Unlock instant, AI-driven research and patent intelligence for your innovation.

Deep learning-based grape embryo image processing method and device

An image processing device and deep learning technology, applied in the field of hydatidiform mole slice image processing, can solve the problems of low efficiency of clinical diagnosis and detection of hydatidiform mole, and achieve the effect of reducing excessive dependence and improving accuracy

Pending Publication Date: 2020-12-11
TSINGHUA UNIV
View PDF1 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] The object of the present invention is to provide a method and device for image processing of hydatidiform mole slices based on deep learning. The problem of low efficiency of fetal clinical diagnosis and detection

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Deep learning-based grape embryo image processing method and device
  • Deep learning-based grape embryo image processing method and device
  • Deep learning-based grape embryo image processing method and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044]A method for image processing of hydatidiform mole slices based on deep learning, the steps are as follows:

[0045] S1, put the hydatidiform mole slice into the microscope stage, focus the microscope, and obtain the hydatidiform hydatidiform slice scan picture under the microscope;

[0046] In this step, firstly, the hydatidiform hydatidiform slices need to be he-stained first, and then put the hydatidiform hydatidiform hydatidiform mole slices into the stage of the digital microscope after the he-stained hydatidiform mole slices, and the microscope is a digital microscope; Clear hydatidiform mole slices can be seen in the field of view of the microscope; finally, the slice scanning module obtains the hydatidiform hydatidiform mole slice scanning image under the microscope.

[0047] S2, because the acquired hydatidiform mole slice scan image has a large size, and the existing network input has requirements for the image size, so it is necessary to segment the image inpu...

Embodiment 2

[0079] The present embodiment also provides a mole slice image processing device based on deep learning, including:

[0080] A microscope, to magnify the tiny structures in slices of hydatidiform hydatidiform mole;

[0081] The slice scanning module is used to obtain the hydatidiform hydatidiform section scan map under the microscope;

[0082] The slice edema label map generation module is used to slice the mole slice scan image to obtain the scan image slice 2, and input the scan image slice 2 into the edema network b-net to obtain the slice edema label map of the mole slice, and All slice edema label maps are fused to obtain a slice edema label map;

[0083] A distribution map generating module, configured to obtain a slice edema distribution map according to the slice edema label map;

[0084] Additionally, you can include:

[0085] The section fluff label map generation module is used to cut the mole slice scan image into pieces to obtain the scan image slice one, and i...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a deep learning-based grape embryo image processing method and device, belongs to medical image detection of grape embryos in the technical field of medical images, and is usedfor solving the problem of low efficiency of clinical diagnosis and detection of grape embryos in the prior art. The method comprises the following steps: acquiring a grape embryo slice scanning graph under a microscope, inputting the grape embryo slice scanning graph into a fluff network and an edema network to obtain a slice fluff label graph and a slice edema label graph of the grape embryo slice scanning graph, and finally obtaining a slice edema distribution graph. According to the invention, image processing can be carried out on two different grape embryo pathological features of villiand edema through the villi network and the edema network to obtain a slice edema distribution diagram, and the distribution diagram is visually displayed to a clinician so as to visually obtain theslice edema area distribution situation.

Description

technical field [0001] The invention belongs to the technical field of medical imaging, and relates to a medical image detection of hydatidiform mole, in particular to a method for processing sliced ​​images of hydatidiform hydatidiform mole by using a deep learning method. Background technique [0002] The hydatidiform mole (HM) is a vesicular mass shaped like a bunch of grapes formed by the placenta after pregnancy. And the mole baby dies or forms a teratoma, and there are very few full-term babies. Under normal circumstances, 10% to 20% of hydatidiform mole will develop into malignant hydatidiform mole and choriocarcinoma. This kind of cancer will transfer through blood group mole, and if it is not treated in time, it will bring life threat to the patient . Therefore, the early pathological diagnosis of hydatidiform mole is of great significance to every sick pregnant woman. [0003] In the prior art, there are mainly two methods for the detection and screening of hyda...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/10G06T5/50G06N3/04G06N3/08
CPCG06T7/0012G06T7/10G06T5/50G06N3/08G06T2207/10061G06T2207/20021G06T2207/20221G06T2207/30044G06N3/045
Inventor 师丽朱承泽王松伟王治忠
Owner TSINGHUA UNIV