Intelligent focus puncture positioning method and device for ultrasonic image of remote fibular ligament

Abstract

The invention discloses an intelligent focus puncture positioning method and device for a remote prefibular ligament ultrasonic image. The method comprises the steps that an ultrasonic image data set of a remote prefibular ligament is obtained; the ultrasonic image data set of the remote fibular ligament comprises N ultrasonic image data of the remote fibular ligament, and N is a positive integer; preprocessing the ultrasonic image data of the distal fibular ligament to obtain preprocessed ultrasonic image data; segmenting the preprocessed ultrasonic image data to obtain segmented ultrasonic image data; performing data enhancement on the segmented ultrasonic image data to obtain enhanced ultrasonic image data; processing the enhanced ultrasonic image data by using a three-dimensional reconstruction model to obtain the three-dimensional reconstruction model; and processing the three-dimensional reconstruction model to obtain puncture path planning information. According to the invention, accurate identification of the distal fibular ligament is realized, and the accuracy of puncture positioning is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and in particular to a method and device for intelligent puncture positioning of lesions of anterior talofibular ligament ultrasound images. Background Art

[0002] The anterior talofibular ligament (ATFL) is one of the most important ligaments on the outside of the ankle joint and plays a key role in maintaining the stability of the ankle joint. Anterior talofibular ligament injury is the most common type of ankle sprain, especially in sports injuries. Accurate diagnosis and location of anterior talofibular ligament injury is crucial for developing an effective treatment plan.

[0003] Although ultrasound imaging technology has the advantages of non-invasive, real-time, and dynamic observation in the diagnosis of musculoskeletal diseases, there are still the following difficulties in actual clinical applications, especially in the scanning of the anterior talofibular ligament:

[0004] 1. Complex anatomical structure: The anterior talofibular ligament is located on the outside of the ankle joint. The surrounding anatomical structure is complex, including the fibula, talus, joint capsule, etc. These structures are prone to produce artifacts in ultrasound images, affecting the clear display of the ligament.

[0005] 2. Individual differences among patients: The anatomical structures of different patients are different. For example, obese patients have a thicker subcutaneous fat layer, which further increases the difficulty of obtaining ultrasound images.

[0006] 3. High technical requirements for operation: Ultrasonic scanning requires the operator to have a high level of technical skills, including accurate placement of the probe, precise control of the scanning angle, etc., which places high demands on the operator's experience and skills.

[0007] For clinicians who are not specialized in ultrasound, it is difficult to accurately identify the anterior talofibular ligament. The main reasons include:

[0008] 1. Insufficient professional knowledge: Non-ultrasound specialists lack systematic ultrasound imaging training and find it difficult to accurately identify complex anatomical structures.

[0009] 2. Difficulty in image interpretation: The dynamic and real-time nature of ultrasound images increases the difficulty of image interpretation. It is difficult for non-professional physicians to accurately judge the status of the ligament in a short period of time.

[0010] 3. Lack of experience accumulation: The interpretation of ultrasound images requires a lot of practical experience accumulation, and non-professional physicians have obvious shortcomings in this regard.

[0011] The learning curve for musculoskeletal ultrasound is relatively steep, mainly reflected in the following aspects:

[0012] 1. High technical requirements: Ultrasonic scanning requires mastery of many aspects of technology such as probe operation, image acquisition, and image interpretation.

[0013] 2. Complex knowledge system: It is necessary to master multidisciplinary knowledge such as anatomy, pathology, and ultrasound imaging.

[0014] 3. Limited practice opportunities: Ultrasound scanning requires a lot of practice opportunities, but the actual clinical operation opportunities are limited, which restricts the physician's learning and experience accumulation.

[0015] Therefore, it is of great significance to study an intelligent puncture localization method for lesions in the anterior talofibular ligament using ultrasound images. Summary of the invention

[0016] The technical problem to be solved by the present invention is to provide a method and device for intelligent puncture positioning of lesions in the anterior talofibular ligament based on ultrasonic images, use a deep learning method to achieve accurate identification of the anterior talofibular ligament, and plan the syringe puncture path through an intelligent navigation system to improve puncture accuracy and safety.

[0017] In order to solve the above technical problems, the first aspect of the embodiment of the present invention discloses a method for intelligent puncture positioning of lesions of the anterior talofibular ligament using ultrasound images, the method comprising:

[0018] S1, obtaining an ultrasound image dataset of the anterior talofibular ligament; the ultrasound image dataset of the anterior talofibular ligament includes N ultrasound image data of the anterior talofibular ligament, where N is a positive integer;

[0019] S2, preprocessing the ultrasound image data of the anterior talofibular ligament to obtain preprocessed ultrasound image data;

[0020] S3, segmenting the preprocessed ultrasonic image data to obtain segmented ultrasonic image data;

[0021] S4, performing data enhancement on the segmented ultrasonic image data to obtain enhanced ultrasonic image data;

[0022] S5, using the three-dimensional reconstruction model, processing the enhanced ultrasound image data to obtain a three-dimensional reconstruction model;

[0023] S6, processing the three-dimensional reconstructed model to obtain puncture path planning information.

[0024] As an optional implementation, in the first aspect of the embodiment of the present invention, the preprocessing of the ultrasound image data of the anterior talofibular ligament to obtain the preprocessed ultrasound image data includes:

[0025] S21, annotating the ultrasound image data of the anterior talofibular ligament to obtain label information data;

[0026] S22, performing denoising processing on the ultrasonic image data of the anterior talofibular ligament to obtain denoised ultrasonic image data;

[0027] S23, performing contrast enhancement processing on the denoised ultrasonic image data to obtain pre-processed pre-talofibular acoustic image data.

[0028] As an optional implementation, in the first aspect of the embodiment of the present invention, the denoising of the ultrasound image data of the anterior talofibular ligament to obtain the denoised ultrasound image data includes:

[0029] S221, using a first feature extraction model to process the ultrasound image data of the anterior talofibular ligament to obtain first feature information;

[0030] S222, down-sampling the first feature information to obtain second feature information;

[0031] S223, using a second feature extraction model, processing the second feature information to obtain third feature information;

[0032] S224, upsampling the third feature information to obtain fourth feature information;

[0033] S225, using the third feature extraction model, processing the fourth feature information to obtain fifth feature information;

[0034] S226, using a convolution model, processing the fifth feature information to obtain sixth feature information;

[0035] S227: Process the sixth characteristic information to obtain denoised ultrasonic image data.

[0036] As an optional implementation, in the first aspect of the embodiment of the present invention, the contrast enhancement processing is performed on the denoised ultrasonic image data to obtain pre-processed pre-talofibular acoustic image data, including:

[0037] S231, decomposing the denoised ultrasonic image data to obtain base layer data and detail layer data;

[0038] S232, performing illumination equalization processing on the base layer data to obtain enhanced base layer data;

[0039] S233, performing enhancement processing on the detail layer data to obtain enhanced detail layer data;

[0040] S234, synthesizing the enhanced base layer data and the enhanced detail layer data to obtain pre-processed pre-talofibular acoustic image data.

[0041] As an optional implementation, in the first aspect of the embodiment of the present invention, the step of performing data enhancement on the segmented ultrasound image data to obtain enhanced ultrasound image data includes:

[0042] S41, using a preset generator network, processing the label information data and random noise to obtain a generated image;

[0043] S42, processing the segmented ultrasound image data and the generated image to obtain a cyclic correlation coefficient;

[0044] S43, using the cyclic correlation coefficient, performing adversarial training on a preset discriminator network and a preset generator network to obtain an optimized discriminator network and an optimized generator network;

[0045] S44, using the optimized generator network, processing the label information data and random noise to obtain an augmented image;

[0046] S45, integrating the amplified image and the segmented ultrasonic image data to obtain enhanced ultrasonic image data.

[0047] As an optional implementation, in the first aspect of the embodiment of the present invention, the use of the three-dimensional reconstruction model to process the enhanced ultrasound image data to obtain the three-dimensional reconstruction model includes:

[0048] S51, using a three-dimensional reconstruction model, performing three-dimensional reconstruction on the enhanced ultrasound image data to obtain three-dimensional data information;

[0049] S52, visualizing the three-dimensional data information to obtain a three-dimensional reconstructed model.

[0050] As an optional implementation, in the first aspect of the embodiment of the present invention, the processing of the three-dimensional reconstructed model to obtain puncture path planning information includes:

[0051] S61, analyzing the three-dimensional reconstructed model to obtain starting point coordinate information and ending point coordinate information;

[0052] S62, setting the cost function and constraint conditions of the path planning problem;

[0053] S63, using Logistic chaos mapping to generate the initial population of particle swarm;

[0054] S64, using the artificial bee colony algorithm to perform iterative search and update the individual position to move it toward a lower fitness value;

[0055] S65, using chaotic mapping to search the local solution space to make it jump out of the local optimal value;

[0056] S66, repeat S64 and S65 until the maximum number of iterations is reached or the fitness value change threshold is reached;

[0057] S67, outputting the optimal fitness value and obtaining puncture path planning information.

[0058] A second aspect of an embodiment of the present invention discloses an intelligent puncture positioning device for lesions of the anterior talofibular ligament using ultrasonic imaging, the device comprising:

[0059] A data acquisition module, used to acquire an ultrasound image data set of the anterior talofibular ligament; the ultrasound image data set of the anterior talofibular ligament includes N ultrasound image data of the anterior talofibular ligament, where N is a positive integer;

[0060] A preprocessing module, used for preprocessing the ultrasonic image data of the anterior talofibular ligament to obtain preprocessed ultrasonic image data;

[0061] An image segmentation module, used for segmenting the preprocessed ultrasonic image data to obtain segmented ultrasonic image data;

[0062] A data enhancement module, used for performing data enhancement on the segmented ultrasonic image data to obtain enhanced ultrasonic image data;

[0063] A three-dimensional reconstruction module, used to process the enhanced ultrasound image data using a three-dimensional reconstruction model to obtain a three-dimensional reconstruction model;

[0064] The path planning module is used to process the three-dimensional reconstructed model to obtain puncture path planning information.

[0065] As an optional implementation, in the second aspect of the embodiment of the present invention, the preprocessing of the ultrasound image data of the anterior talofibular ligament to obtain the preprocessed ultrasound image data includes:

[0066] S21, annotating the ultrasound image data of the anterior talofibular ligament to obtain label information data;

[0067] S22, performing denoising processing on the ultrasonic image data of the anterior talofibular ligament to obtain denoised ultrasonic image data;

[0068] S23, performing contrast enhancement processing on the denoised ultrasonic image data to obtain pre-processed pre-talofibular acoustic image data.

[0069] As an optional implementation, in the second aspect of the embodiment of the present invention, the denoising of the ultrasound image data of the anterior talofibular ligament to obtain the denoised ultrasound image data includes:

[0070] S221, using a first feature extraction model to process the ultrasound image data of the anterior talofibular ligament to obtain first feature information;

[0071] S222, down-sampling the first feature information to obtain second feature information;

[0072] S223, using a second feature extraction model, processing the second feature information to obtain third feature information;

[0073] S224, upsampling the third feature information to obtain fourth feature information;

[0074] S225, using the third feature extraction model, processing the fourth feature information to obtain fifth feature information;

[0075] S226, using a convolution model, processing the fifth feature information to obtain sixth feature information;

[0076] S227: Process the sixth characteristic information to obtain denoised ultrasonic image data.

[0077] As an optional implementation, in the second aspect of the embodiment of the present invention, the contrast enhancement processing is performed on the denoised ultrasonic image data to obtain pre-processed pre-talofibular acoustic image data, including:

[0078] S231, decomposing the denoised ultrasonic image data to obtain base layer data and detail layer data;

[0079] S232, performing illumination equalization processing on the base layer data to obtain enhanced base layer data;

[0080] S233, performing enhancement processing on the detail layer data to obtain enhanced detail layer data;

[0081] S234, synthesizing the enhanced base layer data and the enhanced detail layer data to obtain pre-processed pre-talofibular acoustic image data.

[0082] As an optional implementation, in the second aspect of the embodiment of the present invention, the step of performing data enhancement on the segmented ultrasound image data to obtain enhanced ultrasound image data includes:

[0083] S41, using a preset generator network, processing the label information data and random noise to obtain a generated image;

[0084] S42, processing the segmented ultrasound image data and the generated image to obtain a cyclic correlation coefficient;

[0085] S43, using the cyclic correlation coefficient, performing adversarial training on a preset discriminator network and a preset generator network to obtain an optimized discriminator network and an optimized generator network;

[0086] S44, using the optimized generator network, processing the label information data and random noise to obtain an augmented image;

[0087] S45, integrating the amplified image and the segmented ultrasonic image data to obtain enhanced ultrasonic image data.

[0088] As an optional implementation, in the second aspect of the embodiment of the present invention, the use of the three-dimensional reconstruction model to process the enhanced ultrasound image data to obtain the three-dimensional reconstruction model includes:

[0089] S51, using a three-dimensional reconstruction model, performing three-dimensional reconstruction on the enhanced ultrasound image data to obtain three-dimensional data information;

[0090] S52, visualizing the three-dimensional data information to obtain a three-dimensional reconstructed model.

[0091] As an optional implementation, in the second aspect of the embodiment of the present invention, the processing of the three-dimensional reconstructed model to obtain puncture path planning information includes:

[0092] S61, analyzing the three-dimensional reconstructed model to obtain starting point coordinate information and ending point coordinate information;

[0093] S62, setting the cost function and constraint conditions of the path planning problem;

[0094] S63, using Logistic chaos mapping to generate the initial population of particle swarm;

[0095] S64, using the artificial bee colony algorithm to perform iterative search and update the individual position to move it toward a lower fitness value;

[0096] S65, using chaotic mapping to search the local solution space to make it jump out of the local optimal value;

[0097] S66, repeat S64 and S65 until the maximum number of iterations is reached or the fitness value change threshold is reached;

[0098] S67, outputting the optimal fitness value and obtaining puncture path planning information.

[0099] The third aspect of the present invention discloses another intelligent puncture positioning device for lesions of the anterior talofibular ligament using ultrasonic imaging, the device comprising:

[0100] A memory storing executable program code;

[0101] a processor coupled to the memory;

[0102] The processor calls the executable program code stored in the memory to execute part or all of the steps in the method for intelligent puncture positioning of lesions in the anterior talofibular ligament ultrasound imaging disclosed in the first aspect of the embodiment of the present invention.

[0103] The fourth aspect of the present invention discloses a computer-storable medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the method for intelligent puncture localization of lesions in the anterior talofibular ligament ultrasound imaging disclosed in the first aspect of the embodiment of the present invention.

[0104] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0105] The present invention discloses an intelligent puncture positioning method and device for lesions of the anterior talofibular ligament using ultrasound images, which can realize accurate identification of the anterior talofibular ligament and improve the accuracy of puncture positioning. By performing three-dimensional reconstruction and path planning, the intelligent navigation system can be assisted in planning the syringe puncture path, reducing the risk of puncture. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0107] Figure 1 It is a flow chart of a method for intelligent puncture positioning of anterior talofibular ligament lesions using ultrasound imaging disclosed in an embodiment of the present invention;

[0108] Figure 2 It is an ultrasonic image of the anterior talofibular ligament disclosed in an embodiment of the present invention;

[0109] Figure 3 is a schematic diagram of a method for denoising anterior talofibular ligament ultrasound images disclosed in an embodiment of the present invention;

[0110] Figure 4 It is a structural schematic diagram of a device for intelligent puncture and localization of lesions of anterior talofibular ligament using ultrasonic imaging disclosed in an embodiment of the present invention;

[0111] Figure 5 It is a structural schematic diagram of another intelligent puncture positioning device for lesions of the anterior talofibular ligament using ultrasonic imaging disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0112] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0113] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0114] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0115] The anterior talofibular ligament (ATFL) is the weakest and most easily ruptured ligament tissue on the outside of the ankle. If the ATFL is severely injured, such as a tear or complete rupture, the patient is usually unable to heal on his own. In this case, surgical treatment, such as ligament reconstruction and suturing, is required to restore the integrity and function of the ligament. Knowing the exact location of the ATFL helps to identify the problem in time when the ankle is injured and take appropriate measures to prevent further injury. At present, the diagnosis of ligament injuries mainly relies on manual reading of films, which is labor-intensive and requires high professional qualifications and clinical experience of doctors.

[0116] The present invention discloses a method and device for intelligent puncture positioning of lesions of the anterior talofibular ligament using ultrasonic images. The method comprises: obtaining an ultrasonic image data set of the anterior talofibular ligament; the ultrasonic image data set of the anterior talofibular ligament comprises N ultrasonic image data of the anterior talofibular ligament, where N is a positive integer; preprocessing the ultrasonic image data of the anterior talofibular ligament to obtain preprocessed ultrasonic image data; segmenting the preprocessed ultrasonic image data to obtain segmented ultrasonic image data; performing data enhancement on the segmented ultrasonic image data to obtain enhanced ultrasonic image data; using a three-dimensional reconstruction model, processing the enhanced ultrasonic image data to obtain a three-dimensional reconstruction model; processing the three-dimensional reconstruction model to obtain puncture path planning information. The present invention realizes accurate identification of the anterior talofibular ligament and improves the accuracy of puncture positioning. The following are detailed descriptions respectively.

[0117] Embodiment 1

[0118] See also Figure 1 , Figure 1 1 is a flow chart of a method for intelligent puncture location of anterior talofibular ligament lesions using ultrasound imaging disclosed in an embodiment of the present invention. Figure 1 The method for intelligent puncture location of lesions in the anterior talofibular ligament ultrasound image described above is applied to the field of medical image processing technology, and the embodiments of the present invention are not limited thereto. Figure 1 As shown, the method for intelligent puncture positioning of lesions of the anterior talofibular ligament ultrasound image may include the following operations:

[0119] S1, obtaining an ultrasound image dataset of the anterior talofibular ligament; the ultrasound image dataset of the anterior talofibular ligament includes N ultrasound image data of the anterior talofibular ligament, where N is a positive integer;

[0120] Ultrasound imaging data of the anterior talofibular ligament were obtained using a high-resolution ultrasound probe or from the database of a medical institution.

[0121] S2, preprocessing the ultrasound image data of the anterior talofibular ligament to obtain preprocessed ultrasound image data;

[0122] S3, segmenting the preprocessed ultrasonic image data to obtain segmented ultrasonic image data;

[0123] The pre-processed ultrasound image data is segmented by using a threshold segmentation method to obtain segmented ultrasound image data, wherein the segmented ultrasound image data includes anterior talofibular ligament and a lesion area;

[0124] S4, performing data enhancement on the segmented ultrasonic image data to obtain enhanced ultrasonic image data;

[0125] S5, using the three-dimensional reconstruction model, processing the enhanced ultrasound image data to obtain a three-dimensional reconstruction model;

[0126] S6, processing the three-dimensional reconstructed model to obtain puncture path planning information.

[0127] Figure 2 This is an ultrasound image of the anterior talofibular ligament disclosed in an embodiment of the present invention. In the figure, LM represents the fibula (lateral malleolus), Ta represents the talus, and the arrow represents the anterior talofibular ligament.

[0128] Optionally, preprocessing the ultrasound image data of the anterior talofibular ligament to obtain preprocessed ultrasound image data includes:

[0129] S21, annotating the ultrasound image data of the anterior talofibular ligament to obtain label information data;

[0130] The collected ultrasound image data were annotated by experienced ultrasound imaging physicians, and the annotation content included the boundary of the anterior talofibular ligament, the lesion area (such as tear, swelling, etc., which was further divided into 1st degree, 2nd degree and 3rd degree according to the degree of tear and swelling), with a total of 7 labels.

[0131] S22, performing denoising processing on the ultrasonic image data of the anterior talofibular ligament to obtain denoised ultrasonic image data;

[0132] S23, performing contrast enhancement processing on the denoised ultrasonic image data to obtain pre-processed pre-talofibular acoustic image data.

[0133] Optionally, the denoising the ultrasonic image data of the anterior talofibular ligament to obtain denoised ultrasonic image data includes:

[0134] S221, using a first feature extraction model to process the ultrasound image data of the anterior talofibular ligament to obtain first feature information;

[0135] S222, down-sampling the first feature information to obtain second feature information;

[0136] S223, using a second feature extraction model, processing the second feature information to obtain third feature information;

[0137] S224, upsampling the third feature information to obtain fourth feature information;

[0138] S225, using the third feature extraction model, processing the fourth feature information to obtain fifth feature information;

[0139] S226, using a convolution model, processing the fifth feature information to obtain sixth feature information;

[0140] S227: Process the sixth characteristic information to obtain denoised ultrasonic image data.

[0141] Figure 3 It is a schematic diagram of the anterior talofibular ligament ultrasound image denoising method disclosed in an embodiment of the present invention. The first feature extraction model consists of two layers of Conv networks. The first layer includes 3×3, 1×3, 3×1 branches of convolutions of different sizes and directions. The second layer uses 3×3Conv+ReLU operations to further refine the fused features. Using convolution kernels of different sizes and directions can capture more diverse features, and the expansion rate of Conv is 1.

[0142] y1=C 1×3 (x)

[0143] y2=C 3×1 (x)

[0144] y3=C 3×3 (x)

[0145] f1=Re(C 3×3 (Cat(y1,y2,y3)))

[0146] Where x is the ultrasound image data of the anterior talofibular ligament (noisy image), C 1×3 is a 1×3 convolution, C 3×1 is a 3×1 convolution, C 3×3 is a 3×3 convolution, y1 is the output of a 1×3 convolution, y2 is the output of a 3×1 convolution, y3 is the output of a 3×3 convolution, f1 is the first feature information, Re represents the ReLU activation function, and Cat is a fusion concatenation operation.

[0147] The second feature extraction model includes a first scale model, a second scale model, a third scale model and a fourth scale model. The structures of the four models are the same, which are composed of 4 interactive residual units that are gradually cascaded at multiple scales. The data of the 4 interactive residual units are Cat-concatenated and then subjected to 1×1 convolution to obtain the output.

[0148] The third feature extraction model is a dual-path global attention module. The upper branch extracts wide-area pixel information through two 7×7 Conv operations, and a hidden layer is set in the middle to adjust the number of channels to improve the network operation efficiency. The channel compression ratio r of the hidden layer is 8; the Sigmoid function is used to capture the input-output weight relationship, and the channel-by-channel point multiplication is weighted to the initial feature; the lower branch first obtains the aggregated feature through the downsampling operation, and then uses a multi-layer perceptron unit composed of two 1×1 Conv to adjust the feature map through the hidden layer to capture the multi-dimensional hierarchical dependency between image pixels. The compression ratio r is consistent with the upper branch; the Sigmoid function filters the output feature to between (0,1). Finally, the outputs of the upper and lower branches are added to obtain the fifth feature information.

[0149] The convolution model is 3×3 convolution.

[0150] Processing the sixth characteristic information to obtain denoised ultrasonic image data includes:

[0151]

[0152] in, is the denoised ultrasound image data, x r It is the sixth characteristic information.

[0153] Optionally, performing contrast enhancement processing on the denoised ultrasonic image data to obtain pre-processed pre-talofibular acoustic image data includes:

[0154] S231, decomposing the denoised ultrasonic image data to obtain base layer data and detail layer data;

[0155] Denoised ultrasound image data I(x,y) = base layer data I b (x,y)+Detail layer data I d (x,y)

[0156] I(x,y)=I b (x,y)+I d (x,y)

[0157] The base layer data is obtained by minimizing the following objective function

[0158]

[0159] The objective function consists of two terms: the first is a difference term adapted to the texture component to preserve meaningful structures; and the second is a regularization term based on the total variation, which will constrain image details to the detail layer. represents the gradient operator, and λ is set to twice the global noise estimate σ, that is, λ = 2σ.

[0160] The global noise estimate σ is calculated as:

[0161]

[0162] Where * represents the convolution operator. W and H are the width and height of the image I(x,y) respectively. N is the convolution template, defined as

[0163]

[0164] S232, performing illumination equalization processing on the base layer data to obtain enhanced base layer data;

[0165] Will I b (x, y) is converted to HSI color space to obtain intensity layer I i (x,y),I i (x,y) = L(x,y)·R(x,y), where L(x,y) is the illumination component and R(x,y) is the reflection component;

[0166] The enhanced illumination component is

[0167] L e (x,y)=L(x,y)·L sig (x,y)

[0168] L e (x,y) is the enhanced illumination component, r min and r max They represent the minimum and maximum values ​​of the reflected radiation component respectively, α=0.5~7.5, β=0.002~0.014.

[0169] The enhanced reflection component is

[0170] R e (x,y)=cR(x,y) γ

[0171] Where c is a constant, c = 0.3 and γ = 0.6.

[0172] I′ i (x,y)=L e (x,y)·R e (x,y)

[0173] Among them, I′ i (x, y) is the enhanced strength layer, and I′ i (x, y) is converted to RGB space to obtain the enhanced base layer data.

[0174] S233, performing enhancement processing on the detail layer data to obtain enhanced detail layer data;

[0175] ω(x,y)=|I d (x,y)|*G(x,y)

[0176] Where * represents the convolution operator and G(x,y) represents the Gaussian filter, defined as

[0177]

[0178] In this paper, the Gaussian standard deviation δ=21 is taken, ω(x,y) is the enhanced detail layer data, and x,y are the coordinates of the pixel.

[0179] S234, synthesizing the enhanced base layer data and the enhanced detail layer data to obtain pre-processed pre-talofibular acoustic image data.

[0180] Pre-processed pre-talar acoustic image data = enhanced base layer data + enhanced detail layer data

[0181] Optionally, performing data enhancement on the segmented ultrasound image data to obtain enhanced ultrasound image data includes:

[0182] The Conditional Generative Adversarial Network (CGAN) guides the data generation process by inputting random noise and label information. It consists of a generator network (G) and a discriminator network (D). The generator G generates samples with label features, and the discriminator D is used to distinguish between generated samples and real samples.

[0183] S41, using a preset generator network, processing the label information data and random noise to obtain a generated image;

[0184] S42, processing the segmented ultrasound image data and the generated image to obtain a cyclic correlation coefficient

[0185]

[0186] Among them, i and j represent the positions of the pixels, m and n are the length and width of the image, V1(i, j) is the segmented ultrasound image data, and V2(i, j) is the generated image.

[0187] S43, using the cyclic correlation coefficient, conduct adversarial training on the preset discriminator network and the preset generator network to obtain an optimized discriminator network and an optimized generator network; the training process is as follows.

[0188] (1) Random noise and label information data are input into the generator network G to obtain the generated image. The discriminator network D is fixed, and the cyclic correlation coefficients of the generated image and the label information data are calculated respectively. The maximum value of the cyclic correlation coefficients is selected to improve the loss function and optimize the parameters of the generator network G.

[0189] (2) Then fix the generator network G, and input the generated image, real image and label information data into the discriminator network D to judge the authenticity of the image, and improve the loss function according to the maximum value of the cyclic correlation coefficient between the image and the label image.

[0190] (3) Feed the results back to the generator network G and the discriminator network D, update the parameters, and repeat the above steps to balance the loss functions of the two and reduce the mismatch between the generated images and the labels.

[0191] S44, using the optimized generator network, processing the label information data and random noise to obtain an augmented image;

[0192] When the image is enlarged, the average value of the cyclic correlation coefficients in each label in the original data set is used as the threshold. When the cyclic correlation coefficient between the generated image and the corresponding label image exceeds this threshold, the image is output to obtain the enlarged image.

[0193] S45, integrating the amplified image and the segmented ultrasonic image data to obtain enhanced ultrasonic image data.

[0194] Optionally, the using of the three-dimensional reconstruction model to process the enhanced ultrasound image data to obtain the three-dimensional reconstruction model includes:

[0195] S51, using a three-dimensional reconstruction model, performing three-dimensional reconstruction on the enhanced ultrasound image data to obtain three-dimensional data information;

[0196] Optionally, the three-dimensional reconstruction model is a voxel-based three-dimensional reconstruction algorithm (VBM);

[0197] S52, visualizing the three-dimensional data information to obtain a three-dimensional reconstructed model.

[0198] Optionally, the three-dimensional data information is visualized by Smartbi visualization software or a graphical user interface designed by oneself.

[0199] Optionally, the processing of the three-dimensional reconstructed model to obtain puncture path planning information includes:

[0200] S61, analyzing the three-dimensional reconstructed model to obtain starting point coordinate information and ending point coordinate information;

[0201] S62, setting the cost function and constraint conditions of the path planning problem;

[0202] F tr =ω4f fu +ω5f hi +ω6f da

[0203] Where ω4, ω5, and ω6 are the path length costs f fu 、Path height cost f hi , risk cost da The weight factor, and ω4+ω5+ω6=1, F tr is the cost function.

[0204] S63, using Logistic chaos mapping to generate the initial population of particle swarm;

[0205] S64, using the artificial bee colony algorithm to perform iterative search and update the individual position to move it toward a lower fitness value;

[0206] S65, using chaotic mapping to search the local solution space to make it jump out of the local optimal value;

[0207] S66, repeat S64 and S65 until the maximum number of iterations is reached or the fitness value change threshold is reached;

[0208] S67, outputting the optimal fitness value and obtaining puncture path planning information.

[0209] It can be seen that the present invention discloses a method and device for intelligent puncture positioning of lesions of the anterior talofibular ligament using ultrasound images, which can achieve accurate identification of the anterior talofibular ligament and improve the accuracy of puncture positioning. By performing three-dimensional reconstruction and path planning, the intelligent navigation system can be assisted in planning the syringe puncture path, reducing the risk of puncture.

[0210] Embodiment 2

[0211] The steps of a method for intelligent puncture positioning of anterior talofibular ligament lesions using ultrasound images in this embodiment include:

[0212] 1. Intelligent identification of the anterior talofibular ligament

[0213] 1.1 Data collection and preprocessing

[0214] Data acquisition: A high-resolution ultrasound probe was used to obtain ultrasound imaging data of the anterior talofibular ligament.

[0215] Data annotation: Experienced ultrasound imaging physicians annotate the collected ultrasound image data, including the boundary of the anterior talofibular ligament and the lesion area (such as tear, swelling, etc.).

[0216] Data preprocessing: including denoising, contrast enhancement and normalization to improve image quality.

[0217] 1.2 Deep Learning Model Training

[0218] Model selection: Select a deep learning model suitable for medical image segmentation, such as U-Net, DeepLab, etc.

[0219] Training data: Use labeled ultrasound image data as the training set to train the deep learning model for accurate identification of the anterior talofibular ligament.

[0220] Data enhancement: Data enhancement technology is used to increase the diversity of training data and prevent model overfitting.

[0221] Loss function: Use the Dice coefficient loss function or combine it with the cross entropy loss function to optimize the model's segmentation accuracy for the lesion area.

[0222] 1.3 Model verification and optimization

[0223] Validation set test: Use an independent validation set to test model performance, and calculate indicators such as the Dice coefficient and IoU to evaluate the model segmentation accuracy.

[0224] Hyperparameter tuning: Adjust model hyperparameters based on validation set results to achieve the best performance.

[0225] Model integration: Model integration technology is used to further improve segmentation accuracy.

[0226] 2. Accurately guide the puncture path

[0227] 2.1 3D Reconstruction

[0228] Reconstructed model: A three-dimensional reconstructed model is generated based on the anterior talofibular ligament and lesion area segmented by the deep learning model.

[0229] Visualization: Visualize the 3D reconstructed model to provide an intuitive view of the anatomical structure.

[0230] 2.2 Puncture path planning

[0231] Path calculation: Use the fast marching method or similar path planning algorithms to calculate the optimal puncture path.

[0232] Multi-objective optimization: In the path planning process, multiple objectives (such as path length, angle, safety, etc.) are considered, and a multi-objective optimization algorithm is used for path optimization.

[0233] 2.3 Real-time Navigation

[0234] Real-time tracking: Track the syringe position through real-time ultrasound imaging and update the puncture path in real time.

[0235] Feedback mechanism: Establish a feedback mechanism to issue timely warnings and provide correction suggestions when the syringe deviates from the predetermined path.

[0236] User interface: Provides an intuitive user interface that displays real-time ultrasound images, puncture path, syringe position and other information.

[0237] 3. Device design

[0238] 3.1 Hardware Composition

[0239] Ultrasound probe: High-resolution ultrasound probe with high frame rate and good spatial resolution.

[0240] Computer System: High-performance computer system for running deep learning models and path planning algorithms.

[0241] Syringe Positioning Module: Equipped with high-precision sensors to track syringe position in real time.

[0242] User interface equipment: including monitor, keyboard, mouse, etc., providing an operating interface.

[0243] 3.2 Software composition

[0244] Data acquisition software: controls the ultrasound probe for data acquisition.

[0245] Deep learning model: A trained deep learning model for accurate identification of the anterior talofibular ligament.

[0246] 3D reconstruction module: Generates a 3D reconstruction model based on the segmentation results.

[0247] Path planning module: calculates the optimal puncture path.

[0248] Real-time navigation module: tracks the syringe position in real time and provides navigation information.

[0249] User interface software: provides an operation interface to display ultrasound images, puncture paths, syringe positions and other information.

[0250] It can be seen that the present invention can accurately identify: using deep learning technology to achieve accurate identification of the anterior talofibular ligament, improving the accuracy of puncture positioning. Intelligent navigation: planning the syringe puncture path through the intelligent navigation system, reducing the risk of puncture. Real-time feedback: real-time tracking of the syringe position, providing navigation information, to ensure puncture accuracy. User-friendly: providing an intuitive user interface to help doctors perform accurate punctures.

[0251] Example 3, please refer to Figure 4 , Figure 41 is a schematic diagram of the structure of an intelligent puncture positioning device for lesions of the anterior talofibular ligament using ultrasound imaging disclosed in an embodiment of the present invention. Figure 4 The described intelligent puncture positioning device for lesions of the anterior talofibular ligament based on ultrasound images is applied in the field of medical image processing technology, and the embodiments of the present invention are not limited thereto. Figure 4 As shown, the intelligent puncture positioning device for lesions of the anterior talofibular ligament ultrasound image may include the following operations:

[0252] S301, a data acquisition module, configured to acquire an ultrasound image data set of anterior talofibular ligament; the ultrasound image data set of anterior talofibular ligament includes N ultrasound image data of anterior talofibular ligament, where N is a positive integer;

[0253] S302, a preprocessing module, used to preprocess the ultrasound image data of the anterior talofibular ligament to obtain preprocessed ultrasound image data;

[0254] S303, an image segmentation module, used to segment the pre-processed ultrasound image data to obtain segmented ultrasound image data;

[0255] S304, a data enhancement module, used to perform data enhancement on the segmented ultrasound image data to obtain enhanced ultrasound image data;

[0256] S305, a three-dimensional reconstruction module, used to process the enhanced ultrasound image data using the three-dimensional reconstruction model to obtain a three-dimensional reconstruction model;

[0257] S305, a path planning module is used to process the three-dimensional reconstructed model to obtain puncture path planning information.

[0258] It can be seen that the present invention discloses a method and device for intelligent puncture positioning of lesions of the anterior talofibular ligament using ultrasound images, which can achieve accurate identification of the anterior talofibular ligament and improve the accuracy of puncture positioning. By performing three-dimensional reconstruction and path planning, the intelligent navigation system can be assisted in planning the syringe puncture path, reducing the risk of puncture.

[0259] Embodiment 4

[0260] See also Figure 5 , Figure 5 1 is a schematic diagram of another intelligent puncture positioning device for lesions of the anterior talofibular ligament using ultrasound imaging disclosed in an embodiment of the present invention. Figure 5 The described intelligent puncture positioning device for lesions of the anterior talofibular ligament based on ultrasound images is applied in the field of medical image processing technology, and the embodiments of the present invention are not limited thereto. Figure 5 As shown, the intelligent puncture positioning device for lesions of the anterior talofibular ligament ultrasound image may include the following operations:

[0261] A memory 401 storing executable program codes;

[0262] a processor 402 coupled to the memory 401;

[0263] The processor 402 calls the executable program code stored in the memory 401 to execute the steps of the method for intelligent puncture positioning of the anterior talofibular ligament lesion using ultrasound imaging described in the first and second embodiments.

[0264] Embodiment 5

[0265] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the method for intelligent puncture localization of lesions in the anterior talofibular ligament ultrasound imaging described in Embodiments 1 and 2.

[0266] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0267] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0268] Finally, it should be noted that the method and device for intelligent puncture positioning of lesions in the anterior talofibular ligament by ultrasonic imaging disclosed in the embodiment of the present invention only discloses a preferred embodiment of the present invention, which is only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical schemes described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the various embodiments of the present invention.

Claims

1. A method for intelligent puncture location of lesions of the anterior talofibular ligament using ultrasound imaging, characterized in that: The method comprises: S1, obtaining an ultrasound image dataset of the anterior talofibular ligament; the ultrasound image dataset of the anterior talofibular ligament includes N ultrasound image data of the anterior talofibular ligament, where N is a positive integer; S2, preprocessing the ultrasound image data of the anterior talofibular ligament to obtain preprocessed ultrasound image data; S3, segmenting the preprocessed ultrasonic image data to obtain segmented ultrasonic image data; S4, performing data enhancement on the segmented ultrasonic image data to obtain enhanced ultrasonic image data; S5, using the three-dimensional reconstruction model, processing the enhanced ultrasound image data to obtain a three-dimensional reconstruction model; S6, processing the three-dimensional reconstructed model to obtain puncture path planning information.

2. The method for intelligent puncture and localization of lesions of the anterior talofibular ligament using ultrasound imaging according to claim 1, characterized in that: The preprocessing of the ultrasound image data of the anterior talofibular ligament to obtain preprocessed ultrasound image data includes: S21, annotating the ultrasound image data of the anterior talofibular ligament to obtain label information data; S22, performing denoising processing on the ultrasonic image data of the anterior talofibular ligament to obtain denoised ultrasonic image data; S23, performing contrast enhancement processing on the denoised ultrasonic image data to obtain pre-processed pre-talofibular acoustic image data.

3. The method for intelligent puncture and localization of lesions of the anterior talofibular ligament using ultrasound imaging according to claim 2, characterized in that: The denoising process is performed on the ultrasound image data of the anterior talofibular ligament to obtain denoised ultrasound image data, including: S221, using a first feature extraction model to process the ultrasound image data of the anterior talofibular ligament to obtain first feature information; S222, down-sampling the first feature information to obtain second feature information; S223, using a second feature extraction model, processing the second feature information to obtain third feature information; S224, upsampling the third feature information to obtain fourth feature information; S225, using the third feature extraction model, processing the fourth feature information to obtain fifth feature information; S226, using a convolution model, processing the fifth feature information to obtain sixth feature information; S227: Process the sixth characteristic information to obtain denoised ultrasonic image data.

4. The method for intelligent puncture and localization of lesions of the anterior talofibular ligament using ultrasound imaging according to claim 2, characterized in that: The step of performing contrast enhancement processing on the denoised ultrasonic image data to obtain pre-processed pre-talofibular acoustic image data includes: S231, decomposing the denoised ultrasonic image data to obtain base layer data and detail layer data; S232, performing illumination equalization processing on the base layer data to obtain enhanced base layer data; S233, performing enhancement processing on the detail layer data to obtain enhanced detail layer data; S234, synthesizing the enhanced base layer data and the enhanced detail layer data to obtain pre-processed pre-talofibular acoustic image data.

5. The method for intelligent puncture and localization of lesions of the anterior talofibular ligament using ultrasound imaging according to claim 1, characterized in that: The step of performing data enhancement on the segmented ultrasonic image data to obtain enhanced ultrasonic image data includes: S41, using a preset generator network, processing the label information data and random noise to obtain a generated image; S42, processing the segmented ultrasound image data and the generated image to obtain a cyclic correlation coefficient; S43, using the cyclic correlation coefficient, performing adversarial training on a preset discriminator network and a preset generator network to obtain an optimized discriminator network and an optimized generator network; S44, using the optimized generator network, processing the label information data and random noise to obtain an augmented image; S45, integrating the amplified image and the segmented ultrasonic image data to obtain enhanced ultrasonic image data.

6. The method for intelligent puncture and localization of lesions of the anterior talofibular ligament using ultrasound imaging according to claim 1, characterized in that: The method of processing the enhanced ultrasound image data using the three-dimensional reconstruction model to obtain the three-dimensional reconstruction model includes: S51, using a three-dimensional reconstruction model, performing three-dimensional reconstruction on the enhanced ultrasound image data to obtain three-dimensional data information; S52, visualizing the three-dimensional data information to obtain a three-dimensional reconstructed model.

7. The method for intelligent puncture and localization of lesions of the anterior talofibular ligament using ultrasound imaging according to claim 1, characterized in that: The three-dimensional reconstructed model is processed to obtain puncture path planning information, including: S61, analyzing the three-dimensional reconstructed model to obtain starting point coordinate information and ending point coordinate information; S62, setting the cost function and constraint conditions of the path planning problem; S63, using Logistic chaos mapping to generate the initial population of particle swarm; S64, using the artificial bee colony algorithm to perform iterative search and update the individual position to move it toward a lower fitness value; S65, using chaotic mapping to search the local solution space to make it jump out of the local optimal value; S66, repeat S64 and S65 until the maximum number of iterations is reached or the fitness value change threshold is reached; S67, outputting the optimal fitness value and obtaining puncture path planning information.

8. An intelligent puncture positioning device for lesions of the anterior talofibular ligament using ultrasound imaging, characterized in that: The device comprises: A data acquisition module, used to acquire an ultrasound image data set of the anterior talofibular ligament; the ultrasound image data set of the anterior talofibular ligament includes N ultrasound image data of the anterior talofibular ligament, where N is a positive integer; A preprocessing module, used for preprocessing the ultrasonic image data of the anterior talofibular ligament to obtain preprocessed ultrasonic image data; An image segmentation module, used for segmenting the preprocessed ultrasonic image data to obtain segmented ultrasonic image data; A data enhancement module, used for performing data enhancement on the segmented ultrasonic image data to obtain enhanced ultrasonic image data; A three-dimensional reconstruction module, used to process the enhanced ultrasound image data using a three-dimensional reconstruction model to obtain a three-dimensional reconstruction model; The path planning module is used to process the three-dimensional reconstructed model to obtain puncture path planning information.

9. An intelligent puncture positioning device for lesions of the anterior talofibular ligament using ultrasound imaging, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the method for intelligent puncture positioning of lesions in the anterior talofibular ligament ultrasound imaging as described in any one of claims 1-7.

10. A computer storable medium, characterized in that: The computer storable medium stores computer instructions, and when the computer instructions are called, they are used to execute the method for intelligent puncture positioning of lesions in the anterior talofibular ligament ultrasound imaging as described in any one of claims 1-7.

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