CN112132850A - Blood vessel boundary detection method, system and device based on modal learning - Google Patents

Blood vessel boundary detection method, system and device based on modal learning Download PDF

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CN112132850A
CN112132850A CN202010989178.9A CN202010989178A CN112132850A CN 112132850 A CN112132850 A CN 112132850A CN 202010989178 A CN202010989178 A CN 202010989178A CN 112132850 A CN112132850 A CN 112132850A
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image
modal
blood vessel
detected
model
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CN112132850B (en
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高智凡
刘修健
张贺晔
徐梓峰
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National Sun Yat Sen University
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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
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/253—Fusion techniques of extracted features
    • 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • 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
    • 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
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • 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
    • G06T2207/10101—Optical tomography; Optical coherence tomography [OCT]
    • 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
    • G06T2207/10132—Ultrasound image
    • 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
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular

Abstract

The invention discloses a method, a system and a device for detecting a blood vessel boundary based on modal learning, wherein the method comprises the following steps: acquiring batch images and dividing the images to obtain a hidden mode set and a target mode set; constructing a modal learning model based on a convolution automatic encoder and a bidirectional pyramid network structure; performing parameter optimization on the modal learning model according to the hidden modal set and the target modal set to obtain an optimized model; and acquiring an image to be detected and inputting the image to the optimization model to identify the boundary of the blood vessel. The system comprises: the device comprises an acquisition module, a construction module, a modal learning module and an identification module. The apparatus comprises a memory and a processor for performing the above-described mode learning based vessel boundary detection method. By using the invention, accurate blood vessel boundary detection can be still carried out in various blood vessel environments. The method, the system and the device for detecting the blood vessel boundary based on the modal learning can be widely applied to the field of blood vessel image processing.

Description

Blood vessel boundary detection method, system and device based on modal learning
Technical Field
The invention relates to the field of blood vessel image processing, in particular to a method, a system and a device for detecting a blood vessel boundary based on modal learning.
Background
Intracoronary imaging is an important medical imaging method in clinical practice to present high resolution images of the internal morphology of the coronary arteries by inserting a miniature saturated imaging probe into the coronary arteries from outside the human body. It mainly includes two kinds of imaging methods: intravascular ultrasound (IVUS) and Optical Coherence Tomography (OCT) to show different aspects of coronary artery disease. The blood Vessel Boundary Detection (VBDI) in the image in the coronary artery plays an important role in computer-aided image analysis, the VBDI is helpful for subsequent analysis, and the existing blood vessel boundary detection method based on the manually-made image characteristics is limited by the variability of blood vessel environments and cannot be well applied to various blood vessel environments.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a method, a system, and a device for detecting a blood vessel boundary based on modal learning, which can be well adapted to different blood vessel environments, thereby achieving accurate blood vessel boundary detection in various blood vessel environments.
The first technical scheme adopted by the invention is as follows: a blood vessel boundary detection method based on modal learning comprises the following steps:
acquiring batch images and dividing the images to obtain a hidden mode set and a target mode set;
constructing a modal learning model based on a convolution automatic encoder and a bidirectional pyramid network structure;
performing parameter optimization on the modal learning model according to the hidden modal set and the target modal set to obtain an optimized model;
and acquiring an image to be detected, inputting the image to be detected into the optimization model, and identifying to obtain a blood vessel boundary.
Further, the mode learning model comprises a first convolution automatic encoder, a second convolution automatic encoder and a bidirectional pyramid network, wherein the first convolution automatic encoder is used for image segmentation of the lumen region, the second convolution automatic encoder is used for image segmentation of the lumen region and the middle-membrane outer membrane region, and the bidirectional pyramid network is used for image feature extraction.
Further, the step of performing parameter optimization on the modal learning model based on the hidden modal set and the target modal set to obtain an optimized model specifically includes:
inputting the hidden mode set and the target mode set into a mode learning model;
respectively extracting corresponding modal characteristics based on a modal learning model, performing characteristic fusion, and outputting processed target modal information;
optimizing soft marks and hard marks in the modal learning model parameters according to the target modal information;
and repeating the optimization step to obtain an optimization model.
Further, the bidirectional pyramid network includes 5 cascaded modules, each of which includes 4 expansion convolutional layers.
Further, the step of acquiring the image to be detected and inputting the image to be detected into the optimization model to identify and obtain the blood vessel boundary specifically includes:
acquiring an image to be detected and inputting the image to be detected into an optimization model;
performing region image segmentation and image feature extraction on the image to be detected based on the optimization model, and outputting a target mode of the image to be detected;
and identifying and obtaining a blood vessel boundary according to the target modality of the image to be detected.
Further, the step of performing region image segmentation and image feature extraction on the image to be detected based on the optimization model and outputting a target modality of the image to be detected specifically includes:
performing image segmentation on an image to be detected based on a convolution encoder in the optimization model to obtain a blood vessel lumen area;
extracting image characteristics of the vessel lumen region according to the soft mark and the hard mark in the model to obtain an extraction result;
and outputting the target mode of the image to be detected according to the extraction result.
The second technical scheme adopted by the invention is as follows: a blood vessel boundary detection system based on modal learning, characterized by comprising the following modules:
the acquisition module is used for acquiring batch images and dividing the images to obtain a hidden mode set and a target mode set;
the construction module is used for constructing a modal learning model based on a convolution automatic encoder and a bidirectional pyramid network structure;
the modal learning module is used for carrying out parameter optimization on the modal learning model according to the hidden modal set and the target modal set to obtain an optimized model;
and the identification module is used for acquiring the image to be detected, inputting the image to be detected into the optimization model and identifying to obtain the blood vessel boundary.
The third technical scheme adopted by the invention is as follows: a modality-learning-based blood vessel boundary detection apparatus comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, the at least one program causes the at least one processor to implement a method of modal learning-based vessel boundary detection as described above.
The method, the system and the device have the advantages that: according to the method, the parameters of the modal learning model are optimized for multiple times through the hidden modal set and the target modal set until the optimized model meeting the preset rule is obtained, and finally, the accurate vessel boundary can be identified under the condition of single-mode input.
Drawings
FIG. 1 is a flow chart illustrating the steps of a method for detecting a blood vessel boundary based on modal learning according to the present invention;
fig. 2 is a structural block diagram of a blood vessel boundary detection system based on modal learning according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
As shown in fig. 1, the present invention provides a blood vessel boundary detection method based on modal learning, which includes the following steps:
s1, acquiring batch images and dividing the images to obtain a hidden mode set and a target mode set;
s2, constructing a modal learning model based on the convolution automatic encoder and the bidirectional pyramid network structure;
s3, carrying out parameter optimization on the modal learning model according to the hidden modal set and the target modal set to obtain an optimized model;
and S4, acquiring the image to be detected, inputting the image to be detected into the optimization model, and identifying to obtain the blood vessel boundary.
Specifically, the method specifically comprises two stages, wherein the first stage is hidden modality learning and the second modality distillation, the hidden modality learning firstly simultaneously inputs a hidden modality and a target modality into a model, characteristic expressions in the two imaging modalities are extracted through the model, and the characteristics are further fused, so that processed target modality information is output, the input and processing of one imaging modality are converted into the input and processing of multiple modalities, and the problem of Single Input Single Task (SIST) is solved by a multi-input multi-task (MIMT) structure; the modal distillation identifies the vessel boundaries in the case of applying the knowledge learned by the model to a single mode input, i.e., in the case of only one image input.
Further as a preferred embodiment of the method, the modal learning model includes a first convolution automatic encoder, a second convolution automatic encoder and a bidirectional pyramid network, the first convolution automatic encoder is used for image segmentation of the lumen region, the second convolution automatic encoder is used for image segmentation of the lumen region and the media adventitia region, and the bidirectional pyramid network is used for image feature extraction.
Specifically, in order to extract image features, the identification of different regions of the image, i.e., image segmentation, must be performed first. Only one region in the OCT image is a blood vessel lumen region, the IVUS image has two regions, namely a blood vessel lumen region and a tunica media outer membrane region (namely an MA region), two convolution automatic encoders are used in the process, the two convolution automatic encoders are fused with advanced image characteristics, one convolution automatic encoder is used for segmenting the lumen region in the OCT image and the IVUS image, and the other convolution automatic encoder is used for segmenting the lumen region and the tunica media outer membrane region in the IVUS image, so that the adaptability of the model is enhanced.
In addition, a loss function is included to enrich the error types of network optimization.
Further, as a preferred embodiment of the method, the step of performing parameter optimization on the modal learning model based on the hidden modal set and the target modal set to obtain an optimized model specifically includes:
inputting the hidden mode set and the target mode set into a mode learning model;
respectively extracting corresponding modal characteristics based on a modal learning model, performing characteristic fusion, and outputting processed target modal information;
optimizing soft marks and hard marks in the modal learning model parameters according to the target modal information;
and repeating the optimization step to obtain an optimization model.
Specifically, given Xt，Yt，{Xpi，Ypi}，i＝1，2，···，N；YtIs subjected to XtConstraint of (Y)piIs subjected to XpiConstraints, two sets of data have relevant learnable knowledge between them; the purpose is as follows: learn how to combine { Xt，XpiOne-to-one correspondence to { Y } in the range Ωt，YpiOn.
Wherein, Xt，YtTraining examples and labels for the target modality, respectively, N is the number of hidden modalities, Xpi，YpiAre respectively the ithThe training examples and labels of the modalities are hidden, Ω is the range of parameters for model learning.
As defined above, hidden mode learning is to convert X into XtCorresponds to YtThe SIST problem of (a) is converted into a data set { X }t，Xpi}。
Further as a preferred embodiment of the method, the bidirectional pyramid network includes 5 cascaded modules, each of which includes 4 expansion convolutional layer structures.
In particular, since the high-level feature maps of OCT and IVUS images have a fixed size of the receive field, to accommodate this problem and maintain image resolution, the present invention proposes a bidirectional pyramid network structure. The structure is a dense connection structure formed by connecting 5 cascade modules in the forward direction, wherein each module comprises 4 expansion convolution layers. The values of the expansion ratio in the block from low level to high level are: 2. 2, 4 and 8. And adding a pooling layer between every two adjacent modules for compressing the width and the height of the image. After the image is subjected to expansion convolution, a receiving field can be enlarged, and the loss of detailed information can be reduced by combining low-level high-resolution features and high-level low-resolution features in a densely connected forward structure. In the reverse direction, the outputs of all forward blocks are connected from high to low to generate four signatures. After convolution, each feature map is treated as the output of the bi-directional pyramid network and then accepts supervised information from the labels (i.e. assisted supervision) during network training. The bidirectional pyramid network structure can effectively extract image information of blood vessel regions with variable sizes and reserve blood vessel boundaries.
Further, as a preferred embodiment of the method, the step of acquiring the image to be detected, inputting the image to be detected into the optimization model, and identifying and obtaining the blood vessel boundary specifically includes:
acquiring an image to be detected and inputting the image to be detected into an optimization model;
performing region image segmentation and image feature extraction on the image to be detected based on the optimization model, and outputting a target mode of the image to be detected;
and identifying and obtaining a blood vessel boundary according to the target modality of the image to be detected.
As a preferred embodiment of the method, the step of performing region image segmentation and image feature extraction on the image to be detected based on the optimization model and outputting the target mode of the image to be detected specifically includes:
performing image segmentation on an image to be detected based on a convolution encoder in the optimization model to obtain a blood vessel lumen area;
extracting image characteristics of the vessel lumen region according to the soft mark and the hard mark in the model to obtain an extraction result;
and outputting the target mode of the image to be detected according to the extraction result.
As shown in fig. 2, a blood vessel boundary detection system based on modality learning includes:
the graph structure module is used for generating a graph structure according to the data relation;
the acquisition module is used for acquiring batch images and dividing the images to obtain a hidden mode set and a target mode set;
the construction module is used for constructing a modal learning model based on a convolution automatic encoder and a bidirectional pyramid network structure;
the modal learning module is used for carrying out parameter optimization on the modal learning model according to the hidden modal set and the target modal set to obtain an optimized model;
and the identification module is used for acquiring the image to be detected, inputting the image to be detected into the optimization model and identifying to obtain the blood vessel boundary.
Further in accordance with a preferred embodiment of the present system, the modality learning module further includes:
the input submodule is used for inputting the hidden mode set and the target mode set into the mode learning model;
the modal processing submodule is used for respectively extracting corresponding modal characteristics based on the modal learning model, performing characteristic fusion and outputting processed target modal information;
the optimization submodule is used for optimizing the soft marks and the hard marks in the modal learning model parameters according to the target modal information;
and the loop sub-module is used for repeating the optimization steps to obtain an optimization model.
As a further preferred embodiment of the present system, the identification module further includes:
the to-be-detected image acquisition submodule is used for acquiring an to-be-detected image and inputting the to-be-detected image into the optimization model;
the image processing submodule to be detected is used for carrying out region image segmentation and image feature extraction on the image to be detected based on the optimization model and outputting a target mode of the image to be detected;
and the target mode submodule of the image to be detected is used for identifying and obtaining the blood vessel boundary according to the target mode of the image to be detected.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.
A blood vessel boundary detection device based on modal learning:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, the at least one program causes the at least one processor to implement a method of modal learning-based vessel boundary detection as described above.
The contents in the above method embodiments are all applicable to the present apparatus embodiment, the functions specifically implemented by the present apparatus embodiment are the same as those in the above method embodiments, and the advantageous effects achieved by the present apparatus embodiment are also the same as those achieved by the above method embodiments.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (8)

1. A blood vessel boundary detection method based on modal learning is characterized by comprising the following steps:
acquiring batch images and dividing the images to obtain a hidden mode set and a target mode set;
constructing a modal learning model based on a convolution automatic encoder and a bidirectional pyramid network structure;
performing parameter optimization on the modal learning model according to the hidden modal set and the target modal set to obtain an optimized model;
and acquiring an image to be detected, inputting the image to be detected into the optimization model, and identifying to obtain a blood vessel boundary.
2. The method according to claim 1, wherein the modal learning model comprises a first convolution automatic encoder, a second convolution automatic encoder and a bidirectional pyramid network, the first convolution automatic encoder is used for image segmentation of the lumen region, the second convolution automatic encoder is used for image segmentation of the lumen region and the media adventitia region, and the bidirectional pyramid network is used for image feature extraction.
3. The method according to claim 2, wherein the step of performing parameter optimization on the modal learning model according to the hidden modal set and the target modal set to obtain an optimized model specifically comprises:
inputting the hidden mode set and the target mode set into a mode learning model;
respectively extracting corresponding modal characteristics based on a modal learning model, performing characteristic fusion, and outputting processed target modal information;
optimizing soft marks and hard marks in the modal learning model parameters according to the target modal information;
and repeating the optimization step to obtain an optimization model.
4. The method according to claim 3, wherein the bidirectional pyramid network comprises 5 cascaded modules, and each cascaded module comprises 4 expansion convolutional layer structures.
5. The method according to claim 4, wherein the step of acquiring the image to be detected, inputting the image to the optimization model, and identifying the boundary of the blood vessel includes:
acquiring an image to be detected and inputting the image to be detected into an optimization model;
performing region image segmentation and image feature extraction on the image to be detected based on the optimization model, and outputting a target mode of the image to be detected;
and identifying and obtaining a blood vessel boundary according to the target modality of the image to be detected.
6. The method according to claim 5, wherein the step of performing region image segmentation and image feature extraction on the image to be detected based on the optimization model and outputting the target modality of the image to be detected further comprises:
performing image segmentation on an image to be detected based on a convolution encoder in the optimization model to obtain a blood vessel lumen area;
extracting image characteristics of the vessel lumen region according to the soft mark and the hard mark in the model to obtain an extraction result;
and outputting the target mode of the image to be detected according to the extraction result.
7. A blood vessel boundary detection system based on modal learning is characterized by comprising the following modules:
the acquisition module is used for acquiring batch images and dividing the images to obtain a hidden mode set and a target mode set;
the construction module is used for constructing a modal learning model based on a convolution automatic encoder and a bidirectional pyramid network structure;
the modal learning module is used for carrying out parameter optimization on the modal learning model according to the hidden modal set and the target modal set to obtain an optimized model;
and the identification module is used for acquiring the image to be detected, inputting the image to be detected into the optimization model and identifying to obtain the blood vessel boundary.
8. A blood vessel boundary detection device based on modal learning, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement a method of modal learning-based vessel boundary detection as claimed in any one of claims 1 to 6.
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Citations (3)

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KR20170113251A (en) * 2016-03-24 2017-10-12 재단법인 아산사회복지재단 Method and device for automatic inner and outer vessel wall segmentation in intravascular ultrasound images using deep learning
US20190114773A1 (en) * 2017-10-13 2019-04-18 Beijing Curacloud Technology Co., Ltd. Systems and methods for cross-modality image segmentation
CN109859158A (en) * 2018-11-27 2019-06-07 邦鼓思电子科技（上海）有限公司 A kind of detection system, method and the machinery equipment on the working region boundary of view-based access control model

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20170113251A (en) * 2016-03-24 2017-10-12 재단법인 아산사회복지재단 Method and device for automatic inner and outer vessel wall segmentation in intravascular ultrasound images using deep learning
US20190114773A1 (en) * 2017-10-13 2019-04-18 Beijing Curacloud Technology Co., Ltd. Systems and methods for cross-modality image segmentation
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