WO2018179219A1 - Système informatique, méthode diagnostique à visée animale et programme associé - Google Patents

Système informatique, méthode diagnostique à visée animale et programme associé Download PDF

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Publication number
WO2018179219A1
WO2018179219A1 PCT/JP2017/013252 JP2017013252W WO2018179219A1 WO 2018179219 A1 WO2018179219 A1 WO 2018179219A1 JP 2017013252 W JP2017013252 W JP 2017013252W WO 2018179219 A1 WO2018179219 A1 WO 2018179219A1
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animal
image
result
analysis
information
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PCT/JP2017/013252
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English (en)
Japanese (ja)
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俊二 菅谷
佳雄 奥村
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株式会社オプティム
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Priority to PCT/JP2017/013252 priority Critical patent/WO2018179219A1/fr
Priority to JP2018522159A priority patent/JPWO2018179219A1/ja
Publication of WO2018179219A1 publication Critical patent/WO2018179219A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

Definitions

  • the present invention relates to a computer system, an animal diagnosis method, and a program for diagnosing an animal.
  • Non-Patent Document 1 It is known to diagnose animals by image analysis (see Non-Patent Document 1). Such diagnosis performs image analysis using a visible light image, an infrared image, an X-ray image, an ultrasonic image, or the like to diagnose pregnancy or a disease.
  • Non-Patent Document 1 pregnancy and illness are determined based only on acquired image data, and a plurality of data resources are not analyzed and predicted.
  • the present invention is a computer system that analyzes a correlation between possible prediction results (for example, estrus period and disease) from image analysis and a plurality of data resources, and performs prediction with higher accuracy than analysis of a single image,
  • An object is to provide an animal diagnosis method and program.
  • the present invention provides the following solutions.
  • the present invention is a computer system for diagnosing an animal, Animal image obtaining means for obtaining an animal image of the animal; Animal image analysis means for image analysis of the acquired animal image; Animal information acquisition means for acquiring animal information of the animal; Animal information analysis means for analyzing the acquired animal information; Correlation analysis means for analyzing the correlation between the image analysis result and the analysis result; An animal diagnostic means for diagnosing the animal based on the analyzed correlation result; A computer system is provided.
  • a computer system for diagnosing an animal acquires an animal image of the animal, performs image analysis on the acquired animal image, acquires animal information on the animal, and analyzes the acquired animal information. The correlation between the image analysis result and the analysis result is analyzed, and the animal is diagnosed based on the analyzed correlation result.
  • the present invention is a computer system category, but also in other categories such as animal diagnosis methods and programs, the same actions and effects according to the category are exhibited.
  • a computer system for analyzing a correlation between possible prediction results from image analysis and a plurality of data resources, and performing prediction with higher accuracy than analysis of a single image. It becomes possible to provide.
  • FIG. 1 is a diagram showing an outline of the animal diagnosis system 1.
  • FIG. 2 is an overall configuration diagram of the animal diagnosis system 1.
  • FIG. 3 is a functional block diagram of the computer 10.
  • FIG. 4 is a flowchart showing the image analysis learning process executed by the computer 10.
  • FIG. 5 is a flowchart showing the learning process for animal information analysis executed by the computer 10.
  • FIG. 6 is a flowchart showing animal diagnosis processing executed by the computer 10. It is.
  • FIG. 1 is a diagram for explaining an outline of an animal diagnosis system 1 which is a preferred embodiment of the present invention.
  • the animal diagnosis system 1 includes a computer 10 and is a computer system that diagnoses animals.
  • the animal diagnosis system 1 is a computer system that diagnoses cattle.
  • the computer 10 stores, measures, and inspects various imaging devices such as a visible light camera, an infrared camera, an X-ray camera, a CT (Computed Tomography), an ultrasonic camera, and the like, which are not shown, and external resources different from images.
  • imaging devices such as a visible light camera, an infrared camera, an X-ray camera, a CT (Computed Tomography), an ultrasonic camera, and the like, which are not shown, and external resources different from images.
  • various devices to be performed various sensors for measuring environmental data such as animal step count data, livestock charts that are history data of diseases and estrus, BCS (Body Condition Score), not shown computers, various tests such as blood tests Is a computing device communicably connected to an inspection device or the like that performs the above.
  • the computer 10 acquires an animal image of an animal (step S01).
  • the computer 10 acquires at least one of a visible light image, an infrared image, an X-ray image, a CT scan image, or an ultrasonic image as an animal image. In the following description, it demonstrates as what acquired the visible light image of the cow.
  • the computer 10 performs image analysis on the acquired animal image (step S02).
  • the computer 10 analyzes the feature amount of the animal image (estrus sign, characteristic behavior at the time of estrus, estrus mucus, etc.). For example, the computer 10 analyzes the characteristic amount of the cow from the visible light image, and follows, rides, discipline during rest, thrusting during lying, allowance of riding, mucus outflow, alignment, fight, jaw rest, frame , Analyze the riding detection instrument.
  • the computer 10 learns by associating the animal image stored in advance with the diagnosis result performed on the animal image, and based on the learned result, image analysis is performed on the animal image acquired this time. Good.
  • the computer 10 acquires animal information of animals (step S03).
  • the computer 10 acquires, as animal information, at least one of environmental data such as step count data measured by various sensors, livestock charts that are history data of diseases and estrus, blood test results, or BCS. In the following description, it is assumed that step count data has been acquired.
  • the computer 10 analyzes the acquired animal information (step S04).
  • the computer 10 analyzes whether or not an animal corresponds to, for example, estrus, respiratory disease, wound disease, parasitic disease, reproduction disorder, gastrointestinal disease, metabolic disease, and the like.
  • the computer 10 may learn by associating previously stored animal information and a diagnosis result performed on the animal information, and analyze the animal information acquired this time based on the learned result.
  • the computer 10 analyzes the correlation between the image analysis result and the analysis result (step S05). For example, as a result of image analysis, the computer 10 obtains results such as cow movement, estrus mucus from the vulva, and cows lined up in one example, and as a result of analysis, estrus from pedometer data or livestock charts. These correlations are analyzed when stage symptoms are acquired.
  • the computer 10 diagnoses an animal based on the analyzed correlation result (step S06). For example, the computer 10 determines that the cow is in the estrus period based on the analyzed correlation result.
  • FIG. 2 is a diagram showing a system configuration of the animal diagnosis system 1 which is a preferred embodiment of the present invention.
  • the animal diagnosis system 1 is composed of a computer 10 and a public network (Internet network, third generation, fourth generation communication network, etc.) 5 and is a computer system for diagnosing animals.
  • the computer 10 is the above-described computing device having the functions described later.
  • FIG. 3 is a functional block diagram of the computer 10.
  • the computer 10 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), etc. as the control unit 11, and a device for enabling communication with other devices as the communication unit 12. For example, a WiFi (Wireless Fidelity) compatible device compliant with IEEE 802.11 is provided.
  • the computer 10 also includes a data storage unit such as a hard disk, a semiconductor memory, a recording medium, or a memory card as the storage unit 13. Further, the computer 10 includes, as the processing unit 14, a device for executing various processes such as image processing, state diagnosis, and learning process.
  • control unit 11 reads a predetermined program, thereby realizing an animal image acquisition module 20, a diagnosis result acquisition module 21, and an animal information acquisition module 22 in cooperation with the communication unit 12. Further, in the computer 10, the control unit 11 reads a predetermined program, thereby realizing the storage module 30 in cooperation with the storage unit 13. Further, in the computer 10, the control unit 11 reads a predetermined program, so that the learning module 40, the image analysis module 41, the animal information analysis module 42, the correlation analysis module 43, and the diagnosis module cooperate with the processing unit 14. 44 is realized.
  • FIG. 4 is a flowchart of the image analysis learning process executed by the computer 10. Processing executed by each module described above will be described together with this processing.
  • the animal image acquisition module 20 acquires animal images of animals (step S10).
  • the animal image acquired by the animal image acquisition module 20 is, for example, at least one of a visible light image, an infrared image, an X-ray image, a CT scan image, or an ultrasonic image.
  • the animal image acquisition module 20 may acquire these animal images from a corresponding imaging device, or may acquire via a computer or the like (not shown). In the following description, the animal image acquisition module 20 will be described as acquiring a visible light image of a cow as an animal image.
  • the animal image acquisition module 20 may acquire animal images from a database or the like stored in an external computer (not shown).
  • the storage module 30 stores animal images (step S11).
  • the storage module 30 identifies the identifier of the animal that acquired the animal image this time (animal name, management number, preset reference number, identifier that can uniquely identify other animals, etc.), animal image, Are stored in association with each other. Note that the storage module 30 may store only animal images.
  • the diagnosis result acquisition module 21 acquires the diagnosis result of the animal corresponding to the animal image acquired this time (step S12).
  • the diagnosis result acquisition module 21 acquires a diagnosis result from, for example, an external computer (not shown) that stores livestock records or the like, a terminal device held by a veterinarian or a livestock related person, and the like.
  • the diagnosis result in the present embodiment is, for example, the presence or absence of estrus, identification of a disease, identification of necessary treatment, and the like.
  • the learning module 40 learns the animal image stored in the storage module 30 and the diagnosis result acquired by the diagnosis result acquisition module 21 in association with each other (step S13). In step S13, the learning module 40 learns at least one of the above-described visible light image, infrared image, X-ray image, CT scan image, or ultrasonic image in association with the diagnosis result.
  • the storage module 30 stores the learned result (step S14).
  • the animal diagnosis system 1 executes the above-described image analysis learning process a sufficient number of times and stores the learning result.
  • FIG. 5 is a flowchart of animal information analysis learning processing executed by the computer 10. Processing executed by each module described above will be described together with this processing.
  • the animal information acquisition module 22 acquires animal information of animals (step S20).
  • the animal information acquired by the animal information acquisition module 22 includes, for example, environmental data such as step count data measured by various sensors, livestock medical records that are history data of diseases and estrus, blood test results, or at least BCS results.
  • environmental data such as step count data measured by various sensors, livestock medical records that are history data of diseases and estrus, blood test results, or at least BCS results.
  • the animal information acquisition module 22 may acquire such animal information from various corresponding devices or the like, or may acquire it via a computer or the like (not shown). In the following description, the animal information acquisition module 22 will be described as acquiring step count data as animal information.
  • the storage module 30 stores animal information (step S21).
  • the storage module 30 associates the identifier of the animal that acquired the animal information this time (the name of the animal, the management number, a preset reference number, an identifier that can uniquely identify other animals, etc.) and the animal information.
  • the storage module 30 may store only animal information.
  • the diagnosis result acquisition module 21 acquires the diagnosis result of the animal corresponding to the animal information acquired this time (step S22).
  • the diagnosis result acquisition module 21 acquires a diagnosis result from, for example, an external computer (not shown) that stores livestock records or the like, a terminal device held by a veterinarian or a livestock related person, and the like.
  • the diagnosis result in the present embodiment is, for example, the presence or absence of estrus, identification of a disease, identification of necessary treatment, and the like.
  • the learning module 40 learns by associating the animal information stored in the storage module 30 with the diagnosis result acquired by the diagnosis result acquisition module 21 (step S23). In step S23, the learning module 40 associates at least one of environmental data such as step count data measured by the various sensors described above, livestock charts that are history data of diseases and estrus, blood test results, or BCS with the diagnosis results. To learn.
  • environmental data such as step count data measured by the various sensors described above, livestock charts that are history data of diseases and estrus, blood test results, or BCS with the diagnosis results.
  • the storage module 30 stores the learned result (step S24).
  • the animal diagnosis system 1 executes the above-described learning process for animal information analysis a sufficient number of times and stores the learning result.
  • the above is the learning process for animal information analysis.
  • FIG. 6 is a flowchart of the animal diagnosis process executed by the computer 10. Processing executed by each module described above will be described together with this processing. In the following description, the animal diagnosis system 1 will be described as diagnosing a cow based on the visible light image of the cow and the number of steps data.
  • the animal image acquisition module 20 acquires animal images of animals (step S30).
  • the animal image acquired by the animal image acquisition module 20 is, for example, at least one of a visible light image, an infrared image, an X-ray image, a CT scan image, or an ultrasound image.
  • the animal image acquisition module 20 may acquire these animal images from a corresponding imaging device, or may acquire via a computer or the like (not shown).
  • the image analysis module 41 performs image analysis on the acquired animal image (step S31).
  • step S31 an animal image is image-analyzed based on the result learned by the learning module 40.
  • the image analysis module 41 analyzes the feature amount of the animal image acquired this time (estrus sign, characteristic behavior at the time of estrus, estrus mucus, etc.).
  • the image analysis module 41 analyzes a plurality of candidates such as image parts and features necessary for making a diagnosis from the learning result.
  • the image analysis module 41 extracts, for example, a feature amount of a cow from an animal image, follows, rides, riding, discipline during rest, thrusting behavior during lying, allowance of riding, mucus outflow, alignment, fight, jaw rest, Analyze flamen and norigami detectors.
  • the image analysis module 41 is not necessarily limited to identifying estrus or disease as a result of image analysis, but may only obtain information for diagnosis described later. For example, the image analysis module 41 may obtain preliminary information for diagnosis from the result of image analysis.
  • the animal information acquisition module 22 acquires animal information of animals (step S32).
  • the animal information acquisition module 22 acquires, as animal information, at least one of environmental data such as step count data measured by various sensors, livestock charts that are history data of diseases and estrus, blood test results, or BCS. To do.
  • the animal information acquisition module 22 may acquire such animal information from various corresponding devices or the like, or may acquire it via a computer or the like (not shown).
  • the animal information analysis module 42 analyzes the acquired animal information (step S33).
  • step S33 the animal information analysis module 42 analyzes the animal information based on the result learned by the learning module 40.
  • the animal information analysis module 42 analyzes a plurality of candidates for animal information necessary for making a diagnosis from the learned result.
  • the animal information analysis module 42 analyzes whether or not an animal corresponds to, for example, estrus, respiratory disease, wound disease, parasitic disease, reproductive disorder, gastrointestinal disease, metabolic disease or the like.
  • the animal information analysis module 42 acquires step count data as animal information, and analyzes that there is a suspicion of the estrus period as a symptom corresponding to the step count data.
  • the animal information analysis module 42 is not necessarily limited to the one that identifies estrus and disease as a result of the analysis, and may only obtain information for diagnosis described later. For example, the animal information analysis module 42 may obtain preliminary information for diagnosis from the analysis result.
  • the correlation analysis module 43 analyzes the correlation between the result of image analysis by the image analysis module 41 and the result of analysis by the animal information analysis module 42 (step S34).
  • step S34 for example, the correlation analysis module 43 analyzes the result of image analysis by the image analysis module 41, the analysis result that the cow's movement, the estrus mucus flowed out from the vulva, and the cows are lined up, As a result of the analysis by the information analysis module 42, the correlation with the analysis result that the estrus period is suspected is analyzed.
  • the correlation analysis module 43 analyzes the correlation as a score. That is, the correlation analysis module 43 evaluates as a score how much the diagnosis obtained from the analysis result and the diagnosis obtained from the analysis result have a correlation.
  • the correlation analysis module 43 evaluates the degree of correlation between the diagnosis results of the respective items obtained as a result of the analysis and the diagnosis results of the respective items obtained as a result of the analysis.
  • the correlation analysis module 43 evaluates a high correlation as a high score, and evaluates a low correlation as a low score.
  • the diagnosis module 44 diagnoses an animal based on the analyzed correlation result (step S35).
  • the diagnosis module 44 identifies the estrus or disease of the animal based on the combination of the diagnosis results with the highest score as the result of the analyzed correlation. For example, the diagnosis module 44 specifies that the current animal is in the estrus period based on the above-described image analysis result, the above-described animal information analysis result, and the correlation analysis result.
  • the learning module 40 learns the current diagnosis result in association with the animal image and the animal information (step S36).
  • the storage module 30 stores the learned result (step S37).
  • the computer 10 performs a diagnosis in consideration of the result learned this time at the subsequent diagnosis.
  • the computer 10 may transmit the diagnosis result to a terminal device or the like held by a veterinarian or animal husbandry not shown.
  • the diagnosis result may be transmitted to this terminal device.
  • a specific method of treatment information such as a map and contact information of the nearest corresponding facility, and various types of information such as a risk level may be transmitted together.
  • the terminal device that has received the various types of information may notify the various types of information by display or voice.
  • the above is animal diagnosis processing.
  • the means and functions described above are realized by a computer (including a CPU, an information processing apparatus, and various terminals) reading and executing a predetermined program.
  • the program is provided, for example, in a form (SaaS: Software as a Service) provided from a computer via a network.
  • the program is provided in a form recorded on a computer-readable recording medium such as a flexible disk, CD (CD-ROM, etc.), DVD (DVD-ROM, DVD-RAM, etc.).
  • the computer reads the program from the recording medium, transfers it to the internal storage device or the external storage device, stores it, and executes it.
  • the program may be recorded in advance in a storage device (recording medium) such as a magnetic disk, an optical disk, or a magneto-optical disk, and provided from the storage device to a computer via a communication line.

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Abstract

Le but de la présente invention est de pourvoir à : un système informatique qui analyse, à partir d'une analyse d'image et de multiples ressources de données, des corrélations entre des résultats de prédiction possibles et exécute une prédiction à une précision supérieure à celle obtenue par analyse d'une seule image ; une méthode diagnostique à visée animale ; et un programme associé. La solution selon l'invention porte sur un système informatique capable de diagnostiquer un animal, où le système acquiert une image de l'animal, effectue une analyse d'image sur l'image acquise de l'animal, acquiert des informations d'ordre animal concernant l'animal, analyse les informations d'ordre animal acquises, analyse la corrélation entre le résultat de l'analyse d'image et le résultat de l'analyse des informations d'ordre animal, puis diagnostique l'animal sur la base du résultat de l'analyse de corrélation.
PCT/JP2017/013252 2017-03-30 2017-03-30 Système informatique, méthode diagnostique à visée animale et programme associé WO2018179219A1 (fr)

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JP2018522159A JPWO2018179219A1 (ja) 2017-03-30 2017-03-30 コンピュータシステム、動物診断方法及びプログラム

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