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Methods for identifying biological material by microscopy

A technology of biomaterials, learning methods, applied in the domain of discrete objects with optional quantification of biological origin for the identification of opinions, able to solve the problems of low counts, false negative results, loss, etc.

Pending Publication Date: 2021-01-08
ヴァーディクトホールディングスプロプライエタリーリミテッド
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

It is entirely possible to inadvertently lose some or all of the target material during the purification procedure, resulting in falsely low counts, or even false negative results

Method used

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  • Methods for identifying biological material by microscopy
  • Methods for identifying biological material by microscopy
  • Methods for identifying biological material by microscopy

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0237] Example 1: Machine identification of parasite eggs

[0238] Parasite eggs are identified using a computer comprising a parasite egg identification algorithm according to the present invention. A total of 65 images were analyzed by computer for each of Haemonchus, Taenia mortensi, Leucocele, Osteria, and Trichostrongylus. The results of precision and recall are shown in Table 2 below:

[0239]

[0240] It is conceivable that the performance of the algorithm could be improved by training with a larger number of training images and / or a more diverse range of training images.

example 2

[0241] Example 2: Comparison of crop-fitting and template-based methods during recognition

[0242] Using the clip-fit ​​method versus the Performance of the detection algorithm for the template approach. The results are shown in Table 3 below:

[0243]

[0244] It is to be noted that the crop fitting method is generally superior.

example 3

[0245] Example 3: Comparison of crop-fit ​​and template-based methods in the identification of individual eggs and egg clusters

[0246] For each of Haemonchus (HC), Taenia mortensi (Mon), Nematode elegans (Nem), Oster and Trichostrongylus (Trich), with or without egg contact , evaluated the performance of detection algorithms using crop-fitting methods versus template-based methods. The results are shown in Table 4 below:

[0247]

[0248] It is generally noted that the crop-fit ​​method outperforms the template method when a single egg is involved, whereas the opposite is observed when eggs are in contact.

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PUM

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Abstract

The present invention relates generally to the field of computer-based image recognition. More particularly, the invention relates to methods and systems for the identification, and optionally the quantitation of, discrete objects of biological origin such as cells, cytoplasmic structures, parasites, parasite ova, and the like which are typically the subject of microscopic analysis. The inventionmay be embodied in the form of a method for training a computer to identify a target biological material in a sample. The method may include accessing a plurality of training images, the training images being obtained by light microscopy of one or more samples containing a target biological material and optionally a non-target biological material. The training images are cropped by a human or a computer to produce cropped images, each of which shows predominantly the target biological material. A human then identifies the target biological material in each of the cropped images where identification is possible, and associating an identification label with each of the cropped images where identification was possible. A computer-implemented feature extraction method is then applied to each labelled cropped image. A computer-implemented learning method is then applied to each labelled cropped image to associate extracted features of a biological material with a target biological material.

Description

technical field [0001] The present invention relates generally to the field of computer-based image recognition. More specifically, the present invention relates to methods and systems for identifying and optionally quantifying discrete objects of biological origin, such as cells, cytoplasmic structures, parasites, parasite eggs, etc., which are typically microscopic object of analysis. Background technique [0002] A wide variety of biological materials are analyzed by light microscopy for a variety of reasons. One such use of microscopy is in the diagnosis of medical, veterinary and botanical diseases. For example, microscopy can be used to identify a particular type of host cell among other host cells, such as cancer cells in a population of normal cells. Another example is the identification of infectious microorganisms among other biological or non-biological materials in a sample. Yet another example is the identification and quantification of parasite ovum species...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H30/00G06T7/00G06N5/00G06N20/00G16H30/40
CPCG06T7/0012G06T7/73G06T2207/20132G06T2207/20021G06T2207/20081G06T2207/10056G06T2207/30004G06T2207/20104G06V20/695G06V20/698G16H30/40G16H50/20G16H10/40G06N3/08G06N20/10G06V10/945G06N3/045G16B35/00G16H30/20G06F18/40G06T7/194G06T7/11G06F16/51G06N20/00G06T2207/20092G06F18/2148
Inventor A·库明L·林C·麦卡锡M·杜恩L·嘉文S·卡坦A·唐
Owner ヴァーディクトホールディングスプロプライエタリーリミテッド
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