Method, device, computer-readable medium and electronic device for determining lesion boundary

Abstract

The present application discloses a method, device, computer-readable medium and electronic device for determining a lesion boundary, the method comprising: obtaining raw data corresponding to pixel points in a preset area; the raw data is high-frame rate contrast imaging data that is not affected by user post-processing adjustments; calculating a time intensity curve for the raw data to obtain a time intensity curve corresponding to each pixel point, and since the time resolution of the raw data is high, the time intensity curve determined by the present application is closer to the actual perfusion scene; then, based on the time intensity curve corresponding to the pixel points at multiple depths, characteristic parameters corresponding to the pixel points at multiple depths are extracted; statistical analysis is performed on the characteristic parameters to obtain the lesion boundary of the preset area; on the premise that the raw data is more accurate and real-time, the accuracy and real-time performance of the time intensity curve and characteristic parameters corresponding to the pixel points at each depth are also improved, thereby improving the accuracy of the lesion boundary and realizing real-time evaluation during surgery.

Description

Technical Field

[0001] The present invention belongs to the technical field of angiography data processing, and in particular, relates to a method, device, computer-readable medium and electronic device for determining lesion boundaries. Background Art

[0002] At present, it is a common method to determine the scope of the lesion through preoperative images. Most of them are based on the subjective will of the surgeon to define the scope, and the surgical resection area or ablation area is planned based on preoperative images, or the treatment area is planned based on three-dimensional reconstruction of the image data collected before the operation. However, the data of this method is not real-time, and the preoperative images cannot accurately reflect the patient's condition during the operation. It is easy to have errors and omissions, resulting in residual lesions and recurrence of the disease.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0004] The purpose of the present invention is to provide a method, device, computer-readable medium and electronic device for determining lesion boundaries, so as to solve the problem of inaccurate lesion boundaries in related technologies.

[0005] According to one aspect of an embodiment of the present application, a method for determining a lesion boundary is provided, comprising:

[0006] Obtaining raw data corresponding to pixel points in a preset area; the raw data is high frame rate contrast imaging data that is not affected by user post-processing adjustments;

[0007] Calculate the time intensity curve of the original data to obtain the time intensity curve corresponding to the pixel point;

[0008] Extracting characteristic parameters corresponding to the pixel points at multiple depths according to the time intensity curves corresponding to the pixel points at multiple depths;

[0009] The characteristic parameters are statistically analyzed to obtain the lesion boundary of the preset area.

[0010] In one embodiment of the present application, a statistical analysis is performed on the characteristic parameters to obtain a lesion boundary of a preset area, including: forming a characteristic parameter curve of the pixel points corresponding to the depth based on the characteristic parameters of the pixel points at multiple depths; calculating the inflection point of the characteristic parameter curve at the current depth; using the pixel point corresponding to the inflection point as the boundary pixel point; connecting the boundary pixel points at multiple depths to obtain the lesion boundary.

[0011] In one embodiment of the present application, a statistical analysis is performed on the characteristic parameters to obtain a lesion boundary of a preset area, including: obtaining a preset threshold corresponding to the characteristic parameter; comparing the characteristic parameter of the pixel point at the current depth with the preset threshold to determine the tissue type of each pixel point; determining the boundary pixel point at the current depth according to the tissue type of each pixel point; and connecting the boundary pixel points at multiple depths to obtain the lesion boundary.

[0012] In one embodiment of the present application, the characteristic parameters include at least one of the following parameters: arrival time, peak time, rise time, peak intensity, rise slope, mean transit time, 50% peak time, decline slope, decline time, area under the perfusion curve, area under the decay curve, area under the curve, perfusion rate index and decay rate index.

[0013] In one embodiment of the present application, the perfusion rate index is the ratio of the area under the perfusion curve to the rise time.

[0014] In one embodiment of the present application, the decay rate index is the ratio of the area under the decay curve to the fall time.

[0015] In one embodiment of the present application, after performing statistical analysis on the characteristic parameters to obtain the lesion boundary of the preset area, the method further includes: adjusting the lesion boundary to obtain a safe boundary of the treatment area.

[0016] According to one aspect of an embodiment of the present application, a device for determining a lesion boundary is provided, the device comprising:

[0017] An acquisition module is used to acquire raw data corresponding to pixel points in a preset area; the raw data is high frame rate contrast imaging data that is not affected by user post-processing adjustments;

[0018] The curve calculation module is used to calculate the time intensity curve of the original data to obtain the time intensity curve corresponding to the pixel point;

[0019] A parameter extraction module, used to extract characteristic parameters corresponding to the pixel points at multiple depths according to the time intensity curves corresponding to the pixel points at multiple depths;

[0020] The boundary determination module is used to perform statistical analysis on the characteristic parameters to obtain the lesion boundary of the preset area.

[0021] According to one aspect of an embodiment of the present application, a computer medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for determining the lesion boundary provided in any embodiment of the present application is implemented.

[0022] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: a processor; a memory for storing executable instructions of the processor; the processor executes the executable instructions to enable the electronic device to implement the method for determining the lesion boundary provided in any embodiment of the present application.

[0023] In the technical solution of the present application, the original data corresponding to the pixel points in the preset area are obtained; the original data are high-frame rate contrast imaging data that are not affected by user post-processing adjustments; the time-intensity curve is calculated for the original data to obtain the time-intensity curve corresponding to each pixel point. Since the time resolution of the original data is high, the time-intensity curve determined by the present application is closer to the actual perfusion scene; then, based on the time-intensity curve corresponding to the pixel points at multiple depths, the characteristic parameters corresponding to the pixel points at multiple depths are extracted; the characteristic parameters are statistically analyzed to obtain the lesion boundary of the preset area; on the premise that the original data is more accurate and real-time, the accuracy and real-time performance of the time-intensity curve and characteristic parameters corresponding to the pixel points at each depth are also improved, thereby improving the accuracy of the lesion boundary and realizing real-time evaluation during surgery.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 The flowchart of the method for determining the lesion boundary provided in one embodiment of the present application is schematically shown.

[0027] Figure 2 Schematic diagram of the time intensity curves of different tissues is shown schematically.

[0028] Figure 3 A schematic diagram of a time intensity curve in an embodiment of the present application is schematically shown.

[0029] Figure 4 The schematic diagram schematically shows a rising slope absolute value curve of a pixel point at a certain depth in an embodiment of the present application.

[0030] Figure 5 The schematic diagram schematically shows a rising slope absolute value curve of a pixel point at a certain depth in an embodiment of the present application.

[0031] Figure 6 The schematic diagram schematically shows a rising slope absolute value curve of a pixel point at a certain depth in an embodiment of the present application.

[0032] Figure 7 A schematic diagram of an arrival time curve of a pixel point at a certain depth in an embodiment of the present application is schematically shown.

[0033] Figure 8 A schematic diagram of the lesion boundary in an embodiment of the present application is schematically shown.

[0034] Fig. 9 The structural block diagram of a device for determining a lesion boundary provided in an embodiment of the present application is schematically shown.

[0035] Fig.10 The structural block diagram of an electronic device provided by an embodiment of the present application is schematically shown.

[0036] Fig.11 The structure block diagram of a computer system for implementing an electronic device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0037] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0038] In addition, the features, structures or characteristics described in the present application may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the present application.

[0039] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0040] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0041] When performing refined anatomical resection or ablation treatment, it is very important to plan the treatment boundary of the lesion before and during the operation. Most treatment options hope to remove the tumor completely while retaining as much body organ substance as possible. More remaining organ volume can allow patients to recover their body functions better and faster. Traditional CT (computed tomography) or MRI (magnetic resonance imaging) cannot be evaluated immediately after surgery due to their timeliness and objective factors. Researchers have found that the difference between tumor cells and normal tissue cells is that tumor cells have a large number of new blood vessels inside, so they need more arterial blood supply than normal tissues. Ultrasound contrast-enhanced ultrasound (CEUS) imaging is through the flow of ultrasound contrast agents in the blood, by receiving reflection signals that are much higher than normal tissues, thereby highlighting abnormal tumor cells with new blood vessels. In addition, ultrasound contrast imaging has the characteristics of real-time, repeatable, easy operation and not affected by the environment. It is very suitable for determining the treatment boundary before and during surgery, especially in ultrasound-guided ablation therapy, which can monitor the ablation process in real time with contrast imaging. However, the contrast imaging frame rate of commercial ultrasound imaging systems is basically around 10 Hz, which cannot truly display the perfusion characteristics of rich blood supply lesions, and the determined lesion boundaries are also far from the actual situation. Figure 1 As shown, the present application provides a method for determining a lesion boundary including S110 to S140 to solve the problem of inaccurate lesion range determined in the related art.

[0042] S110, obtaining original data corresponding to pixel points in a preset area; the original data is high frame rate contrast imaging data that is not affected by user post-processing adjustments.

[0043] Specifically, the preset area can be the range of the lesion automatically identified or set by the user. The range of the preset area can be greater than or equal to the range of the lesion, and the contrast image of the preset area can be displayed on the screen to facilitate the operator to observe the lesion. Pixels refer to the basic display elements of the display screen that displays the preset area. It should be understood that in the related art, the user will adjust the image parameters in real time according to the perfusion of the lesion, such as gain, TGC (time gain compensation), etc. to improve the display of the lesion during actual operation. However, although this can better display the lesion, it will also affect the results of the subsequent analysis of the contrast film data, resulting in the perfusion result not being the original. In addition, the frame rate of the high frame rate image in the present application is greater than 100Hz. For example, the present application can transmit a plane wave signal to the preset area, and then receive the echo returned from the preset area. After processing the echo, the high frame rate contrast raw data can be obtained. The high frame rate contrast raw data can more realistically restore the actual perfusion situation and improve the overall accuracy.

[0044] S120, calculating the time intensity curve of the original data to obtain the time intensity curve corresponding to the pixel point.

[0045] Specifically, the time intensity curve (TIC) is a curve that reflects the corresponding relationship between image signal intensity and time. Figure 2 As shown, the time intensity curve corresponding to the tumor tissue is different from the time intensity curve of the normal tissue. In the time period of 0 to 25.7s, the signal intensity reflected by the tumor tissue is generally higher than the signal intensity reflected by the normal tissue, and in the time period of about 7 to 12.9s, the reflected signal intensity of the tumor tissue rises rapidly to a peak value of 49.3, and then slowly decreases. Compared with the rapid rise of the reflected signal intensity of the tumor tissue in the early stage, the reflected signal intensity of the normal tissue changes gently in the early stage, and after reaching the peak value of 38.5, it does not slowly decrease, but fluctuates in a small range around the peak value. In the related art, since the data for constructing the time intensity curve is low-frequency angiography data, and may even be adjusted by display such as gain or time gain compensation, the error between the formed time intensity curve and the actual time intensity curve is large, and it can only roughly reflect the real-time lesions of the patient, and the accuracy is low. The frequency of the original data of the present application is at least 10 times greater than that in the related art, and the present application collects more data points in the same time. The corresponding relationship between a large number of time and signal intensity is expressed in a rectangular coordinate system, and a nearly continuous time intensity curve can be obtained.

[0046] In another embodiment, the corresponding relationship between time and signal strength forms dense discrete points in a rectangular coordinate system, and the discrete points are fitted by a curve fitting technique to obtain the following: Figure 3 The time intensity curve is shown.

[0047] S130 . Extracting characteristic parameters corresponding to the pixel points at multiple depths according to the time intensity curves corresponding to the pixel points at multiple depths.

[0048] Specifically, characteristic parameters are parameters used to describe the characteristics of the time intensity curve, such as signal intensity peak value, time for signal intensity to increase from an initial value to a peak value, time for signal intensity to decrease from a peak value to a stable value, or signal intensity rise rate. Figure 4 The figure shows the correspondence between pixels and feature parameters in a certain scene. The position indicated by the arrow represents the current depth. Assuming that the top of the contrast image is depth 0, and the pixel point where the straight line is located is located at the 100th row below the top of the image, the position of the straight line is depth 100, that is, the depth of the currently acquired image can be determined based on the position of the pixel point corresponding to the straight line in the image. If the contrast image contains X rows of pixels, each row contains N pixels, the time intensity curves of the N pixels at each depth are obtained in turn to determine the feature parameters corresponding to each pixel at the depth; Figure 5 and Figure 6 As shown, feature parameters of N pixels at another depth are obtained, and after traversing X depths, feature parameters of pixels at X depths can be obtained, that is, feature parameters of (X×N) pixels.

[0049] In one embodiment of the present application, the characteristic parameters include at least one of the following parameters: arrival time, peak time, rise time, peak intensity, rise slope, mean transit time, 50% peak time, decline slope, decline time, area under the perfusion curve, area under the decay curve, area under the curve, perfusion rate index and decay rate index.

[0050] Specifically, Figure 3As shown, the arrival time (AT) refers to the time when the contrast agent initially arrives. The time to peak (TTP) refers to the moment when the signal intensity reaches the maximum. The rise time (RT) refers to the time from the beginning of the signal intensity increase to the maximum intensity. The peak intensity (PI) refers to the maximum value of the signal intensity. The wash in rate (WiR) refers to the maximum value of the slope in the rising phase. The mean transit time (mTT) refers to the average time for the contrast agent to pass. The 50% peak time (Time for Full Width Half Max, FWHM) refers to the time between the rise reaching half the peak value (0.5PI) and the fall reaching half the peak value (0.5PI). The fall slope (WoR) refers to the minimum value of the slope in the fall phase. The fall time (FT) refers to the fall time. The area under the perfusion curve (WiAUC) refers to the area under the curve from the beginning of the contrast agent to the moment when the peak PI is reached. The area under the wash out curve (WoAUC) refers to the area under the curve of the contrast agent from the peak moment to the decline time. The area under the curve (Wash in and Wash out Area Under Curve, WiWoAUC) refers to the sum of the area under the perfusion curve (WiAUC) and the area under the wash out curve (WoAUC). The perfusion rate index is the ratio of the area under the perfusion curve (WiAUC) to the rise time (RT). The wash out rate index is the ratio of the area under the wash out curve (WoAUC) to the decline time (FT). New parameters are obtained by associating multiple parameters, such as the perfusion rate index and the wash out rate index proposed in the present application. By associating the area under the perfusion curve with the rise time, or associating the area under the wash out curve with the decline time, a new parameter can be used to associate multiple characteristics of the time intensity curve, so as to combine the various characteristics of the time intensity curve to confirm the boundary, thereby improving the accuracy of the lesion boundary.

[0051] In another embodiment, there may be multiple characteristic parameters. When extracting characteristic parameters corresponding to pixel points at multiple depths based on time-intensity curves corresponding to pixel points at multiple depths, multiple characteristic parameters of the time-intensity curve may be extracted for each depth.

[0052] Specifically, Figure 5 This is a schematic diagram of the corresponding relationship between the pixel points at depth 150 and the absolute value of the rising slope. Figure 7It is a schematic diagram of the correspondence between the pixel points at depth 150 and the arrival time. The characteristic parameters may include the absolute value of the rising slope and the arrival time, or any of the above characteristic parameters. For the pixel points at the same depth, multiple characteristic parameters corresponding to the pixel points may be extracted so that the lesion boundary may be determined by combining multiple characteristic parameters at the same time.

[0053] S140, performing statistical analysis on the characteristic parameters to obtain the lesion boundary of the preset area.

[0054] Specifically, as mentioned above, the reflection capabilities of tumor tissue and normal tissue to signals are different, which will form different time intensity curves, so the characteristic parameters of tumor tissue and normal tissue are also different. For example, the arrival time of tumor cells is earlier than the arrival time of surrounding normal cells, the peak time of tumor cells is earlier than the peak time of surrounding normal cells, the rise time of tumor cells is shorter than the rise time of surrounding normal cells, the peak intensity of tumor cells is greater than the peak intensity of surrounding normal cells, the rising slope of tumor cells is greater than the rising slope of surrounding normal cells, the transit time of tumor cells is shorter than the transit time of surrounding normal cells, the area under perfusion of tumor cells is smaller than the area under perfusion of surrounding normal cells, the descending slope of tumor cells is smaller than the descending slope of surrounding normal cells, the descending time of tumor cells is shorter than the descending time of surrounding normal cells, and the area under the curve of tumor cells is smaller than the area under the curve of surrounding normal cells. It can be seen that for different characteristic parameters, tumor tissue and normal tissue have corresponding differentiated characteristics. Therefore, the tissue type represented by the pixel can be determined based on the difference between the two. For example, the characteristic parameters of the pixels at the same depth can be compared. For example, if the arrival time of pixel A is earlier than that of pixel B, then pixel A belongs to the tumor tissue area, and pixel B is normal tissue. By traversing multiple depths of the contrast image, multiple boundary pixels are statistically obtained, and these boundary pixels are connected to obtain the lesion boundary of the preset area. Figure 8 As shown, the left side is an ultrasound image of a preset area, and the white circle on the right side indicates the lesion boundary corresponding to the ultrasound image on the left side. It should be understood that the boundary in the schematic diagram is only an example, and does not mean that the lesion boundary in the actual scene is a regular circle or other shape, and can also be an irregular shape.

[0055] In the technical solution of the present application, the original data corresponding to the pixel points in the preset area are obtained; the original data are high-frame rate contrast imaging data that are not affected by user post-processing adjustments; the time-intensity curve is calculated for the original data to obtain the time-intensity curve corresponding to each pixel point. Since the time resolution of the original data is high, the time-intensity curve determined by the present application is closer to the actual perfusion scene; then, based on the time-intensity curve corresponding to the pixel points at multiple depths, the characteristic parameters corresponding to the pixel points at multiple depths are extracted; the characteristic parameters are statistically analyzed to obtain the lesion boundary of the preset area; on the premise that the original data is more accurate and real-time, the accuracy and real-time performance of the time-intensity curve and characteristic parameters corresponding to the pixel points at each depth are also improved, thereby improving the accuracy of the lesion boundary and realizing real-time evaluation during surgery.

[0056] In one embodiment of the present application, a statistical analysis is performed on the characteristic parameters to obtain a lesion boundary of a preset area, including: forming a characteristic parameter curve of the pixel points corresponding to the depth based on the characteristic parameters of the pixel points at multiple depths; calculating the inflection point of the characteristic parameter curve at the current depth; using the pixel point corresponding to the inflection point as the boundary pixel point; connecting the boundary pixel points at multiple depths to obtain the lesion boundary.

[0057] Specifically, there are N pixels at each depth, and these N pixels correspond to N time intensity curves. Assume that the characteristic parameter corresponding to the time intensity curve of pixel A at depth 3 is the rising slope a, and then the rising slopes corresponding to other pixels are obtained in turn, and N rising slopes can be obtained, such as Figure 5 As shown, the correspondence between the absolute values ​​of the N rising slopes and the pixel positions is expressed in a coordinate system, wherein the horizontal axis is the pixel position and the vertical axis is the absolute value of the rising slope, so that a characteristic parameter curve of the current depth pixel can be formed, and then the left inflection point and the right inflection point of the characteristic parameter curve are calculated. The left and right boundaries of the current depth can be determined by obtaining the pixel sequence numbers corresponding to the left inflection point and the right inflection point. After traversing multiple depths, multiple boundary pixel points can be determined. Since the temporal frequency of the original data in this application is relatively high, the characteristic parameters of the pixel points determined based on the original data are also more accurate, so the determined boundary pixel points are also more accurate. The boundary pixel points are connected to obtain the lesion boundary. In addition, the method of connecting the boundary pixel points to obtain the boundary line is not limited to the method shown in this application, and this application does not make specific restrictions on this.

[0058] In one embodiment of the present application, the pixel points corresponding to the inflection points are used as boundary pixel points, including: determining the candidate pixel points corresponding to the inflection points of each characteristic parameter curve; and selecting the candidate pixel points with a high number of overlaps as boundary pixel points.

[0059] Specifically, assuming that the characteristic parameters are the absolute value of the rising slope, the arrival time, and the signal peak value, the candidate boundary point P of the boundary is determined according to the curve of the absolute value of the rising slope, the candidate boundary point Q of the boundary is determined according to the curve of the arrival time, and the candidate boundary point P of the boundary is determined according to the curve of the signal peak value. Since the candidate boundary point P is repeated twice, it means that the possibility that the candidate boundary point P is the real boundary point is higher, so the candidate boundary point P is determined as the boundary point.

[0060] In one embodiment of the present application, a statistical analysis is performed on the characteristic parameters to obtain a lesion boundary of a preset area, including: obtaining a preset threshold corresponding to the characteristic parameter; comparing the characteristic parameter of the pixel point at the current depth with the preset threshold to determine the tissue type of each pixel point; determining the boundary pixel point at the current depth according to the tissue type of each pixel point; and connecting the boundary pixel points at multiple depths to obtain the lesion boundary.

[0061] Specifically, because the time intensity curves of tumor tissue and normal tissue are different, the corresponding ranges of their characteristic parameters are also different. For example, the absolute value of the rising slope of tumor tissue is [1.5, 10], and the absolute value of the rising slope of normal tissue is [0, 1.5). It can be seen that the preset threshold of the absolute value of the rising slope is 1.5. If the absolute value of the rising slope of a pixel is greater than or equal to 1.5, then the pixel represents tumor tissue; if the absolute value of the rising slope of a pixel is less than 1.5, then the pixel represents normal tissue. After determining the tissue type of the pixel at the same depth, if the tissue type of the pixel is normal tissue, and the tissue type of its adjacent pixel is tumor tissue, then the adjacent pixel is a boundary pixel. After determining the boundary pixels at multiple depths, these boundary pixels are connected to obtain the lesion boundary.

[0062] In one embodiment of the present application, after performing statistical analysis on the characteristic parameters to obtain the lesion boundary of the preset area, the method further includes: adjusting the lesion boundary to obtain a safe boundary of the treatment area.

[0063] Specifically, further adjustments can be made based on the lesion boundary, such as expanding a portion of the lesion boundary toward the range of normal tissue, so that some cells and tissues that may be undergoing canceration in the expanded range can also be classified as the range that needs to be ablated, and the adjusted boundary can be used as the safety boundary of the treatment area.

[0064] The following describes the device for determining the lesion boundary of the present application. Fig. 9 As shown, the device comprises:

[0065] The acquisition module 910 is used to acquire the raw data corresponding to the pixel points in the preset area; the raw data is the high frame rate contrast imaging data which is not affected by the user's post-processing adjustment;

[0066] The curve calculation module 920 is used to calculate the time intensity curve of the original data to obtain the time intensity curve corresponding to the pixel point;

[0067] A parameter extraction module 930, for extracting characteristic parameters corresponding to the pixel points at multiple depths according to the time intensity curves corresponding to the pixel points at multiple depths;

[0068] The boundary determination module 940 is used to perform statistical analysis on the characteristic parameters to obtain the lesion boundary of the preset area.

[0069] In one embodiment of the present application, the boundary determination module 940 includes: a curve forming unit, which is used to form a characteristic parameter curve of pixel points corresponding to the depth according to the characteristic parameters of pixel points at multiple depths; an inflection point calculation unit, which is used to calculate the inflection point of the characteristic parameter curve at the current depth; a first boundary point determination unit, which is used to use the pixel point corresponding to the inflection point as the boundary pixel point; and a first boundary connection unit, which is used to connect the boundary pixel points at multiple depths to obtain the lesion boundary.

[0070] In one embodiment of the present application, the boundary determination module 940 includes: a threshold acquisition unit, used to obtain a preset threshold corresponding to a characteristic parameter; a type determination unit, used to compare the characteristic parameters of the pixel points at the current depth with the preset threshold to determine the tissue type of each pixel point; a second boundary point determination unit, used to determine the boundary pixel points at the current depth according to the tissue type of each pixel point; a second boundary connection unit, used to connect the boundary pixel points at multiple depths to obtain the lesion boundary.

[0071] In one embodiment of the present application, the characteristic parameters include at least one of the following parameters: arrival time, peak time, rise time, peak intensity, rise slope, mean transit time, 50% peak time, decline slope, decline time, area under the perfusion curve, area under the decay curve, area under the curve, perfusion rate index and decay rate index.

[0072] In one embodiment of the present application, the perfusion rate index is the ratio of the area under the perfusion curve to the rise time.

[0073] In one embodiment of the present application, the decay rate index is the ratio of the area under the decay curve to the fall time.

[0074] In one embodiment of the present application, the device further includes: a boundary adjustment module, which is used to adjust the lesion boundary to obtain a safe boundary of the treatment area.

[0075] It should be noted that the specific implementation content of the above-mentioned device has been explained in detail in the corresponding method embodiment and will not be repeated here.

[0076] The electronic equipment of this application is introduced below, such as Fig.10 As shown, the present application provides an electronic device, which includes: a processor 1010 and a memory 1020, the memory 1020 is used to store executable instructions of the processor; the processor 1010 executes the executable instructions to enable the electronic device to implement the method for determining the lesion boundary provided by any embodiment of the present application.

[0077] Specifically, the method for determining the lesion boundary provided in the present application is stored in the memory 1020 of the electronic device, and the method for determining the lesion boundary is executed by the processor 1010. Therefore, on the premise that the original data is more accurate and real-time, the accuracy and real-time performance of the time intensity curve and characteristic parameters corresponding to the pixel points at each depth are improved, thereby improving the accuracy of the lesion boundary and realizing real-time evaluation during surgery.

[0078] It should be known that the specific implementation content of the electronic device in the present application has been explained in detail in the corresponding method embodiment and will not be repeated here.

[0079] Fig.11 The structure block diagram of a computer system for implementing an electronic device according to an embodiment of the present application is schematically shown.

[0080] It should be noted that Fig.11 The computer system 1100 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0081] like Fig.11 As shown, the computer system 1100 includes a processor 1101, which can be a CPU (Central Processing Unit) or an MCU (Microcontroller Unit). The processor 1101 can perform various appropriate actions and processes according to the program stored in the read-only memory 1102 (Read-Only Memory, ROM) or the program loaded from the storage part 1108 to the random access memory 1103 (Random Access Memory, RAM). In the random access memory 1103, various programs and data required for system operation are also stored. The processor 1101, the read-only memory 1102 and the random access memory 1103 are connected to each other through a bus 1104. The input / output interface 1105 (Input / Output interface, i.e., I / O interface) is also connected to the bus 1104.

[0082] The following components are connected to the input / output interface 1105: an input section 1106 including a keyboard, a mouse, etc.; an output section 1107 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a LAN card, a modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the input / output interface 1105 as needed. A removable medium 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1110 as needed so that a computer program read therefrom is installed into the storage section 1108 as needed.

[0083] In particular, according to an embodiment of the present application, the process described in each method flow chart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, and the computer program contains a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 1109, and / or installed from a removable medium 1111. When the computer program is executed by the processor 1101, various functions defined in the system of the present application are executed.

[0084] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0085] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0086] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0087] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation method of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the implementation method according to the present application.

[0088] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary technical means in the art that are not disclosed in the present application.

[0089] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for determining a lesion boundary, characterized in that: include: Acquire raw data corresponding to pixel points in a preset area; the raw data is high frame rate contrast imaging data that is not affected by user post-processing adjustment; Performing time intensity curve calculation on the original data to obtain a time intensity curve corresponding to the pixel point; Extracting characteristic parameters corresponding to the pixels at multiple depths according to the time intensity curves corresponding to the pixels at multiple depths; wherein the number of depths refers to the number of rows of pixels contained in the contrast image, and each row corresponds to one depth; According to the characteristic parameters of the pixel points at multiple depths, a characteristic parameter curve of the pixel points corresponding to the depth is formed; the inflection point of the characteristic parameter curve at the current depth is calculated; the pixel point corresponding to the inflection point is used as the boundary pixel point; the boundary pixel points at multiple depths are connected to obtain the lesion boundary; Alternatively, a preset threshold corresponding to the characteristic parameter is obtained; the characteristic parameter of the pixel point at the current depth is compared with the preset threshold to determine the tissue type of each pixel point; based on the tissue type of each pixel point, the boundary pixel point at the current depth is determined; the boundary pixel points at multiple depths are connected to obtain the lesion boundary.

2. The method for determining the lesion boundary according to claim 1, characterized in that: The characteristic parameters include at least one of the following parameters: arrival time, peak time, rise time, peak intensity, rise slope, mean transit time, 50% peak time, decline slope, decline time, area under the perfusion curve, area under the decay curve, area under the curve, perfusion rate index and decay rate index.

3. The method for determining the lesion boundary according to claim 2, characterized in that: The perfusion rate index is the ratio of the area under the perfusion curve to the rise time.

4. The method for determining the lesion boundary according to claim 2, characterized in that: The decay rate index is the ratio of the area under the decay curve to the decline time.

5. The method for determining the lesion boundary according to claim 1, characterized in that: After statistically analyzing the characteristic parameters to obtain the lesion boundary of the preset area, the method further includes: The lesion boundary is adjusted to obtain a safe boundary of the treatment area.

6. A device for determining the boundary of a lesion, characterized in that: include: An acquisition module, used to acquire raw data corresponding to pixel points in a preset area; the raw data is high frame rate contrast imaging data that is not affected by user post-processing adjustment; A curve calculation module, used to perform time intensity curve calculation on the original data to obtain a time intensity curve corresponding to the pixel point; A parameter extraction module, used to extract characteristic parameters corresponding to the pixel points at multiple depths according to the time intensity curves corresponding to the pixel points at multiple depths; wherein the number of depths refers to the number of rows of pixel points contained in the contrast image, and each row corresponds to one depth; A boundary determination module is used to form a characteristic parameter curve of pixel points corresponding to the depth based on the characteristic parameters of pixel points at multiple depths; calculate the inflection point of the characteristic parameter curve at the current depth; use the pixel point corresponding to the inflection point as the boundary pixel point; connect the boundary pixel points at multiple depths to obtain the lesion boundary; or obtain a preset threshold value corresponding to the characteristic parameter; compare the characteristic parameter of the pixel point at the current depth with the preset threshold value to determine the tissue type of each pixel point; determine the boundary pixel point at the current depth based on the tissue type of each pixel point; connect the boundary pixel points at multiple depths to obtain the lesion boundary.

7. A computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for determining the lesion boundary described in any one of claims 1 to 5 is implemented.

8. An electronic device, characterized in that: include: processor; A memory, configured to store executable instructions of the processor; The processor executes the executable instructions to enable the electronic device to implement the method for determining the lesion boundary as described in any one of claims 1 to 5 above.

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