CN112967263A - Liver tumor image sample augmentation method based on generation of countermeasure network - Google Patents
Liver tumor image sample augmentation method based on generation of countermeasure network Download PDFInfo
- Publication number
- CN112967263A CN112967263A CN202110291119.9A CN202110291119A CN112967263A CN 112967263 A CN112967263 A CN 112967263A CN 202110291119 A CN202110291119 A CN 202110291119A CN 112967263 A CN112967263 A CN 112967263A
- Authority
- CN
- China
- Prior art keywords
- liver tumor
- tumor image
- network model
- image
- liver
- 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.)
- Pending
Links
- 206010019695 Hepatic neoplasm Diseases 0.000 title claims abstract description 66
- 208000014018 liver neoplasm Diseases 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 title claims abstract description 22
- 230000003416 augmentation Effects 0.000 title claims abstract description 11
- 238000012549 training Methods 0.000 claims abstract description 26
- 230000003042 antagnostic effect Effects 0.000 claims abstract description 14
- 238000007781 pre-processing Methods 0.000 claims abstract description 11
- 238000003709 image segmentation Methods 0.000 claims abstract description 8
- 206010028980 Neoplasm Diseases 0.000 claims description 35
- 210000004185 liver Anatomy 0.000 abstract description 4
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 239000002131 composite material Substances 0.000 description 3
- 230000003902 lesion Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 238000013434 data augmentation Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000002059 diagnostic imaging Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
Images
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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
-
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- 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
- G06T2207/30056—Liver; Hepatic
-
- 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
- G06T2207/30096—Tumor; Lesion
Abstract
The invention discloses a liver tumor image sample augmentation method based on generation of an antagonistic network, which comprises the steps of firstly constructing a paired training set, then constructing a generation antagonistic network model, wherein the generation antagonistic network model comprises a generator and a discriminator, training the generation antagonistic network model, extracting a random liver tumor image from a CT slice data set of liver tumors, preprocessing the random liver tumor image, inputting the preprocessed liver tumor image into the generated antagonistic network model after training to obtain a liver tumor image set, and finally implanting the obtained liver tumor image set into the CT slice set which does not contain the liver tumors to obtain a data set for liver tumor image segmentation. The method can generate random liver tumor images by generating the confrontation network model to realize the augmentation of the liver tumor data set, is beneficial to increasing the variability of the liver tumor, and creates a rich real liver tumor section data set for the liver section.
Description
Technical Field
The invention belongs to the field of medical image processing, and particularly relates to a liver tumor image sample augmentation method based on generation of an antagonistic network.
Background
With the progress of scientific technology, medical imaging technology has been developed greatly, image segmentation is an indispensable means for extracting quantitative information of special tissues in medical images, and in order to accurately distinguish normal tissue structures and abnormal lesions in medical images, medical images need to be segmented, which is a key step in medical image processing.
Since a large number of parameters need to be optimized in the training of the image segmentation model, the number of training samples is high. Therefore, data augmentation of training data becomes one of the main means for applying deep learning technology, and the method mainly performs transformation operations on images, such as translation or rotation, mirror image and the like, to increase the amount of training data. However, the sample image obtained by performing a simple transformation operation on the image has a large error from the actual image.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a liver tumor image sample augmentation method based on generation of an antagonistic network, which can implement augmentation of a liver tumor data set by generating a random liver tumor image through generation of an antagonistic network model. The technical scheme of the invention is as follows:
a liver tumor image sample augmentation method based on generation of an antagonistic network comprises the following steps:
s1, constructing a pair training set;
the method comprises the steps of obtaining a CT slice containing the liver tumor, extracting a tumor image in the CT slice containing the liver tumor to obtain an original tumor image, and preprocessing the original tumor image to obtain a generated tumor image;
s2, constructing and generating a confrontation network model;
the generation of the confrontation network model comprises a generator and an arbiter;
s3, training a generative pair resistance network model;
performing iterative training on a generated countermeasure network model constructed by using the obtained original tumor image and the generated tumor image until the generated countermeasure network model is iterated to a preset number of times, completing training of the generated countermeasure network model, and obtaining the trained generated countermeasure network model;
s4, extracting a random liver tumor image from the CT slice data set of the liver tumor, preprocessing the random liver tumor image, and inputting the preprocessed random liver tumor image into the trained generation confrontation network model to obtain a liver tumor image set;
s5, the obtained liver tumor image set is implanted into a CT slice set not containing liver tumor, and a data set for liver tumor image segmentation is obtained.
Further, the tumor image in the CT slice containing the liver tumor is extracted, and the specific steps comprise adding a mask to the tumor area in the CT slice containing the liver tumor and extracting the mask.
Further, the preprocessing specifically includes modifying the shape, size, location and inner edge of the tumor image.
Further, the generation countermeasure network model includes a Pix2Pix network or a SPADE network.
The invention has the beneficial effects that: the method can generate random liver tumor images by generating an antagonistic network model to realize the amplification of the liver tumor data set, is beneficial to increasing the variability of liver tumors, and creates a rich real liver tumor section data set for liver sections.
Drawings
FIG. 1 is a schematic flow chart of a liver tumor image sample augmentation method based on generation of an antagonistic network according to the present invention;
FIG. 2 is a schematic diagram of the generation of a tumor image in an anti-challenge network model according to the present invention.
Detailed Description
The technical scheme of the invention is further described by combining the drawings and the embodiment:
the embodiment provides a liver tumor image sample augmentation method based on generation of an antagonistic network, as shown in fig. 1, including:
s1, constructing a pair training set;
the method comprises the steps of obtaining a CT slice containing the liver tumor, extracting a tumor image in the CT slice containing the liver tumor to obtain an original tumor image, and preprocessing the original tumor image to obtain a generated tumor image;
in the embodiment of the application, a CT slice with a liver tumor is obtained from a liver tumor segmentation challenge data set (LiTS) as an original tumor image, and a corresponding mask is set according to the size of the tumor in the CT slice, so as to ensure that the set mask can cover the tumor in the CT slice. And preprocessing the tumor mask image, i.e. the original tumor image, including modifying the shape, size, position and inner edge of the original tumor image, obtaining a pair of original tumor images and generating a tumor image.
S2, constructing and generating a confrontation network model;
in the embodiment of the invention, a countermeasure network model is generated by adopting Pix2Pix, and the countermeasure network model comprises a generator and an arbiter. The generator outputs a composite image for learning how to produce a more realistic image, and the discriminator determines whether the input image is a composite image or a realistic image.
S3, training a generative pair resistance network model;
and performing iterative training on the generated countermeasure network model constructed by using the obtained original tumor image and the generated tumor image until the generated countermeasure network model is iterated to a preset number of times, completing training on the generated countermeasure network model, and obtaining the trained generated countermeasure network model.
In the training process of the countermeasure generation network, the purpose of the generator is to generate a real image as much as possible to deceive the discriminator, the discriminator is to distinguish the image generated by the generator from the real image, the training generator and the discriminator form a dynamic game process by alternately training the generator and the discriminator, and finally the image generated by the trained generator is enough to be fake-fake, namely, infinitely close to the real image.
In the embodiment of the present invention, as shown in fig. 2, in the training process of the Pix2Pix to generate the confrontation network model, firstly, the real image (Target) is captured through a Mask (Mask) from a slice containing a liver tumor, an Input generated tumor synthetic image (Input) for generating the confrontation network model is obtained through preprocessing, and an image (Output) generated by the generator is obtained through the training of the Pix2Pix to generate the confrontation network model. The images generated by the generator are used together with the real images to train the discriminator, so that the discriminator learns to distinguish the real images from the composite images. The generator is trained using the results of the last trained discriminator and learns to synthesize a real image. At the end of several training periods, the generator may generate images that the discriminator cannot distinguish from the real images.
To train Pix2Pix to generate the antagonistic network model, 150 sessions were run using Adam optimizer with initial learning rate of 0.0002 and momentum of 0.5. The parameters of the generator loss function are set to GAN weight 1 and L1 weight 100.
S4, extracting a random liver tumor image from the CT slice data set of the liver tumor, preprocessing the random liver tumor image, and inputting the preprocessed random liver tumor image into the trained generation confrontation network model to obtain a liver tumor image set;
s5, the obtained liver tumor image set is implanted into a CT slice set not containing liver tumor, and a data set for liver tumor image segmentation is obtained.
A method for comprehensively implanting focus on CT liver slice to improve the network performance of liver lesion segmentation. By adjusting parameters such as the size, the shape, the position, the internal structure, the average strength and the like of the tumor image, the training set of the liver tumor image segmentation network can be enriched, and the performance of the liver tumor image segmentation network is improved.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected therein by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.
Claims (4)
1. A liver tumor image sample augmentation method based on generation of an antagonistic network is characterized by comprising the following steps:
s1, constructing a pair training set;
the method comprises the steps of obtaining a CT slice containing the liver tumor, extracting a tumor image in the CT slice containing the liver tumor to obtain an original tumor image, and preprocessing the original tumor image to obtain a generated tumor image;
s2, constructing and generating a confrontation network model;
the generation countermeasure network model comprises a generator and an arbiter;
s3, training a generative pair resistance network model;
performing iterative training on a generated countermeasure network model constructed by using the obtained original tumor image and the generated tumor image until the generated countermeasure network model is iterated to a preset number of times, completing training of the generated countermeasure network model, and obtaining the trained generated countermeasure network model;
s4, extracting a random liver tumor image from the CT slice data set of the liver tumor, preprocessing the random liver tumor image, and inputting the preprocessed random liver tumor image into the trained generation confrontation network model to obtain a liver tumor image set;
s5, the obtained liver tumor image set is implanted into a CT slice set not containing liver tumor, and a data set for liver tumor image segmentation is obtained.
2. The method of claim 1, wherein extracting the tumor image from the CT slice containing the liver tumor comprises adding a mask to the tumor region in the CT slice containing the liver tumor and extracting the mask.
3. The method according to claim 1, characterized in that the pre-processing comprises in particular modifying the shape, size, position and inner edges of the tumor image.
4. The method of claim 1, wherein the generating the competing network model comprises a Pix2Pix network or a SPADE network.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110291119.9A CN112967263A (en) | 2021-03-18 | 2021-03-18 | Liver tumor image sample augmentation method based on generation of countermeasure network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110291119.9A CN112967263A (en) | 2021-03-18 | 2021-03-18 | Liver tumor image sample augmentation method based on generation of countermeasure network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112967263A true CN112967263A (en) | 2021-06-15 |
Family
ID=76279322
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110291119.9A Pending CN112967263A (en) | 2021-03-18 | 2021-03-18 | Liver tumor image sample augmentation method based on generation of countermeasure network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112967263A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113538224A (en) * | 2021-09-14 | 2021-10-22 | 深圳市安软科技股份有限公司 | Image style migration method and device based on generation countermeasure network and related equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109767440A (en) * | 2019-01-11 | 2019-05-17 | 南京信息工程大学 | A kind of imaged image data extending method towards deep learning model training and study |
| CN111275691A (en) * | 2020-01-22 | 2020-06-12 | 北京邮电大学 | Small sample tumor necrosis rate classification prediction device based on deep learning |
| CN112241766A (en) * | 2020-10-27 | 2021-01-19 | 西安电子科技大学 | Liver CT image multi-lesion classification method based on sample generation and transfer learning |
-
2021
- 2021-03-18 CN CN202110291119.9A patent/CN112967263A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109767440A (en) * | 2019-01-11 | 2019-05-17 | 南京信息工程大学 | A kind of imaged image data extending method towards deep learning model training and study |
| CN111275691A (en) * | 2020-01-22 | 2020-06-12 | 北京邮电大学 | Small sample tumor necrosis rate classification prediction device based on deep learning |
| CN112241766A (en) * | 2020-10-27 | 2021-01-19 | 西安电子科技大学 | Liver CT image multi-lesion classification method based on sample generation and transfer learning |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113538224A (en) * | 2021-09-14 | 2021-10-22 | 深圳市安软科技股份有限公司 | Image style migration method and device based on generation countermeasure network and related equipment |
| CN113538224B (en) * | 2021-09-14 | 2022-01-14 | 深圳市安软科技股份有限公司 | Image style migration method and device based on generation countermeasure network and related equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110570492B (en) | CT artifact suppression method, device and medium based on neural network | |
| CN112634283A (en) | Hip joint segmentation model establishment method using small sample image training and application thereof | |
| Hong et al. | End-to-end unpaired image denoising with conditional adversarial networks | |
| WO2019015466A1 (en) | Method and apparatus for verifying person and certificate | |
| CN109598722B (en) | Image analysis method based on recurrent neural network | |
| CN109949215B (en) | Low-dose CT image simulation method | |
| CN110675461A (en) | CT image recovery method based on unsupervised learning | |
| CN110782474A (en) | Deep learning-based method for predicting morphological change of liver tumor after ablation | |
| CN112949838B (en) | Convolutional neural network based on four-branch attention mechanism and image segmentation method | |
| CN110097557B (en) | Medical image automatic segmentation method and system based on 3D-UNet | |
| Kjer et al. | Patient-specific estimation of detailed cochlear shape from clinical CT images | |
| CN111260650A (en) | Spine CT sequence image segmentation method and system | |
| WO2023197612A1 (en) | Automatic data augmentation-based medical image segmentation method | |
| Sornam et al. | A new linear adaptive swarm intelligence approach using back propagation neural network for dental caries classification | |
| CN106023205A (en) | Medical image segmentation method based on simplified PSO (Particle Swarm Optimization) and 2D maximum entropy threshold | |
| CN112967263A (en) | Liver tumor image sample augmentation method based on generation of countermeasure network | |
| Liang et al. | Synthesis and edition of ultrasound images via sketch guided progressive growing GANS | |
| Kantipudi et al. | Color channel perturbation attacks for fooling convolutional neural networks and a defense against such attacks | |
| Lv et al. | Automatic segmentation of temporal bone structures from clinical conventional CT using a CNN approach | |
| Peng et al. | Dfgc 2021: A deepfake game competition | |
| WO2022016461A1 (en) | Image metal artifact reduction method | |
| Bharti et al. | Qemcgan: quantized evolutionary gradient aware multiobjective cyclic gan for medical image translation | |
| Zhao et al. | Attractive deep morphology-aware active contour network for vertebral body contour extraction with extensions to heterogeneous and semi-supervised scenarios | |
| Schwab et al. | Multimodal medical image registration using particle swarm optimization with influence of the data's initial orientation | |
| Mikhailov et al. | A deep learning-based interactive medical image segmentation framework |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210922 Address after: 102629 Room 401, building 1, 38 Yongda Road, Daxing biomedical industrial base, Zhongguancun Science and Technology Park, Daxing District, Beijing Applicant after: Beijing precision diagnosis Medical Technology Co.,Ltd. Address before: Room 102, block B2, phase II, software new town, tianguba Road, Yuhua Street office, high tech Zone, Xi'an, Shaanxi 710000 Applicant before: Xi'an Zhizhen Intelligent Technology Co.,Ltd. |
|
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20210615 |