Improved speckle decorrelation ultrasonic probe displacement estimation method based on image segmentation

Abstract

The invention provides an improved speckle decorrelation ultrasonic probe displacement estimation method based on image segmentation, and the method comprises the steps: obtaining an ultrasonic image through an ultrasonic probe, determining a start frame in the ultrasonic image, and taking the start frame as a current frame; segmenting the current frame to obtain different initial regions; screening the different initial regions to determine a region of interest (ROI); determining in-plane displacement and out-of-plane displacement of the ROI; calculating the displacement of the ultrasonic probe; and taking the next frame in the ultrasonic image as the current frame, performing judgment according to a preset correlation condition, if the preset correlation condition is not met, returning to re-segment different initial regions, otherwise, re-calculating the in-plane displacement and the out-of-plane displacement until the last frame is ended. According to the method, the precision of out-of-plane motion estimation of the speckle decorrelation technology can be improved, and a new method is provided for estimating the motion of the ultrasonic probe and performing 3D ultrasonic reconstruction by using the one-dimensional ultrasonic probe.

Description

Technical Field

[0001] The invention relates to an ultrasonic probe displacement estimation method, in particular to an improved speckle decorrelation ultrasonic probe displacement estimation method based on image segmentation. Background Art

[0002] This section merely provides background information related to the present disclosure and is not necessarily prior art.

[0003] Three-dimensional ultrasound imaging technology is a commonly used medical imaging diagnostic technology that generates a three-dimensional image of the target through ultrasonic imaging to assist doctors in their assessment and diagnosis. Three-dimensional ultrasound imaging is a non-invasive, radiation-free examination method that has the advantages of being easy to operate and low cost compared to CT, MRI and other methods.

[0004] However, the current three-dimensional ultrasound imaging system is usually composed of a mechanical scanning probe or a two-dimensional sensor array, which is not only more expensive than the traditional one-dimensional probe, but also has considerable sacrifices in imaging speed and quality. Using IMU or optical tracking systems to provide displacement data also has the disadvantages of high cost and low data accuracy.

[0005] 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 disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] Purpose of the invention: The technical problem to be solved by the present invention is to provide an improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation in view of the deficiencies in the prior art.

[0007] In order to solve the above technical problems, the present invention discloses an improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation, comprising:

[0008] Step 1, using the ultrasound probe to acquire an ultrasound image, determining a starting frame in the ultrasound image, and using the starting frame as the current frame;

[0009] Step 2, segmenting the current frame to obtain different initial regions;

[0010] Step 3, screening the different initial regions, determining the region of interest ROI, and drawing an attenuation coefficient slope ACS graph;

[0011] Step 4, using a multi-target tracking method to determine the in-plane displacement of the region of interest ROI;

[0012] Step 5, using the attenuation coefficient slope ACS map to correct the backscatter coefficient BSC and using the speckle decorrelation method to estimate the out-of-plane displacement of the region of interest ROI;

[0013] Step 6, calculating the ultrasonic probe displacement according to the in-plane displacement and out-of-plane displacement of the region of interest ROI;

[0014] Step 7, taking the next frame in the ultrasound image as the current frame, and making a judgment according to the preset correlation condition, if it is not satisfied, returning to step 2, otherwise executing step 4 until the last frame ends.

[0015] Furthermore, the segmentation described in step 2 specifically includes:

[0016] The U-net network is used to segment the starting frame with echo texture features to obtain different initial regions.

[0017] Furthermore, the determination of the region of interest ROI described in step 3 specifically includes:

[0018] Step 3-1, calculate the interest score K, the method is as follows:

[0019]

[0020] Wherein, S is the area of ​​the initial region, L is the perimeter of the initial region, and ρ is the density of fully developed speckles in the initial region;

[0021] Step 3-2, sort all initial regions according to their interest scores, and add the initial region with the highest interest score to the region of interest ROI;

[0022] Step 3-3, remove the initial area added to the region of interest ROI, and repeat steps 3-2 to 3-3 until the area of ​​the region of interest ROI reaches a preset proportion of the starting frame.

[0023] Furthermore, the multi-target tracking method described in step 4 is used to determine the in-plane displacement of the region of interest ROI, specifically including:

[0024] Step 4-1, according to the region of interest ROI determined in step 3, the in-plane displacement of the region of interest ROI is calculated by using the target tracking method

[0025] Step 4-2: Use the ICP image registration method to calculate the overall in-plane displacement of the current frame

[0026] Step 4-3, calculate the in-plane displacement of the region of interest ROI The details are as follows:

[0027]

[0028] Furthermore, the attenuation coefficient slope ACS map is used to correct the backscatter coefficient BSC in step 5, that is, after each segmentation of the current frame, an attenuation coefficient slope ACS map is drawn to correct the backscatter coefficient BSC, and is not updated before re-segmentation.

[0029] Further, the correction described in step 5 is as follows:

[0030] Calculate the backscatter coefficient BSC distribution h(z,f) in the current frame as follows:

[0031]

[0032] in, <S(z,f) 2 > is the average power spectrum of the gated scan lines of the ultrasound probe in the region of interest ROI, |S ref (F,f)| 2 is the reference power spectrum obtained from the planar reflector at the focal length of the ultrasound probe, A(z,f) is the total attenuation of the ultrasound probe, L is the axial length of the gating window of the ultrasound probe, f is the ultrasound frequency emitted by the ultrasound probe, and the acoustic coupling function D of the ultrasound probe is ref (2F,f) is defined as follows:

[0033]

[0034] Where J0 is the 0th order Bessel function of the first kind, J1 is the 1st order Bessel function of the first kind, and G p (f) is the diffraction effect of ultrasonic wave on frequency, which is calculated as follows:

[0035]

[0036] Wherein α is the transducer radius, c0 is the speed of sound in the propagation medium, and F is the focal length of the ultrasonic probe;

[0037] Furthermore, the total attenuation A(z,f) of the ultrasound probe described in step 5 is calculated as follows:

[0038]

[0039] Wherein, Δz is the axial step size used for the axial spatial discretization of the ultrasonic probe, and the time delay α q The medium is at position z q = the attenuation coefficient slope ACS at qΔz.

[0040] Furthermore, the out-of-plane displacement of the region of interest ROI is estimated by using the speckle decorrelation method described in step 5, specifically including:

[0041] Step 5-1: normalized correlation function p(Δy i ),as follows:

[0042]

[0043] Among them, Δy i is the out-of-plane displacement of the region of interest ROI, e y is the unit vector in the out-of-plane direction, I is the signal intensity, m and n are the length and width of the region of interest ROI;

[0044] Step 5-2, in a medium containing randomly distributed fine particles, the correlation function p(Δr) of the complex echo signal of the ultrasonic probe is as follows:

[0045]

[0046] in, is the square of the average echo intensity in the region of interest ROI, ||ρ(Δr)|| 2 is the normalized correlation of echo signal intensity, Δr represents the displacement, and R(Δr) represents the correlation between adjacent signals, that is;

[0047] R(Δr)=<I(r)I(r+Δr)>

[0048] Step 5-3, according to the correlation function p(Δy i ) and the correlation function p(Δr), combined with the corrected backscattering coefficient BSC, the out-of-plane displacement Δy is obtained i .

[0049] Furthermore, the calculation of the ultrasonic probe displacement described in step 6 specifically includes:

[0050] Step 6-1: If there are multiple regions of interest (ROIs) determined in step 3, the out-of-plane displacement Δy i Represents the i-th region of interest ROI;

[0051] Step 6-2, according to the i-th region of interest ROI, calculate the i-th displacement vector l of the ultrasound probe i , as follows:

[0052]

[0053] Step 6-3, the average value of the displacement vector is used as the ultrasonic probe displacement, which is as follows:

[0054]

[0055] Where n represents the number of regions of interest (ROIs).

[0056] Furthermore, the preset correlation condition described in step 7 specifically includes:

[0057] The correlation coefficient between the current frame and the first frame of any region of interest ROI is less than a threshold or the region of interest ROI is not detected in three consecutive frames using the target tracking method;

[0058] The first frame is the current frame when the region of interest ROI is determined in step 3.

[0059] Beneficial effects:

[0060] The method proposed in the present invention can improve the accuracy of out-of-plane motion estimation using speckle decorrelation technology, and provides a new method for estimating the motion of an ultrasonic probe and performing 3D ultrasonic reconstruction using a one-dimensional ultrasonic probe. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more clear.

[0062] Figure 1 It is a schematic diagram of the overall process of the present invention.

[0063] Figure 2 Schematic diagram of the starting frame segmentation.

[0064] Figure 3 The in-plane displacement measurement results.

[0065] Figure 4 The out-of-plane displacement measurement results.

[0066] Figure 5 is the total displacement measurement result. DETAILED DESCRIPTION

[0067] The design principle of the present invention is as follows:

[0068] Speckle decorrelation technology is based on Rayleigh scattering noise. Although only one-dimensional probe is needed, in the real human body, tissues are often distributed in clusters and strips, which easily produce mirror reflections between tissues, affecting the degree of speckle development and causing serious decorrelation inaccuracy. Therefore, at present, 3D ultrasound will only be popularized when it enhances rather than damages the key advantages of ultrasound, namely cheapness, safety, portability and high resolution.

[0069] The present invention provides an improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation, such as Figure 1As shown, it is achieved through the following technical solutions:

[0070] Step 1: Obtain an ultrasound image, determine the starting frame, and use the U-net network to segment different regions in the starting frame with echo texture features, and determine the region of interest ROI by the interest score of each region. The steps include:

[0071] Step 1-1, obtain an ultrasound image and determine a starting frame. For the obtained starting frame ultrasound image, select a corresponding network, perform image segmentation using a U-net algorithm, and divide the image into different tissue regions.

[0072] Step 1-2, record the coordinates of each area in the image, select relatively regular areas, and transfer them to the image tracking system as the region of interest (ROI) as the initial coordinates.

[0073] Step 1-3, use the encoder to extract features from the ultrasound image and output a feature map.

[0074] Steps 1-4: Use the decoder to upsample the feature map and output the segmentation result.

[0075] The U-net network includes: a convolutional layer (encoder), a deconvolutional layer (decoder) and an activation function ReLu (reference: Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv, abs / 1505.04597.).

[0076] The feature extraction encoder includes two 3×3 convolutions, a ReLu activation function and a 2×2 maximum pooling.

[0077] The decoder includes: two 3×3 convolutions, a ReLu activation function and upsampling.

[0078] The screening mechanism is an area-perimeter ratio screening mechanism, and its score function K is:

[0079]

[0080] Where S is the area, L is the perimeter, and ρ is the density of fully developed speckle. Fully developed speckle needs to meet the following points:

[0081] (1) There are a large number of scatterers, and their phases and amplitudes are statistically independent;

[0082] (2) The amplitudes of different scatterers follow the same statistical distribution;

[0083] (3) Their phases are uniformly distributed;

[0084] The area with high score coefficient was selected as ROI until the ROI exceeded 30% of the ultrasound image area.

[0085] Step 2, using a multi-target tracking method to determine the displacement of each ROI and evaluate the correlation between adjacent frames;

[0086] The determined ROI is used as the initial coordinates of the tracking, and its in-plane displacement is determined using a multi-target tracking method (such as DeepSort)

[0087] Using image registration methods such as ICP (reference: Xu Wenfei, Jin Li, Han Xu, Cheng Haozhe, Tian Xuan. Mirror Iterative Closest Point Algorithm for Missing Point Cloud Registration [J]. Journal of Xi'an Jiaotong University, 2023, Vol. 57(7): 201-212, 220), determine the in-plane displacement of the entire image

[0088] Using the above displacement data, the relative displacement of the tracking area is calculated

[0089] Step 3, using the speckle decorrelation method to estimate the out-of-plane displacement of each ROI;

[0090] The out-of-plane displacement estimation is based on the ultrasonic speckle decorrelation method.

[0091] In a medium containing randomly distributed fine particles, there is a relationship between the correlation functions of the complex echo signals:

[0092]

[0093] Where I is the echo intensity within the ROI, ||ρ(Δr)|| 2 is the normalized correlation of the echo signal strength.

[0094] in:

[0095] R(Δr)=<I(r)I(r+Δr)>

[0096] In actual measurement, the normalized correlation function of the scanning plane with the motion in the direction perpendicular to the slice separation is considered (called the elevation direction). In this case, the echo signal intensity between the image ROIs is determined by the above formula, and the normalized correlation function of the echo signal intensity depends only on the characteristics of the transducer in this direction, which is given by the formula:

[0097] p(Δr)≈(sinc 2 y0*sinc 2 y0) 2

[0098] The function given below can be well approximated as a one-dimensional vector. Each pixel value is converted into a linear scale by a Gaussian function:

[0099]

[0100] Where I is the echo intensity, Δy i is the out-of-plane displacement, e y is the unit vector in the out-of-plane direction, m and n are the length and width of ROI. So:

[0101]

[0102] in: z is the beam length, b0 is the wavelength, and h is the backscatter coefficient (BSC), which can usually be estimated (reference: MF Insana, TJ Hall and LTCook, "Backscatter coefficient estimation using array transducers," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 41, no. 5, pp. 714-723, Sept. 1994, doi: 10.1109 / 58.308508).

[0103] From this we can inversely solve for Δy i .

[0104] However, nonlinear media may cause errors in BSC. Therefore, the present invention uses the attenuation coefficient slope (ACS) for compensation. BSC is given by:

[0105]

[0106] <S(z,f) 2 > is the average power spectrum of the gated scan lines within the data block, |S ref (F,f)| 2 is the reference power spectrum obtained from a planar reflector at the focal length, A(z,f) is the total attenuation, L is the axial length of the gating window, and f is the frequency. ref Defined as:

[0107]

[0108] where Jm is the mth-order Bessel function of the first kind. p (f) is the diffraction effect of ultrasonic wave on frequency:

[0109]

[0110] Where α is the transducer radius, c0 is the speed of sound in the propagation medium, and F is the focal length.

[0111] A(z,f) is given by:

[0112]

[0113] Among them, Δz is the axial step size used for axial space discretization, and the time delay α q The medium is at position z q = ACS at qΔz.

[0114] For ACS calculation, see Coila A, Rouyer J, Zenteno O, Luchies A, Oelze ML, Lavarello R. Total attenuation compensation for backscatter coefficient estimation using full angular spatial compounding. Ultrasonics. 2021; 114: 106376-106376. After each segmentation, an ACS diagram is drawn and is not updated before re-segmentation.

[0115] Step 4: Calculate the displacement of the probe by combining the displacement inside and outside the plane. That is:

[0116]

[0117] Combine multiple ROIs and calculate their average displacement value as the displacement value of the probe:

[0118]

[0119] Step 5, use the correlation coefficient to evaluate the current ROI and the initial area (referring to the ROI area in the first frame when tracking starts). When any ROI correlation coefficient is less than 0.75, or the target tracking mismatch occurs (DeepSort fails to detect a matching area for three consecutive frames), return to step 1-1 and redefine the tracking area. Otherwise, take the next frame in the ultrasound image as the current frame and execute step 4 until all frames in the ultrasound image are executed.

[0120] Embodiment 1:

[0121] like Figure 1 As shown, the present invention provides:

[0122] S101: using a one-dimensional ultrasound probe, acquiring an ultrasound image and determining a starting frame;

[0123] S102: performing image segmentation on the acquired initial ultrasound image by using a U-net algorithm to divide different tissues;

[0124] S103: Screen the region of interest (ROI) until its area exceeds 30% of the total area, and plot the attenuation coefficient slope (ACS) as follows: Figure 4 ,

[0125] Its interest score K is:

[0126]

[0127] Among them, S is the area and L is the perimeter.

[0128] S104: Track the ROIs using multi-target tracking technology, and calculate the in-plane displacement of each ROI.

[0129] S105: Correct the BSC using the ACS image, and estimate the out-of-plane displacement in the ROI using the speckle decorrelation technique.

[0130] S106: Calculate the total displacement of each ROI through the in-plane and out-of-plane displacements:

[0131]

[0132] S107: Calculate the probe displacement by the displacement of each ROI:

[0133]

[0134] S108: Check whether the correlation is insufficient and whether there is a mismatch in target tracking. If so, update the frame to the starting frame, otherwise enter S106. If there is no next frame, end the program.

[0135] In an exemplary embodiment of the present invention, determining the in-plane displacement by DeepSort algorithm includes:

[0136] S201: Determine the minimum tracking box including the ROI

[0137] S202: Initialize the Kalman filter tracker using the detection result of the first frame, and obtain the prediction result of the first frame through Kalman filter prediction.

[0138] S203: Match the detection result of the current frame with the prediction result of the previous frame, calculate the IOU of each detection box and each tracking box, and calculate their cost matrix.

[0139] S204: Input the filtered results and the detection box into the KM algorithm for matching. There are three matching results:

[0140] The first match is successful, indicating that the tracking is successful, and the corresponding Kalman filter tracker is updated with the detection information of the current frame;

[0141] The second type of detection mismatch means that a new target appears in the video and a new tracker needs to be created for the new target. However, since this target appears for the first time, the algorithm considers this result unreliable and needs to match three frames in a row before the result is displayed.

[0142] The third type is tracking mismatch, the tracker will not be updated. When the tracker fails to match the result for three consecutive frames, the track will be deleted.

[0143] Embodiment 2:

[0144] The present invention also provides an embodiment of out-of-plane displacement estimation based on speckle decorrelation technology:

[0145] This embodiment is based on the synthetic speckle image provided by the dataset US simulation & segmentation (reference: Vitale, S., Orlando, JI, Iarussi, E., & Larrabide, I. (2019). Improving realism in patient-specific abdominal ultrasound simulation using CycleGANs. International Journal of Computer Assisted Radiology and Surgery, 15(2), 183-192.). The out-of-plane displacement step between adjacent images is 0.2 mm, and the in-plane displacement is randomly distributed within 0.2 mm, for a total of 11 frames.

[0146] First, the U-net network is used to segment the starting frame and filter it as in the first embodiment. Here is an example of the starting frame filtering result. Figure 2 As shown in the figure, the yellow part is the ROI. DeepSort is then used to track and calculate its in-plane displacement. Figure 2 Take the displacement measurement of the right ROI surface as an example. Figure 3 As shown, the ordinate is the absolute value of the in-plane displacement, and the abscissa is the frame number; then, the out-of-plane displacement is calculated using the speckle decorrelation technique. Figure 4The estimated value of the out-of-plane displacement is given: the ordinate is the absolute value of the out-of-plane displacement, the abscissa is the number of frames, the black curve is the true value, and the red curve is the measured value; finally, as shown in steps S106 and S107, the probe displacement is calculated, and its average value is 0.284, with an error of about 6.7%; Figure 5 As shown in the figure, the ordinate is the absolute value of displacement, the abscissa is the number of frames, the black curve is the true value, and the red curve is the measured value. Of course, this data is based on the synthetic speckle image and is only of reference significance for its performance in actual applications.

[0147] In a specific implementation, the present application provides a computer storage medium and a corresponding data processing unit, wherein the computer storage medium can store a computer program, and when the computer program is executed by the data processing unit, the invention content of an improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation provided by the present invention and some or all steps in each embodiment can be run. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0148] Those skilled in the art can clearly understand that the technical solutions in the embodiments of the present invention can be implemented by means of computer programs and their corresponding general hardware platforms. Based on such an understanding, the technical solutions in the embodiments of the present invention can be essentially or partly contributed to the prior art in the form of computer programs, i.e., software products, which can be stored in a storage medium and include several instructions for enabling a device including a data processing unit (which can be a personal computer, a server, a single-chip microcomputer, an MCU or a network device, etc.) to execute the methods described in various embodiments of the present invention or certain parts of the embodiments.

[0149] The present invention provides a concept and method for improving the speckle decorrelation ultrasound probe displacement estimation method based on image segmentation. There are many methods and ways to implement the technical solution. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. An improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation, characterized in that: include: Step 1, using the ultrasound probe to acquire an ultrasound image, determining a starting frame in the ultrasound image, and using the starting frame as the current frame; Step 2, segmenting the current frame to obtain different initial regions; Step 3, screening the different initial regions, determining the region of interest ROI, and drawing an attenuation coefficient slope ACS graph; Step 4, using a multi-target tracking method to determine the in-plane displacement of the region of interest ROI; Step 5, using the attenuation coefficient slope ACS map to correct the backscatter coefficient BSC and using the speckle decorrelation method to estimate the out-of-plane displacement of the region of interest ROI; Step 6, calculating the ultrasonic probe displacement according to the in-plane displacement and out-of-plane displacement of the region of interest ROI; Step 7, taking the next frame in the ultrasound image as the current frame, and making a judgment according to the preset correlation condition, if it is not satisfied, returning to step 2, otherwise executing step 4 until the last frame ends.

2. The improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation according to claim 1, characterized in that: The segmentation described in step 2 specifically includes: The U-net network is used to segment the starting frame with echo texture features to obtain different initial regions.

3. The improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation according to claim 2, characterized in that: Determining the region of interest (ROI) described in step 3 specifically includes: Step 3-1, calculate the interest score K, the method is as follows: Wherein, S is the area of ​​the initial region, L is the perimeter of the initial region, and ρ is the density of fully developed speckles in the initial region; Step 3-2, sort all initial regions according to their interest scores, and add the initial region with the highest interest score to the region of interest ROI; Step 3-3, remove the initial area added to the region of interest ROI, and repeat steps 3-2 to 3-3 until the area of ​​the region of interest ROI reaches a preset proportion of the starting frame.

4. The improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation according to claim 3, characterized in that: The multi-target tracking method described in step 4 is used to determine the in-plane displacement of the region of interest ROI, specifically including: Step 4-1, according to the region of interest ROI determined in step 3, the in-plane displacement of the region of interest ROI is calculated by using the target tracking method Step 4-2: Use the ICP image registration method to calculate the overall in-plane displacement of the current frame Step 4-3, calculate the in-plane displacement of the region of interest ROI The details are as follows: 。 5. The improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation according to claim 4, characterized in that: The attenuation coefficient slope ACS map is used to correct the backscatter coefficient BSC in step 5, that is, after each segmentation of the current frame, an attenuation coefficient slope ACS map is drawn to correct the backscatter coefficient BSC, and is not updated before re-segmentation.

6. The improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation according to claim 5, characterized in that: The correction described in step 5 is as follows: Calculate the backscatter coefficient BSC distribution h(z,f) in the current frame as follows: in, <S(z,f) 2 > is the average power spectrum of the gated scan lines of the ultrasound probe in the region of interest ROI, |S ref (F,f)| 2 is the reference power spectrum obtained from the planar reflector at the focal length of the ultrasound probe, A(z,f) is the total attenuation of the ultrasound probe, L is the axial length of the gating window of the ultrasound probe, f is the ultrasound frequency emitted by the ultrasound probe, and the acoustic coupling function D of the ultrasound probe is ref (2F,f) is defined as follows: Where J0 is the 0th order Bessel function of the first kind, J1 is the 1st order Bessel function of the first kind, and G p (f) is the diffraction effect of ultrasonic wave on frequency, which is calculated as follows: Wherein α is the transducer radius, c0 is the speed of sound in the propagation medium, and F is the focal length of the ultrasound probe.

7. The improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation according to claim 6, characterized in that: The total attenuation of the ultrasound probe, A(z,f), described in step 5, is calculated as follows: Wherein, Δz is the axial step size used for the axial spatial discretization of the ultrasonic probe, and the time delay α q The medium is at position z q = the attenuation coefficient slope ACS at qΔz.

8. The improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation according to claim 7, characterized in that: The out-of-plane displacement estimation of the region of interest ROI using the speckle decorrelation method described in step 5 specifically includes: Step 5-1: normalized correlation function p(Δy i ),as follows: Among them, Δy i is the out-of-plane displacement of the region of interest ROI, e y is the unit vector in the out-of-plane direction, I is the signal intensity, m and n are the length and width of the region of interest ROI; Step 5-2, in a medium containing randomly distributed fine particles, the correlation function p(Δr) of the complex echo signal of the ultrasonic probe is as follows: in, is the square of the average echo intensity in the region of interest ROI, ||ρ(Δr)|| 2 is the normalized correlation of echo signal intensity, Δr represents the displacement, and R(Δr) represents the correlation between adjacent signals, that is; R(Δr)= <I(r)I(r+Δr)〉 Step 5-3, according to the correlation function p(Δy i ) and the correlation function p(Δr), combined with the corrected backscattering coefficient BSC, the out-of-plane displacement Δy is obtained i .

9. The improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation according to claim 8, characterized in that: The calculation of the ultrasonic probe displacement described in step 6 specifically includes: Step 6-1: If there are multiple regions of interest (ROIs) determined in step 3, the out-of-plane displacement Δy i Represents the i-th region of interest ROI; Step 6-2, according to the i-th region of interest ROI, calculate the i-th displacement vector l of the ultrasound probe i , as follows: Step 6-3, the average value of the displacement vector is used as the ultrasonic probe displacement, which is as follows: Where n represents the number of regions of interest (ROIs).

10. The improved speckle decorrelation ultrasound probe displacement estimation method based on image segmentation according to claim 9, characterized in that: The preset correlation conditions described in step 7 specifically include: The correlation coefficient between the current frame and the first frame of any region of interest ROI is less than a threshold or the region of interest ROI is not detected in three consecutive frames using the target tracking method; The first frame is the current frame when the region of interest ROI is determined in step 3.

Images