CN110279433A - A kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks - Google Patents

A kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks Download PDF

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Publication number
CN110279433A
CN110279433A CN201910384672.XA CN201910384672A CN110279433A CN 110279433 A CN110279433 A CN 110279433A CN 201910384672 A CN201910384672 A CN 201910384672A CN 110279433 A CN110279433 A CN 110279433A
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image
training
deeplabv2
head circumference
neural networks
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CN201910384672.XA
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CN110279433B (en
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罗红
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四川大学华西第二医院
成都市汪汪科技有限公司
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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0866Detecting organic movements or changes, e.g. tumours, cysts, swellings involving foetal diagnosis; pre-natal or peri-natal diagnosis of the baby
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5215Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data

Abstract

The fetus head circumference automatic and accurate measurement method based on convolutional neural networks that the invention discloses a kind of, successively the following steps are included: input primary fetal ultrasound sectiones thalamencephali ultrasonic scan image;Remove the sensitive information of input picture;Quick ultrasound image is removed in multi-angle cutting;Trained DeepLabv2 is inputed to, multi-angle image segmentation result is obtained;Fusion is overlapped to multi-angle image segmentation result;Measurement is completed after extracting partitioning boundary.The present invention is realized to fetal ultrasound sectiones thalamencephali head circumference precise measurement, effectively divide the region of the cranium of identification fetus sectiones thalamencephali using DeepLabV2 convolutional neural networks model, accurately find out the edge in ultrasound image in hyperechoic skull, and then realize the precise measurement of fetus head circumference, suitable for Prenatal ultrasonography for diagnosing, there is positive effect to alleviating doctor's operating pressure, improving antenatal sonographic diagnosis working efficiency, the conflict between doctors and patients being becoming increasingly acute can also be alleviated simultaneously and improve the effective use of medical resource, there is biggish social value and actual use value.

Description

A kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks

Technical field

The present invention relates to medical ultrasonic diagnostic technical fields, and in particular to a kind of fetus head circumference based on convolutional neural networks Automatic and accurate measurement method.

Background technique

Accurate evaluation development of fetus situation is for ensuring the lasting health of mother and newborn during pregnancy and after pregnancy It is most important.Two-dimensional ultrasound since with strong real-time, at low cost, availability is wide, be not present harmful radiation the advantages that, so by It is widely used in antenatal exaination.Prenatal ultrasonography for diagnosing is the observation most common method of embryo growth and development situation, and prevents defect Fetal birth evaluates the important evidence of development of fetus degree.

The main flow of Prenatal ultrasonography for diagnosing includes: first, the acquisition of fetus key position ultrasound standard section;Second, Biological parameter measurement based on fetal ultrasound standard section;Third judges the life of fetus using the gestational age physique and health of fetus Long development condition.By the process of Prenatal ultrasonography for diagnosing it is found that doctor obtains the ultrasound mark at each position of fetus using two-dimensional ultrasound Quasi- section, and biological parameter measurement is carried out on this basis, such as document Chen Hao, Dou Qi, Ni Dong, et al.Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks[C]//Nassir Navab,Joachim Hornegger, Medical Image Computing and Computer-Assisted Intervention.Munich:Springer In International Publishing, 2015:507-514., abdominal circumference is measured on upper abdomen section;On sectiones thalamencephali Measure head circumference;It is long that femur is measured on femur section;Measuring head stern length and nuchal translucency thickness etc. on median sagittal section.

Currently, the head circumference measurement on sectiones thalamencephali is by the manually implemented Fig. 2 institute of ultrasonic doctor in clinical practice diagnosis Show, it is insufficient that this results in following five aspects:

1, ultrasonic image, which is checked on the quality, is difficult to effectively control;Get parms measurement depend on ultrasonic doctor clinical experience and Professional standards, if experience level is insufficient, ultrasonic image quality is difficult to ensure;

2, result of ultrasonography does not have uniformity;Since different ultrasonic doctor abilities and experience are different, to fetus Head circumference measurement result is also not quite similar;

3, it is difficult to effectively improve ultrasonic examination working efficiency;Since complete Prenatal ultrasonography for diagnosing needs to obtain fetus head circumference The mode of parameter, manual measurement leads to inefficiency;

4, sonographer is susceptible to suffer from occupational disease;On the one hand sonographer quantity lacks at present, and working frequency is very high, while ultrasound Doctor need to largely carry out such as mobile probe, freeze frame repetitive operation, be susceptible to suffer from repetitive pressure damage;

5, basic hospital ultrasonic imaging diagnosis technology is to be improved;Basic hospital sonographer diagnostic techniques needs further It improves, is diagnosed particular for elderly parturient women, Grade A hospital is needed to diagnose, cause patient's medical treatment interrogation inconvenient, Grade A hospital Working strength increases, and medical resource does not sink to base really.

In recent years, there are many be based on Hough transformation (Hough transform), Haar feature (Haar-Like Features), multi-threshold (Multilevel thresholding), round shortest path (Circular shortest Paths), active contour model, the automatic head circumference measuring system such as (Active contouring) are suggested.But these methods, all It is the approximate measure mode based on ellipse fitting, there has been no the schemes automatically accurately measured based on skull contour line, this is also The problem that " Intelligence Ultrasound " field value must be broken through.

Computer vision technique (Computer Vision, CV) is fast-developing, wherein depth convolutional neural networks (Deep Convolutional Neural Networks, DCNN) it is with the fastest developing speed.DCNN is on the basis of conventional multilayer perceptron It is inspired and is made a variation by cerebral nerve meta structure, it passes through the combined application of convolution kernel and Chi Huahe, from the high dimension of large amount of complex According to the abstract characteristics of middle extraction, spatial correlation therein is excavated.DCNN has been successfully applied to the field of medical imaging, such as in image Registration, positioning, dissection/eucaryotic cell structure detection, tissue segmentation, the assessment of area of computer aided disease diagnosis and prognosis etc..But it is directed to tire Head circumference measurement on youngster's ultrasound thalamus standard section, occurs still without the full-automatic measuring method based on DCNN.

Summary of the invention

The technical problems to be solved by the present invention are: being deposited during the existing clinical practice diagnosis to the measurement of fetus head circumference Efficiency is lower, doctor's subjective factor is affected and is unfavorable for the problems such as accurately detecting, the present invention provides solve the above problems A kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks, so the present invention propose based on depth convolution mind Through the full-automatic accurate measurement method of network-DeepLabv2 fetus head circumference.

The present invention is achieved through the following technical solutions:

A kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks, comprising the following steps:

Setp1 inputs primary fetal ultrasound sectiones thalamencephali ultrasonic scan image;

Setp2, multi-angle cut ultrasound image;

Setp3 inputs to trained DeepLabv2, obtains multi-angle image segmentation result;

Setp4 is overlapped fusion to multi-angle image segmentation result;

Setp5 extracts image segmentation boundary, completes measurement after obtaining image boundarg pixel length.

Further, the measurement method is to carry out head circumference automatic measurement system at deep learning frame TensorFlow System design and test.

It further, further include the sensitive information for removing input picture, the sensitive information between Setp1 and Setp2 Including about patients' privacy information.

Further, the Setp2 multi-angle cutting refers to choosing multiple angle directions above image and is cut.I.e. Side chooses any position and is cut on the image, in the forward projection point and image of the cutting position of selection between object Distance >=0.

Further, in the Setp3, DeepLabv2 model training method the following steps are included:

Setp31 builds DeepLabv2 network model;

Setp32 is finely adjusted using pre-training collection, to complete the DeepLabv2 network model of pre-training;

Setp33 measures the difference between predicted value and actual value, to correct DeepLabv2 network model;

Setp34, in training process, original image and the pairs of input training of tag image, each pixel pair in image Loss function cross entropy is equivalent weight;

Setp35 optimizes DeepLabv2 network model parameter using stochastic gradient descent algorithm.

Further, the DeepLabv2 model training method concrete operations are as follows: Setp31 uses ImageNet data Collection carries out pre-training to DeepLabv2 network model, completes initialization, achievees the purpose that transfer learning;Setp32 completes pre- instruction Experienced DeepLabv2 network model continues the training on fetal ultrasound image, is finely adjusted, is cut with adapting to fetal ultrasound thalamus The cranium brain of face image divides task;Setp33, in training process, we use cross entropy cost function as loss function, come Measure the difference between predicted value and actual value;Setp34, in training process, original image and the pairs of input of tag image are instructed Practice, each pixel in image is equivalent weight to loss function cross entropy;Setp35, it is excellent using stochastic gradient descent algorithm Change DeepLabv2 network model parameter, to be optimal, entire training process amounts to iteration 2 × 105Secondary, initial learning rate is 2.5×10-4, every 1 × 104Learning rate decline 5 × 10-4Times.

Further, in the Setp5, concrete operation method are as follows: in such a way that multi-angle is cut, inversely by binaryzation Segmented image splicing and recovery at original image, the input picture cut with multi-angle corresponds in original image.

Further, in the Setp6, the concrete operations of partitioning boundary are extracted are as follows: the segmented image obtained for Setp5 It is first expanded, then subtracts the segmented image that Setp5 is obtained with the image after expansion.

The present invention has the advantage that and the utility model has the advantages that

The present invention is realized to fetal ultrasound sectiones thalamencephali head circumference precise measurement, uses DeepLabV2 convolutional neural networks mould Type effectively divides the region of the cranium of identification fetus sectiones thalamencephali, accurately finds out the edge in ultrasound image in hyperechoic skull, And then the precise measurement of fetus head circumference is realized, it is suitable for Prenatal ultrasonography for diagnosing, to alleviation doctor's operating pressure, improves antenatal surpass Audio clinic working efficiency has positive effect, while can also alleviate the conflict between doctors and patients being becoming increasingly acute and improve having for medical resource Effect utilizes, and has biggish social value and actual use value.

Detailed description of the invention

Attached drawing described herein is used to provide to further understand the embodiment of the present invention, constitutes one of the application Point, do not constitute the restriction to the embodiment of the present invention.In the accompanying drawings:

Fig. 1 is sectiones thalamencephali ideograph;

Fig. 2 is that doctor manually scheme by oval measurement in the prior art;

Fig. 3 is that multi-angle cuts original image;

Fig. 4 is image after multi-angle is cut;

Fig. 5 is head circumference measuring method flow chart of the invention;

Fig. 6 is that fetal ultrasound head circumference marks figure comparison diagram.

Specific embodiment

To make the objectives, technical solutions, and advantages of the present invention clearer, below with reference to embodiment and attached drawing, to this Invention is described in further detail, and exemplary embodiment of the invention and its explanation for explaining only the invention, are not made For limitation of the invention.

Embodiment 1

Present embodiments provide a kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks, operating procedure It is successively as follows:

Setp1 inputs primary fetal ultrasound sectiones thalamencephali ultrasonic scan image;It is described a kind of based on convolutional neural networks Fetus head circumference automatic and accurate measurement method is to carry out the design of head circumference automatic measurement system at deep learning frame TensorFlow With test;

Setp2, removes the sensitive information of input picture, and the sensitive information includes the patient's name of image border, age Equal privacy informations;

Quick ultrasound image is removed in Setp3, multi-angle cutting;It as shown in Figure 3 and Figure 4, is the original image of H to an a length of W, width, It is cut from upper left, upper right, bottom right, the angle of lower-left four with ratio Q (0 < Q < 1, Q=0.9 of the present invention), then cutting is obtained Image, be input in model with the size of 321*321;

Setp4 inputs to trained DeepLabv2, obtains multi-angle image segmentation result, to the target area of image The boundary for obscuring region of domain and background area is split;DeepLabv2 the relevant technologies can be found in document Chen L C, Papandreou G,Kokkinos I,et al.DeepLab:Semantic Image Segmentation with Deep Convolutional Nets,Atrous Convolution,and Fully Connected CRFs[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2018,40(4):834-848.

It is 32 core 2.9GHz Intel Xeon E5-2670CPU, NVIDIA that the system experimentation, which runs hardware environment, The computer of 1080TI GPU and 64GB memory.In order to avoid there is overfitting problem, the DeepLabv2 model that the present invention uses It does not take Gaussian Profile to initialize, but directly uses the mode of transfer learning, it is each by what is learnt on natural image Layer parameter migration initialization, and be finely adjusted in fetal ultrasound image training process, it is specific as follows.

DeepLabv2 model training process: 1. first carry out in advance DeepLabv2 network model using ImageNet data set Training completes initialization, achievees the purpose that transfer learning;2. completing the DeepLabv2 network model of pre-training, continue in fetus Training, is finely adjusted on ultrasound image, divides task to adapt to the cranium brain of fetal ultrasound sectiones thalamencephali image;3. training process In, use cross entropy cost function as loss function, the difference between Lai Hengliang predicted value and actual value;4. training process In, original image is trained with the pairs of input of tag image, and each pixel in image is equivalent power to loss function cross entropy Weight;5. optimizing DeepLabv2 network using stochastic gradient descent algorithm (Stochastic gradient descent, SGD) Model parameter, to be optimal;6. entire training process amounts to iteration 2 × 105Secondary, initial learning rate is 2.5 × 10-4, every 1 ×104Learning rate decline 5 × 10-4Times.

Setp5 is overlapped fusion to multi-angle segmentation result;I.e. in such a way that multi-angle is cut, inversely by two-value At original image, the input picture cut with multi-angle corresponds the segmented image splicing and recovery of change in original image;For overlapping Part, the segmentation figure each point pixel value corresponding with original image made using divide operations are maintained within the scope of 0-255.

Setp6 completes measurement after extracting partitioning boundary;For the segmentation figure A that step 6 obtains, first A is expanded, so Original image A is subtracted with the image B after expansion afterwards;Boundary pixel length is finally obtained, measurement is completed.

Embodiment 2

Based on a kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks that embodiment 1 provides, carry out Outcome evaluation:

The fetal ultrasound thalamus data set that the present invention uses amounts to 1261, wherein 1145 are training set, 116 are Test set.

1, qualitative evaluation

Fig. 6 is fetal ultrasound head circumference mark figure.It successively arranges from left to right, first to be classified as clinically used ellipse semi-automatic It marks (dotted line), second is classified as manual contours mark (solid line), and third is classified as the automatic marking result (solid line) of algorithm.It can be very Apparent discovery, our algorithm successfully split the identification of fetus cranium brain, and accurately find out in ultrasound image in Qiang Huisheng Skull edge.At the same time, the segmentation result of algorithm is more accurate than clinically used oval mark, with manual mark base This is consistent, illustrates feasibility of this algorithm in this problem.

2, it is quantitatively evaluated

The present invention uses the evaluation index based on region: coincidence factor (Dice), similarity (Jaccard), to assess segmentation As a result;Carry out assessment of the measurement result using mean value (d), standard deviation (s).

Assuming that A is the target area of hand labeled, B is the target area that algorithm is divided automatically.Dice and Jaccard is calculated Formula is as follows:

Wherein, Area (*) indicates areal calculation symbol.Assessment result is as shown in table 1:

Table 1 is quantitatively evaluated

As shown in Table 1, result of the invention with manually mark coincidence factor, similarity more than 0.95, measurement Mean value error is -0.3517cm (measurement result that-expression marks manually is less than measurement result of the invention), and standard deviation is 0.2816, indicate that the present invention has preferable consistency with manually mark;At the same time, clinically used oval mark is surveyed It is generally bigger than normal than measurement result of the invention to measure result, mean value error is+1.5621cm, the mean value error with manual mark measurement Reach+1.9128cm, illustrates the validity of precise measurement head circumference of the present invention.

Above-described specific embodiment has carried out further the purpose of the present invention, technical scheme and beneficial effects It is described in detail, it should be understood that being not intended to limit the present invention the foregoing is merely a specific embodiment of the invention Protection scope, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should all include Within protection scope of the present invention.

Claims (8)

1. a kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks, which comprises the following steps:
Setp1 inputs primary fetal ultrasound sectiones thalamencephali ultrasonic scan image;
Setp2, multi-angle cut ultrasound image;
Setp3 inputs to trained DeepLabv2, obtains multi-angle image segmentation result;
Setp4 is overlapped fusion to multi-angle image segmentation result;
Setp5 extracts image segmentation boundary, completes measurement after obtaining image boundarg pixel length.
2. a kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks according to claim 1, special Sign is that the measurement method is to carry out the design of head circumference automatic measurement system at deep learning frame TensorFlow and survey Examination.
3. a kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks according to claim 1, special Sign is, further includes the sensitive information for removing input picture between Setp1 and Setp2, the sensitive information includes about trouble Person's privacy information.
4. a kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks according to claim 1, special Sign is that the Setp2 multi-angle cutting refers to choosing multiple angle directions above image and cut.
5. a kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks according to claim 1, special Sign is, in the Setp3, DeepLabv2 model training method the following steps are included:
Setp31 builds DeepLabv2 network model;
Setp32 is finely adjusted using pre-training collection, to complete the DeepLabv2 network model of pre-training;
Setp33 measures the difference between predicted value and actual value, to correct DeepLabv2 network model;
Setp34, in training process, original image and the pairs of input training of tag image, each pixel in image is to loss Function cross entropy is equivalent weight;
Setp35 optimizes DeepLabv2 network model parameter using stochastic gradient descent algorithm.
6. a kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks according to claim 5, special Sign is, the DeepLabv2 model training method concrete operations are as follows: Setp31 uses ImageNet data set pair DeepLabv2 network model carries out pre-training, completes initialization, achievees the purpose that transfer learning;Setp32 completes pre-training DeepLabv2 network model continues the training on fetal ultrasound image, is finely adjusted, to adapt to fetal ultrasound sectiones thalamencephali figure The cranium brain of picture divides task;Setp33, in training process, we use cross entropy cost function as loss function, Lai Hengliang Difference between predicted value and actual value;Setp34, in training process, original image and the pairs of input training of tag image, Each pixel in image is equivalent weight to loss function cross entropy;Setp35 uses stochastic gradient descent algorithm, optimization DeepLabv2 network model parameter, to be optimal, entire training process amounts to iteration 2 × 105Secondary, initial learning rate is 2.5×10-4, every 1 × 104Learning rate decline 5 × 10-4Times.
7. a kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks according to claim 1, special Sign is, in the Setp5, concrete operation method are as follows: in such a way that multi-angle is cut, inversely by the segmented image of binaryzation At original image, the input picture cut with multi-angle corresponds splicing and recovery in original image.
8. a kind of fetus head circumference automatic and accurate measurement method based on convolutional neural networks according to claim 1, special Sign is, in the Setp6, extracts the concrete operations of partitioning boundary are as follows: the obtained segmented image of Setp5 is first carried out swollen It is swollen, then the segmented image that Setp5 is obtained is subtracted with the image after expansion.
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