CN117351012B - Fetal image recognition method and system based on deep learning - Google Patents
Fetal image recognition method and system based on deep learning Download PDFInfo
- Publication number
- CN117351012B CN117351012B CN202311643319.1A CN202311643319A CN117351012B CN 117351012 B CN117351012 B CN 117351012B CN 202311643319 A CN202311643319 A CN 202311643319A CN 117351012 B CN117351012 B CN 117351012B
- Authority
- CN
- China
- Prior art keywords
- data set
- image recognition
- fetal
- deep learning
- fetal image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000001605 fetal effect Effects 0.000 title claims abstract description 86
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000013135 deep learning Methods 0.000 title claims abstract description 18
- 238000012549 training Methods 0.000 claims abstract description 49
- 230000009467 reduction Effects 0.000 claims abstract description 45
- 238000013136 deep learning model Methods 0.000 claims abstract description 27
- 238000012545 processing Methods 0.000 claims abstract description 19
- 239000011159 matrix material Substances 0.000 claims abstract description 12
- 230000004913 activation Effects 0.000 claims abstract description 7
- 230000006835 compression Effects 0.000 claims abstract description 7
- 238000007906 compression Methods 0.000 claims abstract description 7
- 238000012216 screening Methods 0.000 claims abstract description 4
- 230000009466 transformation Effects 0.000 claims abstract description 4
- 238000012360 testing method Methods 0.000 claims description 9
- 238000007781 pre-processing Methods 0.000 claims description 8
- 238000002604 ultrasonography Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000009499 grossing Methods 0.000 claims description 6
- 230000005855 radiation Effects 0.000 claims description 6
- 238000003745 diagnosis Methods 0.000 claims description 5
- 230000011218 segmentation Effects 0.000 claims description 5
- 238000011946 reduction process Methods 0.000 abstract description 2
- 210000003754 fetus Anatomy 0.000 description 9
- 238000010586 diagram Methods 0.000 description 6
- 238000004590 computer program Methods 0.000 description 5
- 238000013527 convolutional neural network Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000003860 storage Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000002059 diagnostic imaging Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 2
- 238000010191 image analysis Methods 0.000 description 2
- 238000003709 image segmentation Methods 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000008774 maternal effect Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 238000004393 prognosis Methods 0.000 description 1
- 230000002285 radioactive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
- 230000007723 transport mechanism Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/32—Normalisation of the pattern dimensions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30044—Fetus; Embryo
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Multimedia (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Medical Informatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Biophysics (AREA)
- Human Computer Interaction (AREA)
- Databases & Information Systems (AREA)
- Quality & Reliability (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Radiology & Medical Imaging (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Ultra Sonic Daignosis Equipment (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a fetal image recognition method and a fetal image recognition system based on deep learning, which relate to the technical field of image processing, wherein the method comprises the steps of performing dimension reduction on a data set twice, and calculating potential space dimension of the data set; reducing the dimension of the data set according to the potential space dimension; related information such as an activation expanding path, a compression weight matrix and the like is considered in the dimension reduction process, and compared with the dimension reduction method in the prior art, the dimension of a data set can be effectively reduced, so that the efficiency of subsequent model training is greatly improved; when the dimension of the data set is reduced for the second time, carrying out Fourier transformation and data screening on the low-dimension data set to obtain conventional radiology feature data, and then carrying out dimension reduction treatment on the radiology feature data set by adopting a Laplacian eigenmap method to obtain a dimension reduced data set; and then the training is input into the deep learning model, so that the model training accuracy is greatly improved.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a fetal image recognition method and system based on deep learning.
Background
Along with the rapid development and popularization of medical imaging equipment, imaging technology is widely applied in clinic and becomes an indispensable auxiliary means for developing disease diagnosis, operation planning, prognosis evaluation and follow-up visit, and medical image segmentation can extract key information from specific tissue images and is a key step for realizing medical image visualization.
The fetal ultrasonic image has a complex structure, is easily influenced by the maternal condition, the fetal position and the form, increases the difficulty of image analysis, and the prior art mostly gives a conclusion according to ultrasonic images by experienced doctors, so that the analysis efficiency is low, the subjectivity is high, the accuracy is to be improved, and the deep learning model is applied to fetal ultrasonic image identification. Scholars have proposed deep learning based CNNs (Convolutional Neural Networks ) to overcome the existing problems. The hierarchical CNN architecture is used to address multi-objective constraints of the fetal abnormality image recognition framework, such as configuration and initialization, noise removal, enhanced image, spine position detection, semantically segmenting rib regions. For the estimation of the body part of each fetal ultrasound image, a plurality of CNN architectures are used, wherein the CNN of each estimated body part is related to each other.
The identification method needs to train a plurality of models, the cost is very high, and when the existing deep neural network model identifies the fetal image, model data needed by training the existing model is large, so that the training efficiency is low.
Therefore, research on low-cost and high-efficiency fetal image recognition models is necessary.
Disclosure of Invention
The invention aims to provide a fetal image recognition method and system based on deep learning, which are used for solving the problems of low model training efficiency and high cost in the prior art.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a fetal image recognition method based on deep learning comprises the following steps:
step S1: acquiring an ultrasonic fetal image with a diagnosis conclusion, preprocessing the ultrasonic fetal image to obtain a preprocessed data set x 1 ；
Step S2: for the data set x 1 Performing first dimension reduction to obtain a data set x 2 The method comprises the following specific steps of:
step S21: calculating the data set x 1 A potential spatial dimension, the potential spatial dimension calculation formula being:
；
wherein,for potential spatial dimension, +.>In order to compress the activation parameters of the path,Win order to compress the weight matrix,b e is a compressed path deviation;
step S22: according to the potential space dimensionFor the data set x 1 Performing first dimension reduction to obtain a data set x 2 The calculation formula is as follows:
；
wherein x is 2 For the data set obtained by the first dimension reduction,it is the activation of the extension path that,W T transpose of the compression weight matrix;
step S3: for the data set x 2 Data processing is carried out to obtain a data set x 3 ；
Step S4: for the data set x 3 Performing second dimension reduction to obtain a data set x 4 ；
Step S5: employing the data set x 4 Training a deep learning model to obtain a trained fetal image recognition model;
step S6: step S6: and inputting the image to be predicted into the fetal image recognition model, and outputting a recognition result of the fetal image.
Further, in the step S1, the preprocessing of the ultrasonic fetal image specifically includes: and removing speckle noise of the ultrasonic fetal image by using a smoothing filter to obtain a smoothed denoising image.
Further, in the step S3, the data set x is acquired 2 Data processing is carried out to obtain a data set x 3 The method comprises the following steps:
step S31, for said dataset x 2 Performing Fourier transform；
Step S32, selecting a radiation group characteristic, and screening the data set after Fourier transformation according to the radiation group characteristic to obtain a data set x 3 。
Further, the radiation group feature includes: first order statistics, gray level co-occurrence matrix, shape-based representation.
Further, in the step S4, the data set x is acquired 3 Performing second dimension reduction to obtain a data set x 4 The method specifically comprises the following steps: the data set x is subjected to the Laplace eigenmap method 3 Dimension reduction is carried out to obtain a data set x 4 。
Further, in the step S5, the deep learning model is a UNet deep segmentation network model.
Further, in the step S5, the specific training steps of the deep learning model are as follows:
step S51: integrating the data set x 4 Randomly dividing the training group and the testing group;
step S52: training the deep learning model using the training set;
step S53: and inputting the test group into the deep learning model, and testing whether the deep learning model meets the preset precision requirement, if so, completing training to obtain a trained fetal image recognition model, and if not, returning to the step S52, and training the deep learning model again.
A deep learning-based fetal image recognition system using the deep learning-based fetal image recognition method of any one of the above, comprising the following modules:
and a data acquisition module: for obtaining a plurality of ultrasonic fetal images with diagnosis conclusions, preprocessing the ultrasonic fetal images to obtain a preprocessed data set x 1 ；
The dimension reduction processing module is used for: is connected with the data acquisition module and is used for acquiring the data set x 1 Performing first dimension reduction to obtain a data set x 2 For the data set x 2 Data processingObtaining a data set x 3 For the data set x 3 Performing second dimension reduction to obtain a data set x 4 ；
The fetal image recognition model training module: is connected with the dimension reduction processing module and is used for adopting the data set x 4 Training a deep learning model to obtain a trained fetal image recognition model;
and a result output module: the fetal image recognition model training module is connected with the fetal image recognition model training module and is used for inputting an image to be predicted into the fetal image recognition model and realizing the recognition of the fetal shape by the fetal image recognition model.
Compared with the prior art, the invention has the following beneficial effects:
firstly, the method performs dimension reduction on the data set twice, and calculates the potential space dimension of the data set through the first pass; reducing the dimension of the data set according to the calculated potential space dimension; related information such as an activation expanding path, a compression weight matrix and the like is considered in the dimension reduction process, and compared with the dimension reduction method in the prior art, the dimension of a data set can be effectively reduced, so that the efficiency of subsequent model training is greatly improved;
secondly, performing Fourier transformation and data screening on the low-dimensional data set to obtain conventional radiological characteristic data when performing second dimension reduction on the data set, and performing dimension reduction processing on the radiological characteristic data set by using a Laplacian eigenmap method to obtain a dimension-reduced data set; then inputting the training data into a deep learning model, so that the training precision of the model is greatly improved;
thirdly, the training data containing the comprehensive information of fetal image recognition is obtained through dimension reduction of the data, and the model is trained, so that the obtained model can recognize each body part of the fetal ultrasonic image, namely one model realizes the comprehensive recognition of the image, and the cost of model training is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those of ordinary skill in the art that the drawings in the following description are exemplary only and that other implementations can be obtained from the extensions of the drawings provided without inventive effort.
FIG. 1 is a schematic flow chart of the method of embodiment 1 of the present invention;
fig. 2 is a schematic structural diagram of a system according to embodiment 2 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The concepts related to the present application will be described with reference to the accompanying drawings. It should be noted that the following descriptions of the concepts are only for making the content of the present application easier to understand, and do not represent a limitation on the protection scope of the present application; meanwhile, the embodiments and features in the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example 1
The fetal ultrasound image helps to assess the growth and development of the fetus. In the prior art, a doctor estimates the growth condition of a fetus by visually estimating the shape of the fetus, and the estimation mode has large error. In the embodiment, the fetal shape in the ultrasonic image of the fetus is segmented through image recognition and segmentation of the ultrasonic image of the fetus, so that a doctor is helped to evaluate the growth condition of the fetus.
As shown in fig. 1, the invention provides a fetal image recognition method based on deep learning, which comprises the following steps:
step S1: obtaining superconclusions with diagnosticsThe sound wave fetal image is preprocessed to obtain a preprocessed data set x 1 。
Specifically, in the step S1, the step of preprocessing the ultrasonic fetal image includes:
the step S11: removing speckle noise of the ultrasonic fetal image by using a smoothing filter to obtain a smoothed denoising image;
the step S12: selecting filters with sizes of 3×3 and 7×7 to apply to the direction smoothing filter;
the S13: denoising all the denoising images through the direction smoothing filter to obtain a data set x 1 。
By the denoising step of this step, the visual appearance of the ultrasound image can be improved, and the ultrasound image can be made smoother by applying a smoothing filter to the ultrasound image.
Step S2: for the data set x 1 Performing first dimension reduction to obtain a data set x 2 。
The method comprises the following specific steps:
first the dataset x is calculated 1 Potential spatial dimensions;
in particular, the data set x 1 Having a spatial dimension of 512 x 512, the data set x 1 To a potential spatial dimension, the potential spatial dimension calculation formula is:
wherein,representing a potential dimension of space that is to be considered,
is an activation parameter of the compressed path,
w represents a compression weight matrix and,
representing a compression path deviation;
then, according to the calculated potential space dimensionFor the data set x 1 Performing first dimension reduction to obtain a data set x 2 ；
Specifically, from the calculated potential spatial dimensionsFor the data set x 1 Performing first dimension reduction to obtain a data set x 2 The formula of (2) is:
wherein x is 2 For the data set obtained by the first dimension reduction,is an active extension path, +.>Is the transpose of the compression weight matrix.
In contrast to the prior art dimension reduction method, in the present embodiment, the potential spatial dimension is calculated by first calculatingAnd then dimension reduction is carried out on the data set according to the potential space dimension, so that the dimension of the data set can be effectively reduced, and the lost image characteristic information is less, thereby greatly improving the efficiency of subsequent model training.
Step S3: for the data set x 2 Data processing is carried out to obtain a data set x 3 。
For ultrasound images, the radiation features may be classified into different categories, e.g., first order features, including tissue density, shape features (i.e., volume and surface area), and texture features.
Specifically, in the step S3, the low-dimensional dataset x 2 Fourier transforming to obtain a conventional radiological characteristic data set x 3 The method comprises the following steps: for the low-dimensional dataset x 2 Performing Fourier transform, selecting three types of radiological group features, namely first order statistics, gray level co-occurrence matrix and shape-based expression, obtaining 354 radiological features, and storing the 354 radiological features into a specific array to obtain a conventional radiological feature data set x 3 。
The first order statistic is a characteristic value calculated directly based on the pixel gray level distribution of the original image;
the gray level co-occurrence matrix is a statistical method for describing the texture features of the image, captures the texture features of the image by analyzing the spatial relationship among pixels in the image and the statistical distribution of gray levels, and can be used for a plurality of computer vision tasks such as image classification, texture recognition, image segmentation and the like;
the shape-based representation is identified from each body part of the fetal ultrasound image.
When the method for extracting the characteristics of the radioactive group is selected, the invention considers that the structure of the fetal ultrasonic image is complex, the fetal ultrasonic image is easy to be influenced by the condition of a parent, the position and the form of the fetus, the connection area between the fetus and the parent is fuzzy and is not easy to be divided, and the difficulty of image analysis is increased.
Step S4: for the data set x 3 Performing second dimension reduction to obtain a data set x 4 。
Specifically, in the step S4, the data set x is subjected to 3 Dimension reduction by dimension reduction pathThe dimensions are: the method of Laplacian eigenmap is adopted for the radiological characteristic data set x 3 Dimension reduction is performed to obtain the radiological characteristic data set x with 354 radiological groups 3 Reduced to data set x with 12 radial groups 4 ；
Step S5: employing the data set x 4 Training the deep learning model to obtain a trained fetal image recognition model.
The deep learning model is a UNet deep segmentation network model.
In the neural network structure, the UNet architecture is suitable for dividing various targets or organs in a plurality of medical imaging modes, pixel point types can be predicted through a small number of training pictures, and the data size of medical images is matched with the UNet model in size, so that overfitting can be effectively avoided; thus, the present invention selects the original UNet depth-segmentation network to identify the shape of the fetus.
Specifically, in the step S5, the specific steps of training the deep learning model are as follows:
step S51: integrating the data set x 4 Randomly dividing the training group and the testing group;
step S52: training the deep learning model using the training set;
step S53: and inputting the test group into the deep learning model, and testing whether the deep learning model meets the preset precision requirement, if so, completing training to obtain a trained fetal image recognition model, and if not, returning to the step S52, and training the deep learning model again.
Further, the model parameters are training step length, learning rate and other parameters.
Step S6: and inputting the image to be predicted into the fetal image recognition model, and outputting a recognition result of the fetal image.
According to the invention, the original data set is subjected to preprocessing, data set dimension reduction, feature extraction and feature dimension reduction in sequence, so that the data dimension reduction is realized under the condition of less influence on the data, the data volume of the training set used as a training UNet depth segmentation network model is greatly reduced, the calculation cost is reduced, and the calculation speed is accelerated.
Example 2
As shown in fig. 2, the present invention further proposes a fetal image recognition system based on deep learning, using the fetal image recognition method based on deep learning as described in any one of embodiment 1, comprising the following modules:
and a data acquisition module: for obtaining a plurality of ultrasonic fetal images with diagnosis conclusions, preprocessing the ultrasonic fetal images to obtain a preprocessed data set x 1 ；
The dimension reduction processing module is used for: is connected with the data acquisition module and is used for acquiring the data set x 1 Performing first dimension reduction to obtain a data set x 2 For the data set x 2 Data processing is carried out to obtain a data set x 3 For the data set x 3 Performing second dimension reduction to obtain a data set x 4 ；
The fetal image recognition model training module: is connected with the dimension reduction processing module and is used for adopting the data set x 4 Training a deep learning model to obtain a trained fetal image recognition model;
and a result output module: the fetal image recognition model training module is connected with the fetal image recognition model training module and is used for inputting an image to be predicted into the fetal image recognition model and realizing the recognition of the fetal shape by the fetal image recognition model.
Example 3
This embodiment includes a computer-readable storage medium having stored thereon a data processing program that is executed by a processor to perform the deep learning-based fetal image recognition method of embodiment 1.
It will be apparent to one of ordinary skill in the art that embodiments herein may be provided as a method, apparatus (device), or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Including but not limited to RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer, and the like. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
The description herein is with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices) and computer program products according to embodiments herein. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application. As used in this specification, the terms "a," "an," "the," and/or "the" are not intended to be special references, but rather are intended to include the singular as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method or apparatus comprising such elements.
It should also be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," and the like indicate an orientation or a positional relationship based on that shown in the drawings, and are merely for convenience of description and simplification of the description, and do not indicate or imply that the apparatus or element in question must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present application. Unless specifically stated or limited otherwise, the terms "mounted," "connected," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art in a specific context.
The above examples and/or embodiments are merely for illustrating the preferred embodiments and/or implementations of the present technology, and are not intended to limit the embodiments and implementations of the present technology in any way, and any person skilled in the art should be able to make some changes or modifications to the embodiments and/or implementations without departing from the scope of the technical means disclosed in the present disclosure, and it should be considered that the embodiments and implementations are substantially the same as the present technology.
Specific examples are set forth herein to illustrate the principles and embodiments of the present application, and the description of the examples above is only intended to assist in understanding the methods of the present application and their core ideas. The foregoing is merely a preferred embodiment of the present application, and it should be noted that, due to the limited text expressions, there is virtually no limit to the specific structure, and that, for a person skilled in the art, modifications, alterations and combinations of the above described features may be made in an appropriate manner without departing from the principles of the present application; such modifications, variations and combinations, or the direct application of the concepts and aspects of the invention in other applications without modification, are intended to be within the scope of this application.
Claims (8)
1. The fetal image recognition method based on deep learning is characterized by comprising the following steps of:
step S1: acquiring an ultrasonic fetal image with a diagnosis conclusion, preprocessing the ultrasonic fetal image to obtain a preprocessed data set x 1 ；
Step S2: for the data set x 1 Performing first dimension reduction to obtain a data set x 2 The method comprises the following specific steps of:
step S21: calculating the data set x 1 A potential spatial dimension, the potential spatial dimension calculation formula being:
；
wherein,for potential spatial dimension, +.>In order to compress the activation parameters of the path,Wto compress the weight matrix, b e Is a compressed path deviation;
step S22: according to the potential space dimensionFor the data set x 1 Performing first dimension reduction to obtain a data set x 2 The calculation formula is as follows:
；
wherein x is 2 For the data set obtained by the first dimension reduction,is an activation parameter of the extension path,W T transpose of the compression weight matrix;
step S3: for the data set x 2 Data processing is carried out to obtain a data set x 3 ；
Step S4: for the data set x 3 Performing second dimension reduction to obtain a data set x 4 ；
Step S5: employing the data set x 4 Training a deep learning model to obtain a trained fetal image recognition model;
step S6: and inputting the image to be predicted into the fetal image recognition model, and outputting a recognition result of the fetal image.
2. The fetal image recognition method based on deep learning according to claim 1, wherein in the step S1, the preprocessing of the ultrasonic fetal image is specifically: and removing speckle noise of the ultrasonic fetal image by using a smoothing filter to obtain a smoothed denoising image.
3. The fetal image recognition method as claimed in claim 1, wherein in said step S3, said data set x is 2 Data processing is carried out to obtain a data set x 3 The method comprises the following steps:
step S31, for said dataset x 2 Performing Fourier transform;
step S32, selecting a radiation group characteristic, and screening the data set after Fourier transformation according to the radiation group characteristic to obtain a data set x 3 。
4. A fetal image recognition method as claimed in claim 3 wherein said radiological group features comprise: first order statistics, gray level co-occurrence matrix, shape-based representation.
5. The fetal image recognition method as claimed in claim 1, wherein in said step S4, the data set x is 3 Performing second dimension reduction to obtain a data set x 4 The method specifically comprises the following steps: the data set x is subjected to the Laplace eigenmap method 3 Dimension reduction is carried out to obtain a data set x 4 。
6. The fetal image recognition method as claimed in claim 1, wherein in the step S5, the deep learning model is a UNet deep segmentation network model.
7. The fetal image recognition method based on deep learning of claim 6, wherein in the step S5, the specific training step of the deep learning model is as follows:
step S51: integrating the data set x 4 Randomly dividing the training group and the testing group;
step S52: training the deep learning model using the training set;
step S53: and inputting the test group into the deep learning model, and testing whether the deep learning model meets the preset precision requirement, if so, completing training to obtain a trained fetal image recognition model, and if not, returning to the step S52, and training the deep learning model again.
8. A deep learning-based fetal image recognition system using the deep learning-based fetal image recognition method of any one of claims 1-7, comprising the following modules:
and a data acquisition module: for obtaining multiple ultrasound fetal images with diagnostic conclusionsPreprocessing the ultrasonic fetal image to obtain a preprocessed data set x 1 ；
The dimension reduction processing module is used for: is connected with the data acquisition module and is used for acquiring the data set x 1 Performing first dimension reduction to obtain a data set x 2 For the data set x 2 Data processing is carried out to obtain a data set x 3 For the data set x 3 Performing second dimension reduction to obtain a data set x 4 ；
The fetal image recognition model training module: is connected with the dimension reduction processing module and is used for adopting the data set x 4 Training a deep learning model to obtain a trained fetal image recognition model;
and a result output module: the fetal image recognition model training module is connected with the fetal image recognition model training module and is used for inputting an image to be predicted into the fetal image recognition model and realizing the recognition of the fetal shape by the fetal image recognition model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311643319.1A CN117351012B (en) | 2023-12-04 | 2023-12-04 | Fetal image recognition method and system based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311643319.1A CN117351012B (en) | 2023-12-04 | 2023-12-04 | Fetal image recognition method and system based on deep learning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117351012A CN117351012A (en) | 2024-01-05 |
| CN117351012B true CN117351012B (en) | 2024-03-12 |
Family
ID=89357825
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311643319.1A Active CN117351012B (en) | 2023-12-04 | 2023-12-04 | Fetal image recognition method and system based on deep learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117351012B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110321968A (en) * | 2019-07-11 | 2019-10-11 | 广东工业大学 | A kind of ultrasound image sorter |
| WO2020081582A1 (en) * | 2018-10-16 | 2020-04-23 | Anixa Diagnostics Corporation | Methods of diagnosing cancer using multiple artificial neural networks to analyze flow cytometry data |
| WO2023274512A1 (en) * | 2021-06-29 | 2023-01-05 | Brainlab Ag | Method for training and using a deep learning algorithm to compare medical images based on dimensionality-reduced representations |
| CN116503607A (en) * | 2023-06-28 | 2023-07-28 | 天津市中西医结合医院（天津市南开医院） | CT image segmentation method and system based on deep learning |
| CN116848549A (en) * | 2022-01-13 | 2023-10-03 | 博医来股份公司 | Detection of image structures via dimension-reduction projection |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11717686B2 (en) * | 2017-12-04 | 2023-08-08 | Neuroenhancement Lab, LLC | Method and apparatus for neuroenhancement to facilitate learning and performance |
| WO2021173763A1 (en) * | 2020-02-28 | 2021-09-02 | Spectral Md, Inc. | Machine learning systems and methods for assessment, healing prediction, and treatment of wounds |
-
2023
- 2023-12-04 CN CN202311643319.1A patent/CN117351012B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020081582A1 (en) * | 2018-10-16 | 2020-04-23 | Anixa Diagnostics Corporation | Methods of diagnosing cancer using multiple artificial neural networks to analyze flow cytometry data |
| CN110321968A (en) * | 2019-07-11 | 2019-10-11 | 广东工业大学 | A kind of ultrasound image sorter |
| WO2023274512A1 (en) * | 2021-06-29 | 2023-01-05 | Brainlab Ag | Method for training and using a deep learning algorithm to compare medical images based on dimensionality-reduced representations |
| CN116848549A (en) * | 2022-01-13 | 2023-10-03 | 博医来股份公司 | Detection of image structures via dimension-reduction projection |
| CN116503607A (en) * | 2023-06-28 | 2023-07-28 | 天津市中西医结合医院（天津市南开医院） | CT image segmentation method and system based on deep learning |
Non-Patent Citations (2)
| Title |
|---|
| 3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification;Haokui Zhang et al;《arXiv:2101.04287v1》;第1-19页 * |
| 基于流形学习的数据降维算法研究;罗廷金;《中国优秀硕士学位论文全文数据库 信息科技辑》;第1-39页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117351012A (en) | 2024-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111325739B (en) | Method and device for detecting lung focus and training method of image detection model | |
| CN109685060B (en) | Image processing method and device | |
| EP3770850A1 (en) | Medical image identifying method, model training method, and computer device | |
| EP3869387A1 (en) | Method and device for three-dimensional image semantic segmentation, terminal and storage medium | |
| CN110310287B (en) | Automatic organ-at-risk delineation method, equipment and storage medium based on neural network | |
| US10853409B2 (en) | Systems and methods for image search | |
| CN111291825B (en) | Focus classification model training method, apparatus, computer device and storage medium | |
| CN110992377B (en) | Image segmentation method, device, computer-readable storage medium and equipment | |
| CN110956632B (en) | Method and device for automatically detecting pectoralis major region in molybdenum target image | |
| CN110930378B (en) | Emphysema image processing method and system based on low data demand | |
| JP2010207572A (en) | Computer-aided detection of lesion | |
| CN111462049A (en) | Automatic lesion area form labeling method in mammary gland ultrasonic radiography video | |
| CN110570394A (en) | medical image segmentation method, device, equipment and storage medium | |
| EP4118617A1 (en) | Automated detection of tumors based on image processing | |
| CN111915626A (en) | Automatic segmentation method and device for ventricle area of heart ultrasonic image and storage medium | |
| CN113850796A (en) | Lung disease identification method and device based on CT data, medium and electronic equipment | |
| CN117351012B (en) | Fetal image recognition method and system based on deep learning | |
| CN113724185A (en) | Model processing method and device for image classification and storage medium | |
| Li et al. | Classify and explain: An interpretable convolutional neural network for lung cancer diagnosis | |
| Shoaib et al. | Comparative studies of deep learning segmentation models for left ventricle segmentation | |
| CN115909016A (en) | System, method, electronic device, and medium for analyzing fMRI image based on GCN | |
| CN114360695B (en) | Auxiliary system, medium and equipment for breast ultrasonic scanning and analyzing | |
| CN113222985B (en) | Image processing method, image processing device, computer equipment and medium | |
| CN115131361A (en) | Training of target segmentation model, focus segmentation method and device | |
| CN112766332A (en) | Medical image detection model training method, medical image detection method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |