CN112419324A - Medical image data expansion method based on semi-supervised task driving - Google Patents

Medical image data expansion method based on semi-supervised task driving Download PDF

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CN112419324A
CN112419324A CN202011329403.2A CN202011329403A CN112419324A CN 112419324 A CN112419324 A CN 112419324A CN 202011329403 A CN202011329403 A CN 202011329403A CN 112419324 A CN112419324 A CN 112419324A
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data set
data
medical image
expansion
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CN112419324B (en
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潘晓光
王小华
尚方信
令狐彬
张娜
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Shanxi Sanyouhe Smart Information Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • 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
    • G06T3/00—Geometric image transformation in the plane of the image
    • G06T3/40—Scaling the whole image or part thereof
    • G06T3/4007—Interpolation-based scaling, e.g. bilinear interpolation
    • 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
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS

Abstract

The invention belongs to the field of image data expansion, and particularly relates to a medical image data expansion method based on semi-supervised task driving, which comprises the following steps: data acquisition: acquiring a related medical image data set, and labeling the category of the medical image data set; data expansion: the data expansion of the medical image is realized by using a task-driven data expansion method; the construction method comprises the following steps: constructing a data expansion method for data set expansion of medical images; data set saving: and combining the expanded data set with the original data set, and storing the data set. The invention solves the problem of obtaining robust segmentation in limited training data setting by using a semi-supervised task-driven data expansion method, and enables the generated enhanced image to improve the segmentation of the medical image. The invention is used for the expansion of medical image data.

Description

Medical image data expansion method based on semi-supervised task driving
Technical Field
The invention belongs to the field of image data expansion, and particularly relates to a medical image data expansion method based on semi-supervised task driving.
Background
The data expansion is a simple technology for expanding a training set based on generation of a synthetic image-label pair, the idea is to convert an image in a mode that a label is kept unchanged, or to define conversion for the image and the label, but the segmentation performance of a medical image cannot be obviously improved by expansion methods such as random affine transformation, random elastic transformation, random contrast transformation and the like, limited labeled data can be learned by a new task-driven data expansion method, and the synthetic data generator can improve the optimization performance for segmentation tasks.
Accurate medical image segmentation is crucial for current clinical applications, but it is difficult to acquire a large number of annotation examples for medical images, resulting in poor segmentation accuracy of medical images. At present, data expansion of medical images is carried out by methods such as random affine transformation, random elastic transformation, random contrast transformation and the like, but experimental results show that data generated by the current method is not true in nature and the effect is not ideal.
Disclosure of Invention
Aiming at the technical problem that the data of the current medical image data expansion is unreal, the invention provides a medical image data expansion method based on semi-supervised task driving, which has high efficiency, high segmentation accuracy and low cost.
In order to solve the technical problems, the invention adopts the technical scheme that:
a medical image data expansion method based on semi-supervised task driving comprises the following steps:
s1, data acquisition: acquiring a related medical image data set, and labeling the category of the medical image data set;
s2, data expansion: the data expansion of the medical image is realized by using a task-driven data expansion method;
s3, the construction method comprises the following steps: constructing a data expansion method for data set expansion of medical images;
s4, data set storage: and combining the expanded data set with the original data set, and storing the data set.
In the data acquisition in S1, the data set is screened and divided by acquiring a related medical image common data set, so as to construct a medical image data set.
In S3, the construction method is used to generate an extended data set, (X)G,YG)＝G((XL,YL),z；wG) Wherein G (·, ·; w is aG) Transformation method for data expansion, z being the random component of the transformation, wGFor the transformed parameters, wherein a data expansion method G, by defining two condition generators, constructing shape and intensity variations for the columns of deformation field generators and intensity field generators, respectively, by which method an image transformation of the input image is performed to obtain an expanded data set, wherein the deformation field generator G is trainedvTransformation information for outputting deformation fields, GvHas a transformation parameter of wGvBy rendering the data set image XLAnd a randomly decimated z-vector as input for generating a dense pixel-by-pixel deformation field
Figure BDA0002795275690000021
According to the generated deformation field v, carrying out bilinear interpolation on the input image and the corresponding label to obtain an extended data set XGvAnd corresponding tag set YGvThe expression mode is as follows:
Figure BDA0002795275690000022
training intensity field generator GIFor outputting additive intensity mask transformations, GIHas a transformation parameter of wGIInputting training data set image XLAnd a randomly extracted unit Gaussian distributed z-vector as input, inputting an additive strength mask
Figure BDA0002795275690000023
Then add Δ I to XLIn (1), obtaining an extended image set XGIAnd corresponding tag set YGIThe expression mode is as follows:
Figure BDA0002795275690000024
data expansion by a deformation field generator and intensity field generator method:
Figure BDA0002795275690000025
the data combination method in the step S4 includes: combining the expanded data set with the original data set, and setting XLFor training the data set, YLFor a corresponding tag dataset, (X)N,YN)＝(XL∪XG,YL∪YG) Said X isG、YGRepresenting the augmented data set and the corresponding tag data set.
Compared with the prior art, the invention has the following beneficial effects:
the invention solves the problem of obtaining robust segmentation in limited training data setting by using a semi-supervised task-driven data expansion method, and enables the generated enhanced image to improve the segmentation of the medical image.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A semi-supervised task driven medical image data expansion method, as shown in fig. 1, comprising the following steps:
step 1, data acquisition: a related medical image dataset is acquired and its categories are labeled.
Step 2, data expansion: data expansion of medical images is achieved using a task-driven data expansion method.
And step 3, a construction method comprises the following steps: a data expansion method is constructed for data set expansion of medical images.
And 4, data set storage: and combining the expanded data set with the original data set, and storing the data set.
Further, in the data acquisition in the step 1, the data set is screened and divided by acquiring a related medical image public data set, so as to construct a medical image data set.
Further, in step 3, a construction method is used to generate the augmented data set, (X)G,YG)＝G((XL,YL),z；wG) Wherein G (·,. cndot.). w is aG) Transformation method for data expansion, z being the random component of the transformation, wGFor the transformed parameters, wherein a data expansion method G, by defining two condition generators, constructing shape and intensity variations for the columns of deformation field generators and intensity field generators, respectively, by which method an image transformation of the input image is performed to obtain an expanded data set, wherein the deformation field generator G is trainedvTransformation information for outputting deformation fields, GvHas a transformation parameter of wGvBy rendering the data set image XLAnd a randomly decimated z-vector as input for generating a dense pixel-by-pixel deformation field
Figure BDA0002795275690000031
According to the generated deformation field v, carrying out bilinear interpolation on the input image and the corresponding label to obtain an extended data set XGvAnd corresponding tag set YGvThe expression mode is as follows:
Figure BDA0002795275690000032
training intensity field generator GIFor output additive strength mask transformation，GIHas a transformation parameter of wGIInputting training data set image XLAnd a randomly extracted unit Gaussian distributed z-vector as input, inputting an additive strength mask
Figure BDA0002795275690000033
Then add Δ I to XLIn (1), obtaining an extended image set XGIAnd corresponding tag set YGIThe expression mode is as follows:
Figure BDA0002795275690000034
data expansion by a deformation field generator and intensity field generator method:
Figure BDA0002795275690000035
further, the data combination method in step 4 is as follows: combining the expanded data set with the original data set, and setting XLFor training the data set, YLFor a corresponding tag dataset, (X)N,YN)＝(XL∪XG,YL∪YG)，XG、YGRepresenting the augmented data set and the corresponding tag data set.
Although only the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art, and all changes are encompassed in the scope of the present invention.

Claims (4)

1. A medical image data expansion method based on semi-supervised task driving is characterized in that: comprises the following steps:
s1, data acquisition: acquiring a related medical image data set, and labeling the category of the medical image data set;
s2, data expansion: the data expansion of the medical image is realized by using a task-driven data expansion method;
s3, the construction method comprises the following steps: constructing a data expansion method for data set expansion of medical images;
s4, data set storage: and combining the expanded data set with the original data set, and storing the data set.
2. The semi-supervised task driven medical image data augmentation method of claim 1, wherein: in the data acquisition in S1, the data set is screened and divided by acquiring a related medical image common data set, so as to construct a medical image data set.
3. The semi-supervised task driven medical image data augmentation method of claim 1, wherein: in S3, the construction method is used to generate an extended data set, (X)G,YG)＝G((XL,YL),z；wG) Wherein G (·, ·; w is aG) Transformation method for data expansion, z being the random component of the transformation, wGFor the transformed parameters, wherein a data expansion method G, by defining two condition generators, constructing shape and intensity variations for the columns of deformation field generators and intensity field generators, respectively, by which method an image transformation of the input image is performed to obtain an expanded data set, wherein the deformation field generator G is trainedvTransformation information for outputting deformation fields, GvHas a transformation parameter of wGvBy rendering the data set image XLAnd a randomly decimated z-vector as input for generating a dense pixel-by-pixel deformation field
Figure FDA0002795275680000011
According to the generated deformation field v, carrying out bilinear interpolation on the input image and the corresponding label to obtain an extended data set XGvAnd corresponding tag set YGvThe expression mode is as follows:
Figure FDA0002795275680000012
training intensity field generator GIFor outputting additive intensity mask transformations, GIHas a transformation parameter of wGITo transportImage X of training data setLAnd a randomly extracted unit Gaussian distributed z-vector as input, inputting an additive strength mask
Figure FDA0002795275680000013
Then add Δ I to XLIn (1), obtaining an extended image set XGIAnd corresponding tag set YGIThe expression mode is as follows:
Figure FDA0002795275680000014
data expansion by a deformation field generator and intensity field generator method:
Figure FDA0002795275680000015
4. the semi-supervised task driven medical image data augmentation method of claim 1, wherein: the data combination method in the step S4 includes: combining the expanded data set with the original data set, and setting XLFor training the data set, YLFor a corresponding tag dataset, (X)N,YN)＝(XL∪XG,YL∪YG) Said X isG、YGRepresenting the augmented data set and the corresponding tag data set.
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