Full-slice digital imaging two-step focusing restoration method based on deep learning

A technology of digital imaging and deep learning, applied in image enhancement, image analysis, image data processing, etc., to achieve the effect of saving the cost of instrument experiments

Inactive Publication Date: 2020-12-04
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] Considering the limitations of traditional methods, the present invention uses advanced machine learning algorithms to solve the quasi-focus restoration problem of full-slice digital imaging

Method used

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  • Full-slice digital imaging two-step focusing restoration method based on deep learning
  • Full-slice digital imaging two-step focusing restoration method based on deep learning
  • Full-slice digital imaging two-step focusing restoration method based on deep learning

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Embodiment Construction

[0029] The specific implementation of the method of the present invention will be further described below.

[0030] to combine Figure 1 to Figure 3 As shown, a deep learning-based full-slice digital imaging two-step quasi-focus restoration method disclosed in this embodiment includes the following steps:

[0031] Step a, input out-of-focus image;

[0032] Step b, automatically focusing on the network;

[0033] Step c, predicting the quasi-focus distance;

[0034] Step d, distance compensation half quasi-focus image;

[0035] Step e, quasi-focus restoration network;

[0036] Step f, quasi-focus image,

[0037] The neural network method is used to realize the two-step quasi-focus restoration function of the whole slice digital imaging.

[0038] Specifically, the input defocused images come from z-stack image stacks obtained by axial scanning movement of different sub-image lateral positions, and each sub-image position obtains 20 positive and negative defocused images and...

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Abstract

The invention is based on the field of biomedical instruments, and particularly relates to a full-slice digital imaging two-step quasi-focus restoration method based on deep learning. The method provides a learning method based on a secondary compensation network, and mainly comprises an automatic focusing network and a quasi-focus restoration network. The designed refocusing network can calculatethe focusing distance corresponding to the out-of-focus image; moving the microscope workbench, performing motion compensation on the focusing distance, and performing secondary photographing to obtain a semi-focusing image; through dual-channel joint end-to-end network design of a focusing image and a semi-focusing image, the method realizes high-precision out-of-focus image restoration of full-slice digital imaging, and performs software virtualization on traditional full-slice digital pathological imaging hardware.

Description

technical field [0001] The present invention is based on the field of biomedical instruments, takes deep learning technology as the core, and specifically relates to a full-slice digital imaging two-step quasi-focus restoration method based on deep learning, which can be widely used in the fields of instrument science, artificial intelligence, medical imaging and automation, etc. Research. Background technique [0002] In recent years, advanced digital pathology imaging technology has been widely researched and applied. Whole Slide Images (WSI, Whole Slide Images), that is, virtual microscopy, can collect traditional microscopic sections in the form of digital images, which can achieve arbitrary computer access, easy storage, and communication between researchers and doctors. Off-site transmission, etc. Whole slide digital imaging is crucial in bioimaging research, such as in areas such as cancer analysis and disease prediction. At present, the US Food and Drug Administra...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06N3/04G06F30/27
CPCG06T5/001G06F30/27G06T2207/20081G06T2207/20084G06T2207/10056G06N3/045
Inventor 刘贤明李强
Owner HARBIN INST OF TECH
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