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

Cerebrospinal fluid cell image automatic identification and counting method

A cerebrospinal fluid and cell technology, applied in the field of cerebrospinal fluid cytology detection, can solve the problems of error-prone and low accuracy of cell recognition, and achieve the effect of improving accuracy and good effect

Pending Publication Date: 2021-02-05
SHANGHAI JIAO TONG UNIV +2
View PDF10 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The method of automatic cerebrospinal fluid cell recognition only cuts the border of the cells. This method will make the recognition results affected by the cell background and surrounding cells, and it is more prone to errors when the cells are dense.
Affected by cerebrospinal fluid cell samples, the number of certain types of cells is very small, and the identification accuracy of these cells is often low

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
  • Cerebrospinal fluid cell image automatic identification and counting method
  • Cerebrospinal fluid cell image automatic identification and counting method
  • Cerebrospinal fluid cell image automatic identification and counting method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0037] A method for automatic identification and counting of cerebrospinal fluid cell images, the specific steps are:

[0038] Step 1: Preprocess the original image of cerebrospinal fluid cells, mark each original image of cerebrospinal fluid cells, draw the edge contour line of each cell according to the connection of dense points, and mark the cell type, save the coordinates of each dense point And the cell type is in json format;

[0039]Step 2: Randomly divide the original image of cerebrospinal fluid cells into three parts: training set, verification set and test set according to 4:1:1;

[0040] Step 3: According to the labeling results of the cells, cut the edge contour line to segment each cell, and save it as a segmentation data set according to the cell type, and record the corresponding original image and the part of the training set, verification set and test set.

[0041] Step 4: Convert the original image data sets of cerebrospinal fluid cells into COCO format fo...

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 cerebrospinal fluid cell image automatic identification and counting method. The method comprises the following steps: 1, preprocessing an original cerebrospinal fluid cell image and storing the original cerebrospinal fluid cell image as an original image set; 2, segmenting a single cell image from the cerebrospinal fluid cell original image set obtained in the step 1 according to a labeling result, and storing the single cell image as a segmented image set according to a cell type; 3, correspondingly dividing the cerebrospinal fluid cell original image set obtained in the step 1 and the cerebrospinal fluid cell segmented image set obtained in the step 2 into a training set, a verification set and a test set; 4, constructing a convolutional neural network by usinga Pytorch deep learning framework; 5, constructing a convolutional neural network by using a Pytorch deep learning framework; 6, inputting a segmented test set obtained in the step 4 into the classification network obtained in the step 5; and 7, obtaining the positions of the cells in the segmented test set and segmentation, classification and counting results obtained in the step 5 according tothe single cerebrospinal fluid cell original image. According to the method, the accuracy of cell identification can be improved under the condition that the number of samples is small, and automaticidentification and counting are achieved.

Description

technical field [0001] The invention belongs to the technical field of cytological detection of cerebrospinal fluid, in particular to a method for automatic identification and counting of cerebrospinal fluid cell images. Background technique [0002] Cerebrospinal fluid is a colorless, transparent, viscous fluid located between the arachnoid and meninges of the meninges and in the spinal cord. It is produced by specialized ependymal cells in the choroidal plexus of the ventricle and is taken up by arachnoid granules. There is about 125mL of cerebrospinal fluid in each human body, and about 500mL is produced every day. It is a pure physiological saline containing microglial cells, mainly used as a mechanical buffer for the cerebral cortex, providing basic mechanical and immune protection for the brain inside the skull, and also plays a vital role in the automatic regulation of cerebral blood flow in the brain important role. Cerebrospinal fluid occupies the subarachnoid sp...

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): G06T7/00G06T7/11G06T7/12G06N3/04G06K9/62G06K9/32
CPCG06T7/0012G06T7/11G06T7/12G06T2207/20081G06T2207/20084G06T2207/30024G06T2207/30016G06T2207/30242G06V10/25G06N3/045G06F18/24G06F18/214
Inventor 王士林王振海马晓峰王国玮何学仙王晨苏许先伟
Owner SHANGHAI JIAO TONG UNIV