart.
The first visual chart refers to a visual chart of time change of morphological data, and specifically includes, but is not limited to, a line graph, a bar graph, a scatter graph, and the like, which are not limited herein. In this embodiment, the morphological data of each period may be time-sequenced according to the time dimension, and the time-sequenced morphological data may be data-visualized to form the first visualization chart. The transformation condition of the morphological data along with time can be intuitively obtained through the visual chart, and the accuracy of postoperative review evaluation is improved.
Taking a visualization chart as an example, the real cavity volume, the false cavity volume and the aorta in each period can be respectively time-sequenced to obtain the real cavity volume, the false cavity volume and the aorta volume in each period in the time dimension, and then the real cavity volume, the false cavity volume and the aorta volume in each period in the time dimension are subjected to data visualization to form a three-fold-line folding line chart, wherein the three fold-line folding lines respectively correspond to the volume change condition of the real cavity, the volume change condition of the false cavity and the volume change condition of the aorta.
According to the technical scheme, images to be detected in multiple periods after aortic dissection operation are obtained; inputting the image to be detected into an aortic dissection model for prediction to obtain aortic dissection segmentation results in each period; and for any period, determining morphological data corresponding to the aortic dissection segmentation result based on the aortic dissection segmentation result and the voxels of the image to be detected. The morphological data of each period can be compared and analyzed according to the time dimension, so that the change condition of the morphological data of each period in the time dimension is obtained, and the change conditions of different periods are combined, thereby improving the accuracy of the aortic dissection postoperative evaluation.
Example two
Fig. 2 is a flowchart of a method for detecting aortic dissection after operation according to the second embodiment of the present invention, which is a preferred embodiment provided on the basis of the above embodiment, and optionally, the method further includes: respectively constructing a geometric model of a segmentation area based on the aortic dissection segmentation results of each period; and for any geometric model, meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be detected and the clinical information, and performing hydrodynamic simulation based on the boundary conditions to obtain hydrodynamic simulation results of corresponding periods. Wherein, the explanation of the same or corresponding terms as the above embodiments is not repeated herein. As shown in fig. 2, the method includes:
s210, obtaining images to be detected in a plurality of periods after aortic dissection operation.
S220, inputting the image to be detected into an aortic dissection model for prediction, and obtaining aortic dissection segmentation results in each period.
And S230, for any period, determining morphological data corresponding to the aortic dissection segmentation result based on the aortic dissection segmentation result and voxels of the image to be detected.
S240, respectively constructing geometric models of the segmented regions based on the aortic dissection segmentation results of each period; and for any geometric model, meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be detected and the clinical information, and performing hydrodynamic simulation based on the boundary conditions to obtain hydrodynamic simulation results of corresponding periods.
In this embodiment, since the aortic dissection segmentation result includes a true lumen, a false lumen and an aorta, and the geometric models of the segmentation areas are respectively constructed based on the aortic dissection segmentation result in each period to respectively construct corresponding three-dimensional geometric models, it can be understood that the true lumen three-dimensional geometric model, the false lumen three-dimensional geometric model and the aortic three-dimensional geometric model in each period are constructed. For any three-dimensional geometric model, dividing the aortic dissection result corresponding to the three-dimensional geometric model into a structured grid and/or an unstructured grid according to grid division parameters, wherein the grid division parameters comprise grid division size, grid quality index and optimization iteration times.
In the embodiment, the clinical information and the image to be detected are subjected to feature analysis to obtain the inlet flow of the geometric model, and all the outlet flows are determined based on the boundary condition model and the inlet flow; determining an inlet velocity based on the cross-sectional area or cross-sectional radius of the aorta in combination with the inlet flow; determining all outlet velocities based on the branch size of the aortic branch in combination with all outlet flows; and further determining boundary conditions of the inlet of the geometric model according to the inlet flow and/or the inlet speed and the blood pressure value, and determining boundary conditions of the outlet of the geometric model according to the outlet flow and/or the outlet speed and the blood pressure value. The clinical information comprises, but is not limited to, information such as aortic flow rate at chest and abdomen, blood pressure value, aortic and ultrasonic flow rate of each branch; the blood pressure value may be calculated from the systolic and diastolic blood pressure.
In this embodiment, the method for fluid dynamics simulation may be: and carrying out hydrodynamic simulation on the aortic dissection result based on the set boundary conditions and combining with a conservation law to obtain a hydrodynamic simulation result, wherein the conservation law comprises an energy conservation law, a mass conservation law and the like. In this embodiment, a low-order coupling model of a blood vessel may be obtained, the low-order coupling model is used as a boundary condition of the geometric model, and the aortic dissection segmentation result is subjected to hydrodynamic simulation by using the combination conservation law, so as to obtain a hydrodynamic simulation result. The fluid dynamics simulation results include, but are not limited to, blood flow pressure difference, fluid velocity field, fluid flow, etc., which are not limited herein.
On the basis of the above embodiment, optionally, the method further includes: and carrying out time sequence on the aortic hydrodynamic simulation results in each period based on the time dimension, and carrying out data visualization based on the time sequence of the aortic hydrodynamic simulation results to obtain a second visualization chart.
The second visual icon refers to a visual chart of the fluid dynamics simulation result changing along with time, and specifically, the second visual chart includes, but is not limited to, a line graph, a bar graph, a scatter graph, and the like, which are not limited herein. In this embodiment, the fluid dynamics simulation results of each period may be time-sequenced according to the time dimension, and the time-sequenced fluid dynamics simulation results may be data-visualized to form a second visualization chart. The change condition of the fluid dynamics simulation result along with time can be intuitively obtained through the visual chart, so that the accuracy and the evaluation efficiency of postoperative review evaluation are improved.
According to the technical scheme, images to be detected in multiple periods after aortic dissection operation are obtained; inputting the image to be detected into an aortic dissection model for prediction to obtain aortic dissection segmentation results in each period; for any period, determining morphological data corresponding to the aortic dissection segmentation result based on the aortic dissection segmentation result and voxels of the image to be detected; respectively constructing a geometric model of the segmentation area based on the aortic dissection segmentation results of each period; and for any geometric model, meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be detected and the clinical information, and performing hydrodynamic simulation based on the boundary conditions to obtain hydrodynamic simulation results of corresponding periods. The morphological data and the fluid dynamics simulation results of each period can be compared and analyzed according to the time dimension, so that the change condition of the morphological data and the fluid dynamics simulation results under the time dimension is obtained, the change conditions of different periods are combined, and the accuracy of the aortic dissection postoperative evaluation is further improved.
Example III
Fig. 3 is a schematic structural diagram of a post-operation detection device for aortic dissection according to the third embodiment of the invention. As shown in fig. 3, the apparatus includes:
the to-be-detected image acquisition module 310 is configured to acquire to-be-detected images of a plurality of periods after aortic dissection;
the aortic dissection segmentation module 320 is configured to input the image to be detected into an aortic dissection model for prediction, so as to obtain aortic dissection segmentation results in each period;
the morphological data determining module 330 is configured to determine morphological data corresponding to the aortic dissection segmentation result based on the aortic dissection segmentation result and voxels of the image to be detected for any period.
According to the technical scheme, images to be detected in multiple periods after aortic dissection operation are obtained; inputting the image to be detected into an aortic dissection model for prediction to obtain aortic dissection segmentation results in each period; and for any period, determining morphological data corresponding to the aortic dissection segmentation result based on the aortic dissection segmentation result and the voxels of the image to be detected. The morphological data of each period can be compared and analyzed according to the time dimension, so that the change condition of the morphological data of each period in the time dimension is obtained, and the change conditions of different periods are combined, thereby improving the accuracy of the aortic dissection postoperative evaluation.
On the basis of the above embodiment, optionally, the aortic dissection segmentation module 320 is configured to sequentially input the image to be detected into an aortic dissection segmentation model according to a time dimension to predict, so as to obtain aortic dissection segmentation results in each period respectively; or inputting the images to be detected in each period into the aortic dissection model for prediction to obtain aortic dissection results in each period.
On the basis of the above embodiment, optionally, the aortic dissection results include a true lumen, a false lumen and an aorta; the morphological data determining module 330 is configured to multiply the true lumen, the false lumen and the aorta with voxels of the image to be detected, respectively, to obtain morphological data of each segmented region for an aortic dissection segmentation result at any period; wherein the morphology data comprises a true lumen volume, a false lumen volume, and an aortic volume.
Based on the foregoing embodiment, optionally, the apparatus further includes a first visualization chart determining module, configured to time-sequence the morphological data of each period based on the time dimension, and perform data visualization based on the time-sequence morphological data, to obtain a first visualization chart.
On the basis of the above embodiment, optionally, the apparatus further includes a fluid dynamics simulation module, configured to construct a geometric model of the segmented region based on the aortic dissection segmentation results of each period; and for any geometric model, meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be detected and the clinical information, and performing hydrodynamic simulation based on the boundary conditions to obtain hydrodynamic simulation results of corresponding periods.
Based on the foregoing embodiment, optionally, the apparatus further includes a second visualization chart determining module, configured to time-sequence the aortic hydrodynamic simulation results in each period based on a time dimension, and perform data visualization based on the time-sequence aortic hydrodynamic simulation results, to obtain a second visualization chart.
Based on the above embodiment, optionally, the apparatus further includes an aortic dissection segmentation model training module, configured to obtain a training data set, where the training data set includes a sample image of an aortic dissection post-operation aortic region and aortic dissection annotation data; the following processes are iteratively executed until the training ending condition is met, and a trained aortic dissection model is obtained: inputting the sample image into an aortic dissection segmentation model to be trained to obtain a prediction result; determining a target loss based on the prediction result and aortic dissection annotation data; and carrying out parameter adjustment on the aortic dissection model based on the target loss.
The device for detecting the aortic dissection after operation provided by the embodiment of the invention can execute the method for detecting the aortic dissection after operation provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM12 and the RAM13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a post-operative detection method of aortic dissection.
In some embodiments, the post-operative detection method of aortic dissection may be implemented as a computer program, which is tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM12 and/or the communication unit 19. When the computer program is loaded into RAM13 and executed by processor 11, one or more steps of the above-described method of post-operative detection of aortic dissection may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform a post-operative detection method of aortic dissection by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
The computer program for implementing the aortic dissection post-operative detection method of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
Example five
The fifth embodiment of the present invention further provides a computer readable storage medium, where computer instructions are stored, where the computer instructions are configured to cause a processor to perform a method for detecting aortic dissection after surgery, the method including:
acquiring images to be detected in a plurality of periods after aortic dissection operation; inputting an image to be detected into an aortic dissection model for prediction to obtain aortic dissection segmentation results in each period; for any period, determining morphological data corresponding to the aortic dissection segmentation result based on the aortic dissection segmentation result and voxels of the image to be detected.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for post-operative detection of aortic dissection, comprising:
acquiring images to be detected in a plurality of periods after aortic dissection operation;
inputting the image to be detected into an aortic dissection model for prediction to obtain aortic dissection segmentation results in each period;
and for any period, determining morphological data corresponding to the aortic dissection segmentation result based on the aortic dissection segmentation result and the voxels of the image to be detected.
2. The method according to claim 1, wherein the inputting the image to be detected into the aortic dissection model for prediction, to obtain aortic dissection results of each period, includes:
according to the time dimension, sequentially inputting the images to be detected into an aortic dissection segmentation model to predict, and respectively obtaining aortic dissection segmentation results in each period;
or inputting the images to be detected in each period into the aortic dissection model for prediction to obtain aortic dissection results in each period.
3. The method of claim 1 or 2, wherein the aortic dissection results comprise a true lumen, a false lumen and an aorta; the aortic dissection segmentation result comprises morphological data of each period determined based on the aortic dissection segmentation result and voxels of the image to be detected, and the method comprises the following steps:
for the aortic dissection segmentation result in any period, multiplying the true lumen, the false lumen and the aorta with voxels of the image to be detected respectively to obtain morphological data of each segmentation area; wherein the morphology data comprises a true lumen volume, a false lumen volume, and an aortic volume.
4. The method according to claim 1, wherein the method further comprises:
and carrying out time sequence on the morphological data of each period based on the time dimension, and carrying out data visualization based on the time sequence morphological data to obtain a first visualization chart.
5. The method according to claim 1, wherein the method further comprises:
respectively constructing a geometric model of a segmentation area based on the aortic dissection segmentation results of each period;
and for any geometric model, meshing the geometric model, determining an inlet and at least one outlet of the geometric model, determining boundary conditions of the inlet and the at least one outlet of the geometric model according to the image to be detected and the clinical information, and performing hydrodynamic simulation based on the boundary conditions to obtain hydrodynamic simulation results of corresponding periods.
6. The method of claim 5, wherein the method further comprises:
and carrying out time sequence on the aortic hydrodynamic simulation results in each period based on the time dimension, and carrying out data visualization based on the time sequence of the aortic hydrodynamic simulation results to obtain a second visualization chart.
7. The method of claim 1, wherein the training method of the aortic dissection model comprises:
acquiring a training data set, wherein the training data set comprises a sample image of an aortic region after aortic dissection operation and aortic dissection annotation data;
the following processes are iteratively executed until the training ending condition is met, and a trained aortic dissection model is obtained: inputting the sample image into an aortic dissection segmentation model to be trained to obtain a prediction result; determining a target loss based on the prediction result and aortic dissection annotation data; and carrying out parameter adjustment on the aortic dissection model based on the target loss.
8. A post-operative detection device for aortic dissection, comprising:
the to-be-detected image acquisition module is used for acquiring to-be-detected images of a plurality of periods after aortic dissection operation;
the aortic dissection segmentation module is used for inputting the image to be detected into an aortic dissection model for prediction to obtain aortic dissection segmentation results in each period;
and the morphological data determining module is used for determining morphological data corresponding to the aortic dissection segmentation result based on the aortic dissection segmentation result and the voxels of the image to be detected at any time.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of post-operative detection of aortic dissection of any of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the method of post-operative detection of aortic dissection according to any one of claims 1 to 7.
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