WO2018028789A1 - Procédé et ensemble pour déterminer au moins une implication, en particulier pathologique - Google Patents

Procédé et ensemble pour déterminer au moins une implication, en particulier pathologique Download PDF

Info

Publication number
WO2018028789A1
WO2018028789A1 PCT/EP2016/069142 EP2016069142W WO2018028789A1 WO 2018028789 A1 WO2018028789 A1 WO 2018028789A1 EP 2016069142 W EP2016069142 W EP 2016069142W WO 2018028789 A1 WO2018028789 A1 WO 2018028789A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
implication
module
information
kernel
Prior art date
Application number
PCT/EP2016/069142
Other languages
German (de)
English (en)
Inventor
Clemens Otte
Original Assignee
Siemens Aktiengesellschaft
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Aktiengesellschaft filed Critical Siemens Aktiengesellschaft
Priority to PCT/EP2016/069142 priority Critical patent/WO2018028789A1/fr
Publication of WO2018028789A1 publication Critical patent/WO2018028789A1/fr

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0075—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/129—Using chemometrical methods
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the invention relates to a method for detecting at least one, in particular pathological, implication according to claim 1 and an arrangement for detecting at least one, in particular pathological, implication according to claim 11.
  • a spectrometer in the medical field can be used to determine whether a tissue sample contains cancerous tissue by examining tissue parts.
  • Example ⁇ instance can this optical spectroscopy for use kom ⁇ men, wherein the tissue sample with light, especially in the near-infrared spectroscopy (NIRS) is irradiated with short-wave infrared light and then the intensity of the re ⁇ inflected light as a function of the wavelength of the light is measured.
  • NIRS near-infrared spectroscopy
  • spectroscopy is not only used to classify objects, but also for tasks that use the statistical method of regression to make predictions about quantitative properties, such as blood alcohol values. Furthermore, spectroscopy is also used in condition monitoring, for example in the context of an industrial application, in which vibration amplitudes are measured as a function of the frequency.
  • the complexity of the spectra therefore requires advanced methods of preprocessing and analysis.
  • the analysis of this requires, for example, a model for ⁇ Kali-calibration of the data generated, wherein for this purpose the characteristics of the respective measured sample are known and are set in relation to the each ⁇ weilig detected spectra.
  • the object of the invention is to provide a solution that overcomes the existing for the use of spectroscopy chal ⁇ aging, in particular for the medical field, in demge ⁇ genüber improved manner.
  • This object is achieved by the method for detecting at least one, in particular pathological, implication according to claim 1 by its features, and by the Anord ⁇ tion for detecting at least one, in particular pathological, implication according to claim 11.
  • the first and / or second Vorverarbei ⁇ processing module is operated in a further development of the invention so as to the set by means of a Merkmalsex Exerciseon, in particular intensities of each wavelength or frequency, correlated from the zugrun- delitis with the object, in particular spectrometrically detected, first information of data, in particular as coefficients of a function according to signal processing.
  • a machine processing by means of mathematical, in particular statistical methods of signal processing is possible.
  • Each preprocessing module provides its own view of the underlying object.
  • a view can be, for example, as ⁇ by defining that a pre-processing an egg gene mathematical filter function uses (for example, the first derivative of the spectrum over the wavelengths) on the spectra, and provides additional information.
  • a view can also be defined in that a separate measurement rate of the underlying object takes place, into ⁇ particular in a different spectral range than in the first measurement.
  • a view not spectral data of the underlying object beinhal ⁇ th, in particular genomic and / or proteomic Informati ⁇ tions in the medical field This allows, for example, to capture patient-specific factors that may have an influence on the spectral measurement and to take them into account in the calculation of the object properties in the prediction module.
  • the method is developed in such a way that the prediction module is operated in a processor-controlled manner such that training is initially carried out in a training phase using methods of machine learning for data for which the implication to be predicted is known.
  • linear models eg partial least squares
  • neural networks such as the multilayer perceptron, or kernel-based methods such as the Gaussian process model or the Support Vector Machine.
  • the invention is also advantageous further formed when the Vor kausmodulen a particular-use a kernel-based method, prediction module follows, wherein the prediction module, the data records of the pre-processing each ⁇ wells processing functions by separate sub-models and / or kernel and this by means of a mathematical function, in particular addition or Multiplication, combined.
  • the arrangement according to the invention for detecting at least one, in particular pathological, implication has means for carrying out the method according to one of the preceding claims.
  • the inventive arrangement helps by the realization of the method according to the invention by its implementation for the realization of the said advantages of the method and its developments.
  • FIG 3 shows a further embodiment in which a further development is shown in specific ⁇ matic representation in which each set of features that is known from the wavelet transformation or compression, so-called “wavelet decomposition" is performed.
  • FIG. 1 schematically shows the approach known from the prior art, in which a spectrum RS generated, for example, by NIRS is fed to preprocessing provided by a preprocessing module A.
  • preprocessing should reduce irrelevant information in the spectrum and highlight relevant information from the spectrum.
  • it is intended to reduce interfering influences of the measuring process, such as varying distances between the sensor and the sample, using suitable methods, such as, for example, offset correction or standardization of the spectrum.
  • An essential disadvantage of the approach illustrated in FIG. 1 is that the model provides the information about the sample only in a single representation (ie, a single view) as a result of preprocessing A.
  • the first inventive step is, therefore, to combine a plurality among ⁇ Kunststoffmug views with Ideally, complementing mutually independent as possible information about the sample.
  • Another essential inventive approach underlying the solution is further the transformation of learning in spectral data to a so-called multi-view learning problem known from machine learning.
  • the starting point is the spectrum RS of a sample produced, for example, by NIRS.
  • the data of the spectrum are transformed as signals suitable for processing in parallel to a plurality n of preprocessing modules AI ... An.
  • each of the preprocessing modules AI... An generates a set of features using algorithms that differ in each case, so that a plurality of feature vectors ⁇ ,... ⁇ are transformed at the prediction module B as signals suitable for processing.
  • the prediction module B can now, as it has available by the different ⁇ union preprocessing algorithms different views of one and the same object (ie complementary information about the sample has), this combined be ⁇ seek in such a way that the elaboration of relevant data from irrelevant data and learning can be performed in a much more optimized way, ultimately producing more accurate prediction data shaped to output signals.
  • the multi-view inventive learning problem transformation also allows another not directly represented In ⁇ play.
  • further spectroscopes can be the source of the spectra and thus feature vectors, these spectroscopes differing, for example, in the spectral measuring range. Also conceivable, at least individual Merkmalsvekto ⁇ ren Xi can be obtained from non-spectroscopic sources.
  • the dimension of the total vector X is here equal to the sum of the dimensions of the individual vectors XI... Xn.
  • the modeling in B takes place here on the total feature vectors X.
  • the second possibility is to process, instead of the concatenation to ei ⁇ nem total vector, the individual feature vectors XI ... Xn separated in module B. This is advantageous in particular ⁇ sondere heterogeneous in nature records as th example, in the combination of spectroscopic DA genomic non-spectroscopic (eg /
  • Separate processing of the feature vectors XI... Xn in module B can take place, for example, by n sub-models being trained separately on the XI... Xn and their n model predictions f (Xl), f (Xn) being suitably combined, for example about weighted averaging.
  • the models in module B can be, for example, linear models, neural networks or kernel-based methods such as Gaussian process models or support vector machines.
  • a variant embodiment of the invention uses kernel ⁇ based methods in the prediction module B. This function defines a similarity ei ne kernel k (X, Y) between two each by their feature vectors X and Y represented objects.
  • a well-functioning kernel for spectral data is, for example, the so-called MLP kernel known in the literature, which is characterized by a w 2 X T Y + a b 2
  • X and Y represent combined vectors of two spectra and the so-called hyperparameters a, a w , a b be optimized during exercise.
  • the function h can form the weighted sum and / or the weighted product of the individual kernels.
  • One advantage of this is that the individual kernels work in smaller spaces, so fewer total parameters need to be trained by machine learning. For example, instead of using the above-mentioned embodiment with MLP Kernel instead of concatenating to a 100-dimensional space, it may be split into two kernels, each operating in a 50-dimensional space.
  • N views N feature sets ver ⁇ work ⁇ N kernels are processed by M and thus ei ⁇ nige kernel process a concatenation of feature sets, while others merely supplied a feature set as the basis of their processing to get.
  • One advantage of the kernel is that various hyperparameter a kernel can also be interpreted as a measure of the importance of the input side this size often. This can help validate the model and identify spectral regions whose values are relevant to the prediction.
  • Gauß Gauß rational model
  • the schematic representation shows a so-called wavelet transformation. So also a processor-based signal processing of the resulting (spectral) data.
  • Decomposition which, according to their function, have a thinned-out Presentation of the input data, for example, for the kernel generated because many of the coefficients generated by the wavelet transform at least approximately the value of zero.
  • This transformation is applied to two views; generated in part to a detected by spectroscopic methods such as NIRS of egg ⁇ ner sample spectrum and on the other to a second point of view, the processing for example by an operation performed Signalver- of the spectrum of the first derivative Spekt ⁇ rums.
  • coefficients thinned and thinned out by wavelet decomposition can then, as indicated in the figure by the plus sign, be joined together by addition to combined feature vectors and passed on to the various models or kernels mentioned above, for example.
  • a spectrum of 250 wavelengths may be less than 50
  • Wavelet coefficients are represented. The concatenation of two views of 250 wavelengths initially would thus yield a combined feature vector with a dimension of less than 100 instead of a dimension of 500.

Landscapes

  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Pathology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • General Health & Medical Sciences (AREA)
  • Surgery (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Biophysics (AREA)
  • Animal Behavior & Ethology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Fuzzy Systems (AREA)
  • Chemical & Material Sciences (AREA)
  • Immunology (AREA)
  • General Physics & Mathematics (AREA)
  • Biochemistry (AREA)
  • Evolutionary Computation (AREA)
  • Analytical Chemistry (AREA)
  • Mathematical Physics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physiology (AREA)
  • Psychiatry (AREA)
  • Signal Processing (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

L'invention concerne un procédé ainsi qu'un ensemble permettant de déterminer au moins une implication, en particulier pathologique, selon laquelle il est déterminé, sur la base d'un premier ensemble de données produit par mesure spectrométrique d'un échantillon d'objet et qui est en corrélation avec ledit objet et sur la base d'au moins un deuxième ensemble de données en corrélation avec le premier ensemble de données, sur la base d'une combinaison du premier et du second ensemble de données, le premier ensemble de données étant formé par un premier module de prétraitement et le second ensemble de données, par un second module de prétraitement, puis étant acheminé jusqu'à au moins un module de prédiction au niveau duquel intervient une sortie de données qui sert de base au processus de détermination.
PCT/EP2016/069142 2016-08-11 2016-08-11 Procédé et ensemble pour déterminer au moins une implication, en particulier pathologique WO2018028789A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/EP2016/069142 WO2018028789A1 (fr) 2016-08-11 2016-08-11 Procédé et ensemble pour déterminer au moins une implication, en particulier pathologique

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/EP2016/069142 WO2018028789A1 (fr) 2016-08-11 2016-08-11 Procédé et ensemble pour déterminer au moins une implication, en particulier pathologique

Publications (1)

Publication Number Publication Date
WO2018028789A1 true WO2018028789A1 (fr) 2018-02-15

Family

ID=56799436

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2016/069142 WO2018028789A1 (fr) 2016-08-11 2016-08-11 Procédé et ensemble pour déterminer au moins une implication, en particulier pathologique

Country Status (1)

Country Link
WO (1) WO2018028789A1 (fr)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6258576B1 (en) * 1996-06-19 2001-07-10 Board Of Regents, The University Of Texas System Diagnostic method and apparatus for cervical squamous intraepithelial lesions in vitro and in vivo using fluorescence spectroscopy
US6411907B1 (en) * 1997-07-25 2002-06-25 Intelligent Optical Systems, Inc. Accurate tissue injury assessment
DE102013200058B3 (de) * 2013-01-04 2014-06-26 Siemens Aktiengesellschaft Automatisierte Auswertung der Rohdaten eines MR-Spektrums

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6258576B1 (en) * 1996-06-19 2001-07-10 Board Of Regents, The University Of Texas System Diagnostic method and apparatus for cervical squamous intraepithelial lesions in vitro and in vivo using fluorescence spectroscopy
US6411907B1 (en) * 1997-07-25 2002-06-25 Intelligent Optical Systems, Inc. Accurate tissue injury assessment
DE102013200058B3 (de) * 2013-01-04 2014-06-26 Siemens Aktiengesellschaft Automatisierte Auswertung der Rohdaten eines MR-Spektrums

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HORI R ET AL: "A combined FT-IR microscopy and principal component analysis on softwood cell walls", CARBOHYDRATE POLYMERS, APPLIED SCIENCE PUBLISHERS, LTD. BARKING, GB, vol. 52, no. 4, 1 June 2003 (2003-06-01), pages 449 - 453, XP004411424, ISSN: 0144-8617, DOI: 10.1016/S0144-8617(03)00013-4 *

Similar Documents

Publication Publication Date Title
DE102014012745B4 (de) Hintergrundkorrektur in Emissionsspektren
EP3481275B1 (fr) Procédé et dispositif pour déterminer un degré de détérioration des cheveux et procédé pour déterminer un moyen pour traiter les cheveux spécifique à l'utilisateur
DE112010004023B4 (de) Filmdickenmessvorrichtung und Filmdickenmessverfahren
DE112011100038T5 (de) Verfahren zur Entwicklung von Erkennungsalgorithmen für die laserinduzierte PlasmaEmissionsspektroskopie
DE69837192T2 (de) Eichmethode für instrumente zur spektrografischen analyse
EP2480868B1 (fr) Procédé de production et de détection d'un spectre raman
EP3159681B1 (fr) Procédé et dispositif de détermination pouvant être automatisée de la limite de détection et de l'erreur relative lors de la quantification de la concentration d'une substance à analyser dans un échantillon
WO2019020259A1 (fr) Prétraitement pour algorithme de classification
EP3865052A1 (fr) Système de mesure et procédé de mesure pour déterminer le facteur de protection d'une crème solaire
DE102005009195B4 (de) Verfahren und Vorrichtung zum Korrigieren eines Spektrums
DE4203587A1 (de) Quantitatives spektralanalytisches verfahren zur bestimmung von bestandteilen in einer probe
EP3707496B1 (fr) Identification d'au moins une caractéristique spectrale dans un spectre d'un échantillon pour analyse de constituants
DE102010050198B3 (de) Verfahren zur Bestimmung von chemischen Bestandteilen von festen oder flüssigen Substanzen mit Hilfe der THz-Spektroskopie
DE60301764T2 (de) Gerät und Verfahren zum Nachweis von Zirkulardichroismus im Infrarotbereich
WO2018028789A1 (fr) Procédé et ensemble pour déterminer au moins une implication, en particulier pathologique
DE112019006092T5 (de) Lose gekoppeltes inspektions- und metrologiesystem zur überwachung eines produktionsprozesses mit hohem volumen
EP3619618A1 (fr) Procédé de configuration assistée par ordinateur d'un modèle fondé sur données en fonction de données d'apprentissage
DE102019205512B4 (de) Vorrichtung und verfahren zur auswertung von spektralen eigenschaften eines messobjekts
DE60126600T2 (de) Analyseverfahren für stoffmischungen
DE102017104872A1 (de) ATR Spektrometer und Verfahren zum Analysieren der chemischen Zusammensetzung einer Probe
DE102014218354B4 (de) Verfahren zur Gewinnung von in einem Ergebnis einer NMR-Messung kodierter Information
DE112019002366T5 (de) Resonanzuntersuchung von produktionsgegenständen durch überprüfung von belegprodukten
WO2023001752A1 (fr) Procédé et système d'analyse d'un échantillon sur la base de données
WO2018059662A1 (fr) Sélection de longueur d'onde dans la classification de tissus par imagerie hyperspectrale
WO2018029088A1 (fr) Procédé permettant de vérifier la concordance d'un échantillon de bière avec une bière de référence

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16756644

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 16756644

Country of ref document: EP

Kind code of ref document: A1