WO2020087838A1 - 血管壁斑块识别设备、***、方法及存储介质 - Google Patents
血管壁斑块识别设备、***、方法及存储介质 Download PDFInfo
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
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- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
Definitions
- the present invention belongs to the field of medical technology, and particularly relates to a device, system, method and storage medium for identifying blood vessel wall plaque.
- Magnetic resonance imaging is currently the only non-invasive imaging method that can clearly display atherosclerotic plaques throughout the body.
- MRI of the blood vessel wall can not only quantitatively analyze systemic plaques such as intracranial arteries, carotid arteries and aorta, but also accurately identify unstable features such as fibrous caps, hemorrhage, calcification, lipid nuclei, inflammation, etc. , Is currently recognized as the best plaque imaging method.
- the purpose of the present invention is to provide a blood vessel wall plaque identification device, system, method and storage medium, aiming to solve the existing technology, the artificial identification of blood vessel wall plaque caused by low efficiency and recognition accuracy cannot be effectively guaranteed The problem.
- the present invention provides a vessel wall plaque identification device, including: a memory and a processor, the processor implementing the computer program stored in the memory to achieve the following steps:
- a deep learning method is used to identify the plaques in the MRI image.
- the deep learning method is used to identify the plaque in the MRI image, which specifically includes the following steps:
- the initial image is input to a deep learning neural network to recognize the plaque, and a recognition result is obtained.
- inputting the initial image into a deep learning neural network to identify the plaque specifically includes the following steps:
- the residual convolutional neural network includes a convolutional network layer, an activation function network layer and a batch normalization network layer.
- the adjustment standard is used to process the batch of standard data to obtain batch adjustment data having the same or similar distribution as the input batch data for output.
- the present invention provides a blood vessel wall plaque identification system, the system includes:
- An acquisition module for acquiring magnetic resonance MRI images of blood vessel walls and,
- the recognition module is used to recognize the plaque in the MRI image by using a deep learning method.
- the identification module specifically includes:
- a preprocessing module for preprocessing the MRI image to obtain an initial image
- the deep learning module is used for inputting the initial image to the deep learning neural network to identify the plaque, and obtain a recognition result.
- the deep learning module specifically includes:
- a convolution module used to perform feature extraction processing on the initial image to obtain a convolution feature image
- a candidate frame module used to determine candidate regions for the convolutional feature image, and correspondingly obtain a fully connected feature map
- a fully connected module is used to classify based on the fully connected feature map to obtain the recognition result.
- the present invention also provides a blood vessel wall plaque identification method, the method includes the following steps:
- a deep learning method is used to identify the plaques in the MRI image.
- the present invention also provides a computer-readable storage medium that stores a computer program, and when the computer program is executed by a processor, the steps in the foregoing method are implemented.
- a magnetic resonance MRI image of a blood vessel wall is obtained; a plaque in the MRI image is identified using a deep learning method.
- the deep learning method is used to identify the plaque of the blood vessel wall, which can greatly reduce the labor and improve the accuracy of the plaque recognition, thereby improving the recognition efficiency and ensuring the recognition accuracy.
- FIG. 1 is a schematic structural diagram of a blood vessel wall plaque identification device according to Embodiment 1 of the present invention.
- FIG. 2 is a flowchart of a method implemented by a processor in Embodiment 2 of the present invention
- Embodiment 3 is a schematic structural diagram of a deep learning neural network in Embodiment 3 of the present invention.
- Embodiment 4 is a processing flowchart of a deep learning neural network in Embodiment 3 of the present invention.
- FIG. 5 is a schematic structural diagram of a residual convolutional neural network in Embodiment 4 of the present invention.
- Embodiment 6 is a flowchart of processing of a batch-normalized network layer in Embodiment 5 of the present invention.
- FIG. 7 is a schematic structural diagram of a blood vessel wall plaque identification system according to Embodiment 6 of the present invention.
- Embodiment 8 is a schematic structural diagram of an identification module in Embodiment 7 of the present invention.
- Embodiment 8 of the present invention is a schematic structural diagram of a deep learning module in Embodiment 8 of the present invention.
- FIG. 10 is a processing flowchart of a method for identifying a plaque on a blood vessel wall according to Embodiment 10 of the present invention.
- FIG. 11 is a schematic structural diagram of a deep learning neural network according to an application example of the present invention.
- FIG. 1 shows a blood vessel wall plaque identification device provided in Embodiment 1 of the present invention.
- the device is mainly used to: intelligently identify plaque in a blood vessel wall MRI image using artificial intelligence (Artificial Intelligence) technology.
- the device may be a separate computer, chip, or may be physically integrated with other devices, for example, integrated with an MRI device, or may be represented as a cloud server.
- Blood vessel wall plaque can be roughly divided into stable plaque and unstable plaque. Unstable plaque is easy to fall off from the blood vessel wall and cause thrombosis.
- Unstable plaque has fibrous cap, hemorrhage, calcification, lipid nuclei, inflammation and other instability Sexual characteristics, when using AI technology to identify blood vessel wall plaque, not only can identify the presence of blood vessel wall plaque, but also can identify the type of blood vessel wall plaque. For ease of description, only parts related to the embodiments of the present invention are shown, which are described in detail as follows:
- the blood vessel wall plaque recognition device includes: a memory 101 and a processor 102.
- the processor executes the computer program 103 stored in the memory 101, the following steps are achieved: first, an MRI image of a blood vessel wall is obtained, and then a deep learning method is used to detect the spot in the MRI image Block identification.
- the device in order to realize the transmission of data and signaling such as images, the device may further include a network module; in order to realize the output of recognition results, the device may also include an output module such as a display screen; in order to realize manual control, The device may also include input modules such as a mouse and a keyboard.
- An MRI image of a blood vessel wall usually refers to a blood vessel wall slice image.
- any suitable deep learning method can be used to identify the plaque in the MRI image of the blood vessel wall, for example: regional convolutional neural network (Regions with Convolutional Neural Network, R-CNN), fast region Convolutional neural network (Fast R-CNN), multi-class single shot detector (Single Shot MultiBox Detector, SSD), etc.
- R-CNN regional convolutional neural network
- Fast R-CNN fast region Convolutional neural network
- SSD single shot detector
- the deep learning method is used to identify the plaque of the blood vessel wall, which can greatly reduce the labor and improve the accuracy of plaque recognition, thereby improving the recognition efficiency and ensuring the recognition accuracy.
- the use of MRI to carry out a comprehensive and accurate image evaluation of ischemic stroke-related vascular bed plaques, and the use of artificial intelligence for rapid and accurate diagnosis, is of great significance for the screening and etiological exploration of high-risk stroke population to prevent recurrence.
- this embodiment further provides the following content:
- step S201 the above MRI image is preprocessed to obtain an initial image.
- preprocessing may involve cropping the image to reduce redundant calculations.
- step S202 the initial image is input to the deep learning neural network to perform patch recognition, and a recognition result is obtained.
- the architecture of the deep learning neural network may adopt R-CNN architecture, Fast R-CNN architecture, Accelerated Regional Convolutional Neural Network (Faster R-CNN) architecture, SSD architecture, and masked area convolutional neural network. (Mask R-CNN) architecture, etc.
- this embodiment further provides the following content:
- the deep learning neural network specifically includes: a convolution subnetwork 301, a candidate frame subnetwork 302, and a fully connected subnetwork 303.
- each sub-network processing is roughly as follows, and each sub-network processing corresponds to the detailed flow of the above step S202:
- the convolution sub-network 301 can perform step S401 shown in FIG. 4 to perform feature extraction processing on the initial image to obtain a convolution feature image.
- the convolution subnetwork 301 may include a multi-segment convolutional neural network, and each segment of the convolutional neural network may use a residual convolutional neural network to alleviate the problems of gradient disappearance and gradient explosion, or non-residual convolutions.
- a convolutional neural network of course, the convolution subnetwork 301 may also use a combination of a non-residual convolutional neural network and a residual convolutional neural network.
- the candidate frame sub-network 302 may perform step S402 shown in FIG. 4 to determine candidate regions for the convolutional feature image, and correspondingly obtain a fully connected feature map.
- the candidate frame sub-network 302 may adopt a sliding window of a predetermined size, and based on the center point of each sliding window, generate a predetermined number of candidate frames with a predetermined size on the initial image. The center point of the sliding window corresponds.
- candidate regions corresponding to each candidate frame can be obtained. Each candidate region correspondingly generates a candidate region feature map.
- Candidate region feature maps can also be pooled accordingly to obtain fully connected feature maps.
- the fully-connected sub-network 303 may perform step S403 shown in FIG. 4, perform classification and other processing based on the fully-connected feature map, and obtain a recognition result, and the recognition result indicates whether there is a blood vessel wall plaque.
- the two branches of the fully-connected sub-network 303 can be respectively subjected to corresponding classification, regression and other processing.
- the corresponding fully-connected sub-network 303 can correspondingly include a classification network layer and a regression network layer.
- the classification network layer can be used to determine whether the candidate area is the foreground or the background according to the fully connected feature map, that is, whether there is a blood vessel wall plaque in the candidate area.
- the regression network layer can be used to correct the coordinates of the candidate frame and finally determine the location of the plaque.
- the area-based convolutional neural network is used to recognize the plaque of the blood vessel wall, which can improve the accuracy of the recognition and facilitate the application of AI artificial intelligence diagnosis using medical images.
- this embodiment further provides the following content:
- the residual convolutional neural network may include multiple network layers as shown in FIG. 5: Convolutional network layer 501, activation function network layer 502, and batch normalization network layer 503. Among them, each network layer processing is roughly as follows:
- the convolutional network layer 501 can use a preset convolution kernel to perform convolution processing on the input image.
- the activation function network layer 502 may use an S-type (Sigmoid) function, a hyperbolic tangent (Tahn) function, or a rectified linear unit (ReLU) function to perform activation processing.
- Sigmoid S-type
- Tihn hyperbolic tangent
- ReLU rectified linear unit
- the batch normalization network layer 503 can not only realize the traditional standardization process, but also enable the network to accelerate convergence and further alleviate the problems of gradient disappearance and gradient explosion.
- this embodiment further provides the following content:
- processing of the batch normalized network layer 503 may specifically include the steps shown in FIG. 6:
- step S601 the input batch data processed through the convolutional network layer 501 are averaged.
- step S602 the variance of the batch data is calculated according to the mean.
- step S603 the batch data is standardized according to the mean and variance to obtain batch standard data.
- step S604 the batch standard data is processed using an adjustment factor to obtain batch adjustment data having the same or similar distribution as the input batch data for output.
- the adjustment factor has a corresponding initial value during initialization, and then based on the initial value, the adjustment factor can be trained together with the parameters processed by the network layer in the reverse transmission, so that the adjustment factor can learn the input batch The distribution of data. After the input batch data is processed by batch normalization, the distribution of the original input batch data remains.
- FIG. 7 correspondingly shows the blood vessel wall plaque recognition system provided in Embodiment 6 of the present invention.
- the system is also mainly used to: use AI technology to intelligently recognize the plaque in the blood vessel wall MRI image.
- the system may be a separate Computers and chips can also be in the form of a group of computers or a chipset formed by cascading chips. For ease of explanation, only parts related to the embodiments of the present invention are shown, and the details are as follows:
- the blood vessel wall plaque identification system includes:
- An acquisition module 701 for acquiring magnetic resonance MRI images of blood vessel walls and,
- the recognition module 702 is used to recognize the plaque in the MRI image by using the deep learning method.
- this embodiment further provides the following content:
- the identification module 702 specifically includes the structure shown in FIG. 8:
- the preprocessing module 801 is used to preprocess the MRI image to obtain the initial image;
- the deep learning module 802 is used to input the initial image to the deep learning neural network to perform patch recognition and obtain a recognition result.
- this embodiment further provides the following content:
- the deep learning module 802 specifically includes the structure shown in FIG. 9:
- the convolution module 901 is used to perform feature extraction processing on the initial image to obtain a convolution feature image
- the candidate frame module 902 is used to determine candidate regions for the convolutional feature image and obtain a fully connected feature map accordingly;
- the fully connected module 903 is used to classify based on the fully connected feature map and obtain a recognition result.
- this embodiment further provides the following content:
- the convolution module 901 may specifically use several residual convolutional neural networks to perform feature extraction processing on the initial image.
- the residual convolutional neural network may include a convolutional network layer 501, an activation function network layer 502, and a batch normalization network layer 503, which are still shown in FIG. The specific processing of each network layer will not be repeated.
- FIG. 10 correspondingly shows the blood vessel wall plaque identification method provided in Embodiment 10 of the present invention.
- the method specifically includes the following steps:
- step S1001 an MRI image of the blood vessel wall is obtained.
- step S1002 a deep learning method is used to identify the plaque in the MRI image.
- each step may be similar to the content described in the corresponding positions in the foregoing embodiments, and will not be repeated here.
- a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented. The steps S1001 to S1002 shown. Alternatively, when the computer program is executed by the processor, the functions described in the foregoing system embodiments are realized, for example, the functions of the aforementioned deep learning neural network.
- the computer-readable storage medium in the embodiments of the present invention may include any entity or device capable of carrying computer program code, and a recording medium, such as ROM / RAM, magnetic disk, optical disk, flash memory, and other memories.
- the deep learning neural network can be used to identify plaques on blood vessel walls, and may specifically include the architecture shown in FIG. 11:
- the entire deep learning neural network includes: a convolution subnetwork 301, a candidate box subnetwork 302, and a fully connected subnetwork 303.
- the convolution subnetwork 501 includes a first-stage convolutional neural network 1101, a pooling layer 1102, a second-stage convolutional neural network 1103, a third-stage convolutional neural network 1104, and a fourth-stage convolutional neural network 1105.
- the first segment of the convolutional neural network 1101 uses a non-residual convolutional neural network
- the second segment of the convolutional neural network 1103, the third segment of the convolutional neural network 1104 and the fourth segment of the convolutional neural network 1105 use the residual Product neural network.
- the residual convolutional neural network includes multiple network layers, still shown in FIG. 5: a convolutional network layer 501, an activation function network layer 502, and a batch normalization network layer 503.
- the candidate frame sub-network 302 includes: a regional candidate network (Region Proposal Network, RPN) 1106 and a regional pooling network 1107.
- RPN Regional Proposal Network
- the fully connected sub-network 303 includes a classification network layer 1108 and a regression network layer 1109.
- a fifth segment convolutional neural network 1111 is also included between the candidate box subnetwork 302 and the fully connected subnetwork 303.
- a mask network layer 1110 is also set.
- an initial image of size 224 ⁇ 224 is obtained.
- the MRI image of the blood vessel wall here is usually a slice image.
- the initial image is input to the first segment of the convolutional neural network 1101 for initial feature extraction of the convolution calculation.
- the resulting feature map is processed by the pooling layer 1102, and then output to the second segment of the convolutional neural network 1103, the third
- the segment convolutional neural network 1104 and the fourth segment convolutional neural network 1105 perform further feature extraction.
- the size of the convolution kernel used in the first-stage convolutional neural network 1101 for convolution calculation is 7 ⁇ 7, and the step size is 2, which can reduce the data size by half.
- the size of the feature map output by the first-stage convolutional neural network 1101 is 112 ⁇ 112. After the feature map output by the first segment of the convolutional neural network 1101 is processed by the pooling layer 1102, the size of the feature map is 56 ⁇ 56.
- the convolutional network layer 501 in the residual convolutional neural network used can be calculated using the following formula (1):
- i, j are the pixel coordinate positions of the input image
- I is the input image data
- K is the convolution kernel
- p, n are the width and height of the convolution kernel
- S (i, j) is the output convolution data .
- the batch normalized network layer 503 can perform the following calculations:
- the input batch data ⁇ x 1 ... m is the output data of the convolutional network layer 501.
- n is the total number of data.
- ⁇ is a small positive number to avoid the divisor being zero.
- ⁇ is the scaling factor and ⁇ is the translation factor.
- the adjustment factors ⁇ and ⁇ have corresponding initial values during initialization.
- the initial value of ⁇ is approximately equal to 1
- the initial value of ⁇ is approximately equal to 0, and then based on This initial value, the adjustment factors ⁇ and ⁇ can be trained together with the parameters processed by the network layer in the reverse transmission, so that ⁇ and ⁇ learn the distribution of the input batch data, and the input batch data is batch normalized After processing, the distribution of the batch data originally entered is still retained.
- the activation function network layer 502 can perform the calculation shown in the following formula (6):
- x is the output data of the batch normalized network layer 503
- f (x) is the output of the activation function network layer 502.
- the above three operations of the convolutional network layer 501, the activation function network layer 502, and the batch normalization network layer 503 can form a neural network block.
- the second segment of the convolutional neural network 1103 has 3 neural network blocks. Among them, the size of the convolution kernel used in one neural network block is 1 ⁇ 1, and the number of convolution kernels is 64; The size of the convolution kernel used is 3 ⁇ 3, and the number of convolution kernels is 64; there is also a convolution kernel size used in the neural network block of 1 ⁇ 1, and the number of convolution kernels is 256.
- the third segment of the convolutional neural network 1104 has 4 neural network blocks, of which the size of the convolution kernel used in one neural network block is 1 ⁇ 1 and the number of convolution kernels is 128; The size of the convolution kernel used is 3 ⁇ 3, and the number of convolution kernels is 128; there is also a convolution kernel size used in the neural network block of 1 ⁇ 1, and the number of convolution kernels is 512.
- the fourth segment of the convolutional neural network 1105 has 23 neural network blocks.
- the size of the convolution kernel used in one neural network block is 1 ⁇ 1, and the number of convolution kernels is 256;
- the size of the convolution kernel used is 3 ⁇ 3, and the number of convolution kernels is 256;
- the size of the convolution kernel used in another neural network block is 1 ⁇ 1, and the number of convolution kernels is 1024.
- the output convolution feature image is 14 ⁇ 14 ⁇ 1024, indicating that the output convolution feature image size is 14 ⁇ 14, and the number of convolution kernels is 1024.
- the convolution feature image processed by the convolution sub-network 301 is then input into the RPN 1106 and the regional pooling network 1107 for corresponding processing.
- RPN1106 is used to extract candidate regions. Specifically, a sliding window with a predetermined size of 3 ⁇ 3 is used. Based on the center point of each sliding window, a predetermined number of 9 candidate frames with a predetermined size are generated on the initial image. Each candidate The center point of the frame corresponds to the center point of the sliding window. Correspondingly, candidate regions corresponding to each candidate frame can be obtained. Each candidate region correspondingly generates a candidate region feature map.
- the output convolutional feature image is 14 ⁇ 14 ⁇ 1024
- the predetermined size of the sliding window is 3 ⁇ 3
- the predetermined number of candidate frames is 9, then 256 can be obtained accordingly
- candidate region feature maps that is, 256-dimensional fully connected features.
- the area size of some candidate frames is the same, and the area size of this partial candidate frame is different from the area size of other partial candidate frames.
- the area and aspect ratio of the candidate frames can be obtained according to the settings.
- the area pooling network 1107 is used to pool the candidate area feature map into a fixed-size pooling feature map according to the position coordinates of the candidate frame.
- the regional pooling network 1107 can be RoiAlign network.
- the candidate box is derived from the regression model, which is generally a floating-point number.
- the RoiAlign network does not quantize floating-point numbers. For each candidate box, divide the candidate region feature map into 7 ⁇ 7 units, fix four coordinate positions in each unit, calculate the values of the four positions by bilinear interpolation, and then perform the maximum pooling operation . For each candidate box, a pooled feature map of 7 ⁇ 7 ⁇ 1024 is obtained, and all pooled feature maps constitute the initial fully connected feature map.
- the fifth segment convolutional neural network 1111 has 3 neural network blocks, of which the size of the convolution kernel used in one neural network block is 1 ⁇ 1 and the number of convolution kernels is 512; The size of the convolution kernel used is 3 ⁇ 3, and the number of convolution kernels is 512; there is also a convolution kernel size used in the neural network block of 1 ⁇ 1, and the number of convolution kernels is 2048.
- the final fully-connected feature map processed by the fifth-stage convolutional neural network 1111 enters three branches of the fully-connected sub-network 803: a classification network layer 1108, a regression network layer 1109, and a mask network layer 1110.
- the classification network layer 1108 is used to input the final fully connected feature map processed by the fifth segment convolutional neural network 1111, and to judge whether the candidate area is the foreground or the background, and the output is a 14 ⁇ 14 ⁇ 18 array, where “18 "Means that the nine candidate boxes will output both foreground and background results.
- the regression network layer 1209 is used to predict the coordinates, height and width of the center anchor point of the candidate frame, and to modify the coordinates of the candidate frame.
- the output is 14 ⁇ 14 ⁇ 36, where “36” represents the four endpoint values of the nine candidate frames.
- the mask network layer 1110 uses a 2 ⁇ 2 convolution kernel of a certain size to upsample the feature map of the candidate area that is determined to be a calcification and has undergone position correction, to obtain a 14 ⁇ 14 ⁇ 256 feature map.
- the convolution process obtains a 14 ⁇ 14 ⁇ 2 feature map, which is then masked to segment the foreground and background.
- the number of categories is 2, indicating the presence or absence of breast calcifications.
- the location of the calcifications can be further obtained.
- the calculation of the classification network layer loss function used in the fully connected subnetwork 303 to optimize the classification is shown in the following formula (7), which is used to optimize the regression when the classification result is the presence of calcified foci
- the calculation of the regression network layer loss function is shown in the following formula (8).
- the value of b is (ti-ti '), ti is the predicted coordinate, and ti' is the real coordinate.
- the optimization processing of the mask processing may involve: in the classification processing, the cross entropy is calculated after the activation function Sigmoid processing.
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Abstract
Description
Claims (10)
- 一种血管壁斑块识别设备,其特征在于,包括:存储器及处理器,所述处理器执行所述存储器中存储的计算机程序时实现如下步骤:获得血管壁磁共振MRI图像;利用深度学习方法对所述MRI图像中的斑块进行识别。
- 如权利要求1所述的设备,其特征在于,利用深度学习方法对所述MRI图像中的斑块进行识别,具体包括下述步骤:对所述MRI图像进行预处理,得到初始图像;将所述初始图像输入至深度学习神经网络进行所述斑块的识别,得到识别结果。
- 如权利要求2所述的设备,其特征在于,将所述初始图像输入至深度学习神经网络进行所述斑块的识别,具体包括下述步骤:对所述初始图像进行特征提取处理,得到卷积特征图像;对所述卷积特征图像确定候选区域,相应得到全连接特征图;基于所述全连接特征图进行分类,得到所述识别结果。
- 如权利要求3所述的设备,其特征在于,对所述初始图像进行特征提取处理,得到卷积特征图像,具体为:采用若干残差卷积神经网络对所述初始图像进行特征提取处理,其中,所述残差卷积神经网络中包括卷积网络层、激活函数网络层及批量归一化网络层。
- 如权利要求4所述的设备,其特征在于,采用若干残差卷积神经网络对所述初始图像进行特征提取处理,具体包括下述步骤:通过所述批量归一化网络层对输入的批量数据求均值;根据所述均值求所述批量数据的方差;根据所述均值及所述方差,对所述批量数据进行标准化处理,得到批量标准数据;采用调整因子对所述批量标准数据进行处理,得到具有与输入的所述批量数据的分布相同或类似的批量调整数据以进行输出。
- 一种血管壁斑块识别***,其特征在于,所述***包括:获取模块,用于获得血管壁磁共振MRI图像;以及,识别模块,用于利用深度学习方法对所述MRI图像中的斑块进行识别。
- 如权利要求6所述的***,其特征在于,所述识别模块具体包括:预处理模块,用于对所述MRI图像进行预处理,得到初始图像;以及,深度学习模块,用于将所述初始图像输入至深度学习神经网络进行所述斑块的识别,得到识别结果。
- 如权利要求7所述的***,其特征在于,所述深度学习模块具体包括:卷积模块,用于对所述初始图像进行特征提取处理,得到卷积特征图像;候选框模块,用于对所述卷积特征图像确定候选区域,相应得到全连接特征图;以及,全连接模块,用于基于所述全连接特征图进行分类,得到所述识别结果。
- 一种血管壁斑块的识别方法,其特征在于,所述方法包括下述步骤:获得血管壁磁共振MRI图像;利用深度学习方法对所述MRI图像中的斑块进行识别。
- 一种计算机可读存储介质,所述计算机可读存储介质存储有计算机程序,其特征在于,所述计算机程序被处理器执行时实现如权利要求9所述方法中的步骤。
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