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WO2009013363A1 - Système d'estimation de la volumétrie pour l'allaitement maternel - Google Patents

Système d'estimation de la volumétrie pour l'allaitement maternel Download PDF

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Publication number
WO2009013363A1
WO2009013363A1 PCT/ES2007/000453 ES2007000453W WO2009013363A1 WO 2009013363 A1 WO2009013363 A1 WO 2009013363A1 ES 2007000453 W ES2007000453 W ES 2007000453W WO 2009013363 A1 WO2009013363 A1 WO 2009013363A1
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WO
WIPO (PCT)
Prior art keywords
breastfeeding
estimation
volumetry
previous
ultrasound
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
Application number
PCT/ES2007/000453
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English (en)
Spanish (es)
Inventor
Vicente Jorge Ribas Ripoll
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sabirmedical SL
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Sabirmedical SL
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Filing date
Publication date
Application filed by Sabirmedical SL filed Critical Sabirmedical SL
Publication of WO2009013363A1 publication Critical patent/WO2009013363A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Clinical applications
    • A61B8/0825—Clinical applications for diagnosis of the breast, e.g. mammography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
    • A61B5/1073—Measuring volume, e.g. of limbs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
    • A61B5/4261—Evaluating exocrine secretion production
    • A61B5/4288—Evaluating exocrine secretion production mammary secretions
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/43—Detecting, measuring or recording for evaluating the reproductive systems
    • A61B5/4306—Detecting, measuring or recording for evaluating the reproductive systems for evaluating the female reproductive systems, e.g. gynaecological evaluations
    • A61B5/4312—Breast evaluation or disorder diagnosis
    • 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
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms

Definitions

  • the present invention develops a system, including a digital ultrasonic scanning device, for estimating the volume of milk ingested by a newborn during the breastfeeding process, being configured as a handheld ultrasonic ultrasound device. .
  • the system provides the final measurement through the ultrasound information obtained by its operating procedures.
  • the patent WO2006054287 develops a system that integrates various devices integrated in a bra, with openings that allow breast exposure to breastfeed the newborn. Inside said bra are various ultrasound emitting / receiving probes, which use the Doppler Effect to calculate the flow of milk and from it to estimate the volume of milk taken by the newborn.
  • the US5827191 and WO2006003655 patents develop a system for measuring the volume of milk ingested by a newborn based on an elastic nipple covering through which, in order to measure it, the flow of milk taken.
  • Both systems have the disadvantage of establishing a physical barrier between the mother and the baby, disturbing not only the intimacy between the two, but also the breastfeeding process itself, since it hinders both the sucking of the infant and the production of milk derived from the stimulus direct nipple by the child.
  • one of the main objectives of the present invention is to solve the problem derived from the process of breastfeeding of newborns during the first six months of life providing real-time data to new mothers on the volume of milk ingested by their newborn.
  • Another of the objectives and advantages obtained with the ease and portability of the system of the present invention refers to the increase in the quality of life and comfort of new mothers and newborns since minimal contact is required for the realization of the estimation (requires only two pre and post suction scans), being another of the main objectives of the present invention to extend the breastfeeding process beyond the first six months of life.
  • the system for estimating volumetry in breastfeeding uses a contact ultrasonic device using the physical Back-Scatter principle with which a first scan of the breast of the breast is performed.
  • mother providing a first set of parameters which may include, the total volume of the breast, the estimated number of lobes and lobules (functional units of the mammary gland), the number of lactophores-channels, acoustic properties of the tissue, etc.
  • a fractal model is obtained through which the volume of milk stored (total capacity) in the lactophores and in the whole of the mammary gland (especially the lobules). This model also allows an estimation of the milk production rate once the volumetric reserve previously calculated with the model is consumed.
  • a preprocessing of the images is carried out prior to the estimation phase.
  • the system presented in this invention can use the discrete cosine transform (Discrete Cosinus Transform / DCT) or the autocorrelation matrix values of the input images.
  • the preferred implementation of this system performs said preprocessing by means of Wavelet transforms (Discrete Wavelet Transform /
  • the volume of milk ingested by the newborn is estimated through the implementation of a committee of neural networks of the type ⁇ Radial Basis Functions', which calculate a function that returns the volume ingested from Preprocessed images.
  • the neural networks committee is constituted by a set of networks, which is trained from a sampling of the input image, so that by averaging the outputs of said networks the variance of the estimate is compensated and, at the same time, the input of each neural network has a lower dimension.
  • This committee of neural networks can be combined with image processing techniques and, in particular, with the segmentation of preprocessed images to calculate the degree of collapse of the breast tree (ie of all breast tissue) and, as of this degree For compression, estimate the volume of milk ingested (this estimate is direct since the total volume of milk available from the fractal model of the breast is known).
  • Figure 1 shows a representation of the part bottom of the system for estimating breastfeeding where the ultrasonic emitting device is detailed.
  • Figure 2 shows a representation of the profile of the system for the estimation of breastfeeding where it is appreciated that it is a non-invasive handheld device.
  • Figure 3 shows a superior representation of the system for the estimation of breastfeeding where the screen where the data of the readings of the volumetry and the temporal evolution of the same are shown.
  • Figure 4 shows the high level architecture of the global system for ultrasonic capture and the implementation of estimation algorithms.
  • System components include an ultrasonic emitter, a transmission / reception switch, analog / digital converters FPGA or DSP devices and interconnection elements between plates.
  • Figure 5 shows the minimum, medium and maximum volume of milk stored in a breast (BSC) obtained from the application of the fractal model of the present invention.
  • Figure 6 shows the bank of filters that implement the DWT (Discrete Wavelet Transform) of the present invention.
  • the high-pass and low-pass components of the filter bank are detailed as well as the resolution levels for the calculation of the wavelet transformation.
  • Figure 7 shows the block diagram for the estimator based committee of neural networks of the Radial Basis Functions type, which estimate the volumetry of breastfeeding from the samples of preprocessed images (preprocessed ultrasound).
  • Figure 6 shows the block and operation diagram for the estimation of breastfeeding volumetry combining the fractal model defined in the present invention with image segmentation techniques.
  • the present invention consists of a system for estimating the volume of milk ingested by a newborn during breastfeeding whose data is evaluated in real time by means of an ultrasound capture device (1) linked with different digital subsystems (7, 8 , 9) that implement, respectively, a fractal model (7) of the human breast, a committee of classifiers based on neural networks (8) of the RBF type and, finally, another subsystem (9) that calculates the degree of collapse of the breast tree .
  • the preferred implementation of the system uses the digital fractal (7) and neural network (8) subsystems above, the breastfeeding estimator of the present invention can be integrated with the sub-system for measuring the collapse of the breast tree (9) to reduce the variance of the estimates made.
  • the device (1) consists of an ultrasonic transmitter and receiver (2), a transmission card responsible for generating the ultrasound pulses of the conformation of the ultrasound emission beam and the digital / analog conversion of said pulses and of the beam conformation ( ⁇ beaforming ') of the emitter.
  • said sending / receiving device (2) is implemented by a single DSP microcontroller device type FPGA.
  • It also consists of the device (2) of one or several receiving devices (5) (one for each channel according to the previous beam conformation) whose function is the reception of the pulses emitted by the sending card
  • said receiving elements are implemented by a DSP device
  • the number of ultrasound channels may vary between 8 and 64.
  • the device (1) consists of a connection panel between cards, which interconnects the sending card (4) and the receiving cards (5) (8-64 channels) with the controller card (6) which is responsible not only for control the receiving transmitter (2) but also on it the filters and detailed models are implemented then.
  • said controller is implemented by means of an FPGA type DSP device.
  • the invention has the appropriate means of computing and processing information that allow it to apply different procedures according to different subsystems for processing the information received.
  • Subsystem (7) implements a fractal model described below.
  • the channels bifurcate dichotomously, reducing their length and diameter systematically.
  • the breast tree ends in approximately 2 10 lobules.
  • Each of these lobules is divided into 4 generations of alveoli, which constitute the mammary gland.
  • structures can be deduced optimal by minimizing viscosity dissipation ( ⁇ viscous dissipation ') within the volume of the tree.
  • the ratio between the diameter and the length between the p-1 generation and the p generation is h p . If we denote V as the volume of a given channel, the channel reduced by a factor h results in a volume multiplied by a factor h 3 for each generation. For after p generations, the sizes will have been reduced by a factor hixh ⁇ x .. xh p so that the volume of a tree of N + l generations
  • V (n + l) V (n) + V or r n "1
  • r 2h 3 .
  • V V 0 ( 1 ⁇ rN1 ) + -2 N + 1 ⁇ h 3 1-r 3
  • the value of h can be calculated from the ultrasound taken in the subsystem (3) since this value is the relationship between the length of the lactophore channel and its radius.
  • the subsystem (4) is responsible for estimating this factor h and calculating the BSC, using the above equation from h and the number of terminal lactophores.
  • the fractal model presented above not only defines the BSC but also provides the theoretical basis for the subsystem (9) since during a milk intake, the amount of Breast tissue should be kept constant. In other words, this implies that any change within the chest during a milk intake will be due to a pressure drop (ie the structure of the tree of channels and lobules collapses during the 'Emptying of the chest). Therefore, the volume of milk consumed by the newborn can be estimated from the BSC and the degree of breast tissue collapse.
  • Figure 5 shows the minimum, average and maximum BSC for a breast as a function of h.
  • the subsystem inputs (8) consist of two images obtained from the ultrasound of the receiving transmitter (2) made before and after breastfeeding.
  • the present subsystem consists of two distinct phases.
  • the second phase is based on ⁇ Machine Learning 'techniques and computes the total volume from the data obtained from the preprocessed images / ultrasound.
  • the preprocessing phase of the images (10) aims to transform the input image into a vector, which captures the characteristics associated with the problem of estimating the volume of breastfeeding. This vector is invariant in relation to the rotation of the input images, changes in the angle of measurement (or taking of the ultrasound), common characteristics related to the production of milk that are independent of the person, etc.
  • said image preprocessing phase (10) two different techniques are used that can be used independently or in conjunction with the aim of obtaining the data vector described above.
  • the first technique uses the Discrete Cosine Transform (Discrete Cosinus Transform or DCT) and the second uses the Two-Dimensional Wavelet Transform (Discrete Wavelet Transform or DWT).
  • DCT Discrete Cosinus Transform
  • DWT Two-Dimensional Wavelet Transform
  • the DCT transform has been selected because it computes a representation of the image as a combination of horizontal and vertical frequencies. Therefore, with this representation, the details of the image that have certain frequency characteristics will appear along a circle depending on the angle that said characteristic has with respect to the horizontal and vertical axes. Therefore, the representation of the input image in the transformed DCT domain will be invariant with respect to the rotations that can be made on the original image. Moreover, just a part of the image in the transformed domain will be relevant, so that the region of interest of the image in the transformed domain may be selected in order to reduce redundancy while, at the same time, all the information contained in the image is preserved original.
  • the second approach to preprocessing (10) of the input image is based on the DWT.
  • the application of this preprocessing of the input images together with the DCT or independently since scattering properties ⁇ 'of the input ultrasound may be characterized by their spatiotemporal univocally properties is considered. Normally, these properties have been studied by the Discrete Two-Dimensional Fourier Transform (2D-DFT) or by the STFT (Short-Term Fourier Transform). It is a well-known result that both 2D-DFT and STFT cannot simultaneously represent the temporal and frequency properties at the same time, and with different degrees of resolution, of an input image.
  • the DWT presents the ideal framework for the problem of estimating the volume of breastfeeding since it provides a representation of the images in which the relevant information (presence of tubes, layers of tissue, etc.) of information related to the smooth change of the gray scales, artifacts due to the diffraction of the waves, the effect of scaling and rotation of the image because each measurement is performed under slightly different conditions, etc .
  • the DWT coefficients describe the correlation between the selected wavelet (in the preferred implementation of the present invention Haar and Daubechines wavelets have been selected) and the image at various scales / resolutions (ie the degree of similarity between the image and the wavelet for a time-discrete combination and position).
  • the calculated coefficients provide the amplitudes of the wavelet series over a set of scales and translations, which must be added in order to obtain the original image.
  • the DWT analysis can be understood as a search on the image of interest of characteristics that resemble the selected wavelet. This search is done on several scales and several wavelet sizes. For this reason, the Haar Wavelets have been selected to identify the lactophores channels while the Daubechines Wavelets are used both for the detection of lobules and for the elimination of artefacts derived from the absorption and scattering of the initial images.
  • the implementation of this analysis in the preprocessing (10) is carried out by means of a filter bank (figure 6). With this set of filters, the input image is divided into various spectral components ('sub-band coding') in order to obtain the high-pass, low-pass and pass-band components of the input signal. These components are obtained by applying filters h (high-pass) and g (step- low) .
  • filters h high-pass
  • g step- low
  • the RBF neural network is based on the computation of centroids
  • the criterion for evaluating the quality of the estimated function will be the Mean Square Error (MSE) applied to a control or validation sequence on a set of elements of the original Ultrasound Database.
  • MSE Mean Square Error
  • the performance of the RBF learning algorithms can be improved by various techniques. Such techniques include the combination of classifiers and / or training of the classifiers with non-uniform distributions on the data, the coding of the output of the classifiers or the exploitation of the properties of the Kernels due to the high dimensionality of the data. These methods for improving estimator performance are analyzed by the expectation of the input / output data and by the relationship between the dimensionality of the input and the number of samples.
  • the error on the measurement of the module (11) can be decomposed as the sum of the squares of the bias of the estimate and its variance.
  • the estimation error can be controlled by reducing both bias and variance. Therefore, if the set of neural networks is trained so that the output of each network is a random variable, the effect of computing the average of all outputs will result in a reduction of the variance and, therefore, of the error of estimation. This reduction will be a function of the number of Neural networks used in the system. In the preferred implementation of the system, depending on the degree of reliability required, between 1 and 100 neural networks can be used.
  • the neural networks of the present invention are organized in a committee-like structure so that, given a common input, each network uses a sampled version of the common input and calculates the output of each of them in parallel.
  • the effect of sampling the common input is that a different systematic error is introduced into each network. Therefore, when the output of a group of neural networks is added, since each network has a different bias, the estimation error decreases even more because the systematic error is now taken as a mean and the reduction of the variance described previously.
  • the system (8) can be combined with the system (9) (figure 8) where the input images are segmented by image processing techniques, which include opening, smoothing, and overlay ', calculation of the perimeter / segmentation and estimation of the area of the previous perimeter.
  • image processing techniques which include opening, smoothing, and overlay ', calculation of the perimeter / segmentation and estimation of the area of the previous perimeter.
  • the ratio of areas calculated by this subsystem applied to the fractal model (BSC) described above provides a second estimate of the completely incorrect volume with which it has been calculated with the module (11) figure 7 so, if the average between the two is taken estimates, the systematic error decreases.

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Abstract

L'invention concerne un système d'estimation de la volumétrie pour l'allaitement maternel, à savoir le volume de lait ingéré par un nourrisson pendant une tétée. Ledit système établit un modèle fractal de la poitrine humaine dans le but d'estimer le volume total de lait stocké dans celle-ci. Le système de la présente invention utilise un système d'émission et de réception d'ultrasons pour la capture d'une image avant la tétée et d'une autre après la tétée. Lesdites images sont prétraitées par une DCT et une DWT avant l'estimation. Le procédé pour estimer la volumétrie est fondé sur un comité de réseaux neuronaux destiné à réduire l'erreur entre la valeur réelle et l'estimation par réduction du biais (erreur systématique) et la variance du système d'estimation par établissement d'une moyenne des réponses du comité de réseaux neuronaux. L'invention concerne également un autre algorithme pour réduire ladite erreur systématique sur la base de techniques de segmentation d'images en combinaison avec ledit système fractal.
PCT/ES2007/000453 2007-07-20 2007-07-25 Système d'estimation de la volumétrie pour l'allaitement maternel Ceased WO2009013363A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
ESP200702029 2007-07-20
ES200702029A ES2311413B1 (es) 2007-07-20 2007-07-20 Sistema para la estimacion de la volumetria en la lactancia materna.

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WO2009013363A1 true WO2009013363A1 (fr) 2009-01-29

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8992445B2 (en) 2011-02-27 2015-03-31 Milkotech Systems Ltd Apparatus and method for real-time measurement of changes in volume of breast and other organs
KR20210145442A (ko) * 2020-05-25 2021-12-02 건국대학교 글로컬산학협력단 초음파를 이용한 모유량 측정시스템
CN114765945A (zh) * 2019-11-26 2022-07-19 皇家飞利浦有限公司 用于在母乳喂养或挤奶期间监测乳流量的监测系统和方法

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2398384A4 (fr) * 2009-02-17 2014-10-15 Innovia Medical Ltd Dispositif de mesure de consommation de lait en allaitement maternel

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5827191A (en) * 1995-09-28 1998-10-27 Rosenfeld; Haim Method and a device for monitoring milk volume during breast feeding
WO2006003655A1 (fr) * 2004-07-01 2006-01-12 Tulsa (N.Y.M.) Engineering Solutions Ltd Dispositif d'allaitement
WO2006054287A1 (fr) * 2004-11-18 2006-05-26 Mamsense Ltd. Appareil et procédé de mesure de débit de lait maternel

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5827191A (en) * 1995-09-28 1998-10-27 Rosenfeld; Haim Method and a device for monitoring milk volume during breast feeding
WO2006003655A1 (fr) * 2004-07-01 2006-01-12 Tulsa (N.Y.M.) Engineering Solutions Ltd Dispositif d'allaitement
WO2006054287A1 (fr) * 2004-11-18 2006-05-26 Mamsense Ltd. Appareil et procédé de mesure de débit de lait maternel

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
RAMSAY D.T. ET AL.: "Anatomy of the lactating human breast redefined with ultrasound imaging", JOURNAL OF ANATOMY, vol. 206, no. 6, June 2005 (2005-06-01), pages 525 - 534, XP055063447, DOI: doi:10.1111/j.1469-7580.2005.00417.x *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8992445B2 (en) 2011-02-27 2015-03-31 Milkotech Systems Ltd Apparatus and method for real-time measurement of changes in volume of breast and other organs
CN114765945A (zh) * 2019-11-26 2022-07-19 皇家飞利浦有限公司 用于在母乳喂养或挤奶期间监测乳流量的监测系统和方法
KR20210145442A (ko) * 2020-05-25 2021-12-02 건국대학교 글로컬산학협력단 초음파를 이용한 모유량 측정시스템
KR102375225B1 (ko) 2020-05-25 2022-03-16 건국대학교 글로컬산학협력단 초음파를 이용한 모유량 측정시스템

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ES2311413B1 (es) 2009-12-17

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