Nonlinear optical image recognition method based on deep learning and feature enhancement

A nonlinear optical and feature enhancement technology, applied in the field of biomedical optical imaging and image processing, can solve the problems of cost and delay of the best treatment timing, and achieve high accuracy and specificity.

Active Publication Date: 2020-07-03
FUJIAN NORMAL UNIV
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However, traditional histopathological diagnosis usually requires complex procedures such as...

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  • Nonlinear optical image recognition method based on deep learning and feature enhancement
  • Nonlinear optical image recognition method based on deep learning and feature enhancement
  • Nonlinear optical image recognition method based on deep learning and feature enhancement

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[0035] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of this application, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0036] The technical solution in this application will be described below with reference to the accompanying drawings.

[0037] like figure 1 As shown, this embodiment provides a method for segmentation and classification of prostate cancer nonlinear optical images based on deep learning and feature enhancement, which specifically includes the...

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Abstract

The invention discloses a nonlinear optical image recognition method based on deep learning and feature enhancement. The method comprises the steps of obtaining a nonlinear optical image sample set generated based on two-photon laser fluorescence and second harmonic; developing a dual-channel adaptive threshold complementary set segmentation algorithm to segment the gland cavity structure in the prostate cancer tissue image; an image classification network model (AlexNet) is improved, and a segmented glandular cavity structure image is used as a newly added signal channel to carry out featureenhancement learning, so that intelligent Gleason classification with higher accuracy and specificity for unmarked sections of prostate cancer tissues is realized.

Description

technical field [0001] The invention relates to the field of biomedical optical imaging and image processing, in particular to a nonlinear optical image recognition method based on deep learning and feature enhancement. Background technique [0002] At present, the Gleason grading system is still one of the most powerful tools for the selection of treatment options and prognosis of prostate cancer. Gleason grading is mainly based on the structural characteristics of the glandular cavity in the histopathological map of the prostate. However, traditional histopathological diagnosis usually requires complex processes such as tissue fixation and staining, which takes a lot of time and may delay the best time for treatment. The non-linear optical microscopy imaging technology based on two-photon laser fluorescence and second harmonic generation of the tissue's own components can present the glandular lumen structure on which Gleason grading depends with high resolution without l...

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/62G06T5/00G06T7/00G16H50/20G16H70/60
CPCG06T5/007G06T7/0012G16H50/20G16H70/60G06T2207/10064G06T2207/10061G06T2207/30081G06T2207/30096G06V20/695G06V20/698G06V10/267G06V2201/03G06F18/253Y02A90/10
Inventor 朱小钦杨亲亲徐哲鑫蔡坚勇
Owner FUJIAN NORMAL UNIV
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