A tissue classification method and device based on cardiovascular ivoct images

Abstract

The present invention relates to a method and device for tissue classification based on cardiovascular IVOCT images, wherein the method includes: step 1, acquiring a plurality of marked IVOCT images; step 2, establishing an IVOCT image sample set, and storing the IVOCT images The sample set is divided into a training sample set and a test sample set; Step 3, constructing a convolutional neural network structure; Step 4, using the training sample set to train the convolutional neural network to obtain a CNN model; Step 5, incorporating The test sample set is input into the CNN model to obtain tissue type maps corresponding to different organizations. In the embodiment of the present invention, the CNN model has two output terminals, which respectively display the outline and internal structure of the tissue, which solves the problem that in the prior art, the resolution of the output image is slightly small, and some image information is lost, resulting in tissue boundaries. Shows unclear technical issues.

Description

technical field [0001] The invention belongs to biological tissue imaging technology, in particular to a method and device for tissue classification based on cardiovascular IVOCT images. Background technique [0002] A biopsy is a common medical test in which a pathologist looks at a sample of tissue taken from a subject under a microscope to determine the nature or extent of disease. Typically, tissue needs to be cut into very thin sections and stained before it can be viewed under a microscope. Optical coherence tomography (OCT) is an alternative to non-destructive optical imaging modalities that can provide high-resolution 3D images of tissue from biopsy samples without staining. Optical coherence microscopy (OCM) combines the advantages of OCT and confocal microscopy to provide high-resolution cellular images. [0003] Normal arteries have a uniform layered structure composed of intima, media and adventitia, but when blood vessels are diseased, different types of tissu...

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Problems solved by technology

[0003] Normal arteries have a uniform layered structure consisting of intima, media and adventitia, but when blood vessels are diseased, different types of tissues will be contained in the blood vessels. Therefore, it is necessary to classify and detect these different tissues. However, , until now, the detection and classification of these tissues mainly rely on manual work, which is very time-consuming

[0004] A convolutional network for biomedical image segmentation is proposed in the prior art. This method uses a contraction path to capture content, an enlargement path to precisely locate, and two paths to form a U-shape, called U-Net. The final output image resolution of U-Net will be slightly smaller than the original image, resulting in the loss of image information and unable to truly restore the organizational structure

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