EP1787156A1 - Diagnostic automatise de la malaria et d'autres infections - Google Patents

Diagnostic automatise de la malaria et d'autres infections

Info

Publication number
EP1787156A1
EP1787156A1 EP05762647A EP05762647A EP1787156A1 EP 1787156 A1 EP1787156 A1 EP 1787156A1 EP 05762647 A EP05762647 A EP 05762647A EP 05762647 A EP05762647 A EP 05762647A EP 1787156 A1 EP1787156 A1 EP 1787156A1
Authority
EP
European Patent Office
Prior art keywords
automated diagnosis
images
diagnosis
automated
image
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.)
Withdrawn
Application number
EP05762647A
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German (de)
English (en)
Inventor
Nicholas Etienne Ross
David Milton Rubin
Charles John Pritchard
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Individual
Original Assignee
Individual
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Filing date
Publication date
Application filed by Individual filed Critical Individual
Publication of EP1787156A1 publication Critical patent/EP1787156A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G02—OPTICS
    • G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00—Microscopes
    • G02B21/0004—Microscopes specially adapted for specific applications
    • G02B21/0008—Microscopes having a simple construction, e.g. portable microscopes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N15/1429—Signal processing
    • G01N15/1433—Signal processing using image recognition
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N15/1468—Optical investigation techniques, e.g. flow cytometry with spatial resolution of the texture or inner structure of the particle
    • G01N2015/1472—Optical investigation techniques, e.g. flow cytometry with spatial resolution of the texture or inner structure of the particle with colour
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N2015/1497—Particle shape
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
    • Y02A50/30—Against vector-borne diseases, e.g. mosquito-borne, fly-borne, tick-borne or waterborne diseases whose impact is exacerbated by climate change

Definitions

  • This invention lies in the field of diagnosis and identification of micro-organisms and other objects.
  • the invention has particular application in the field of identification of infections and of pathological conditions, including by the automated visual identification of micro-organisms.
  • the invention is applicable over a range of organisms.
  • Infectious diseases such as malaria, tuberculosis and sexually transmitted infections (STI's) are a major global health problem, causing millions of deaths annually.
  • the economic effect of diseases such as malaria is also significant.
  • the control of these diseases requires a rapid and accurate diagnosis that facilitates prompt treatment.
  • microscopy Despite advances in diagnostic techniques for malaria and many other diseases, light microscopy remains the most widely and commonly used method. This entails examining thick and thin blood smears for the presence of Plasmodia. It is the most efficient and reliable diagnostic technique, with very high sensitivies and specificities. Microscopy also makes it possible to differentiate between species, quantify parasitaemia and observe asexual stages of the parasites. Low material costs mean that the marginal costs of tests are very low. Microscopy is labour intensive and time consuming and the accuracy of the final diagnosis ultimately depends on the skill and experience of the technician and the time spent studying each slide. Compared with expert microscopy, standard laboratory microscopy has a sensitivity of approximately 90 %, a figure that drops dramatically in the field. Variable smear quality and slide degeneration with time are also problematic.
  • Antigen and immunoassay based technologies have also been developed to provide simple 'dip stick' diagnoses for diseases such as malaria and chlamydia. These technologies are extremely portable and require very low levels of training, but have high per test costs and other limitations, such as the inability of malaria testing kits to simultaneously diagnose all species or quantify parasitaemia. In addition, a different test is also required for every disease for which a diagnosis is attempted.
  • This invention describes a system to improve upon infectious disease diagnosis by microscopy by removing its most serious limitation, viz. reliance on diagnosis by a human operator. This is achieved by developing a digital image processing system to automate the examination of samples such as (but not limited to) blood, sputum or cervical smears. To be successful, the system must provide at least the same diagnostic information as would normally be provided by a human operator. For example, in the case of malaria, it should give a positive or negative diagnosis of malaria with similar sensitivity and specificity to normal microscopy, differentiate parasites by species and quantify the degree of parasitaemia.
  • Chlamydia Chlamydia, gonorrhea, syphilis or tuberculosis, or parasites such as malaria, bilharzias, leishmaniasis and African trypanosomiasis.
  • the term "germ” will be used herein to encompass any micro-organism or object that the invention can be applied to.
  • a process in accordance with this invention includes the steps of : - capturing images of a germ from suitably prepared sample slides using an automated microscope; and analyzing the images using specially designed algorithms on a computing platform, whereby images are: pre-processed using filters such as median and morphological filters; analysed for relevant information, such as using granulometry to determine object radii; segmented using various novel threshold selection methods; characterized using features generated from first and second order statistical properties of the objects; and classified using these features by means of a suitable classifier such as back propagation feed forward neural networks in a binary tree topology.
  • filters such as median and morphological filters
  • relevant information such as using granulometry to determine object radii
  • segmented using various novel threshold selection methods characterized using features generated from first and second order statistical properties of the objects
  • classified using these features by means of a suitable classifier such as back propagation feed forward neural networks in a binary tree topology.
  • the system of this invention enables the automatic identification and diagnosis of said diseases and the yielding of other diagnostic information, such as parasite load and species in the case of malaria.
  • the capturing of images preferably uses a novel, simple auto configuration of microscope.
  • the characterising step may also include novel features that quantify criteria examined by human technicians during manual analysis.
  • Apparatus in accordance with this invention incorporates an image acquisition platform that captures microscopic images from a sample slide in an automated, systematic manner and then processes these images on a computing platform using image classification algorithms. Rather than simply being a diagnostic aid to a skilled technician by semi- automating the process and identifying areas of interest, the system is able to replace the technician and completely perform the diagnosis.
  • the system also benefits from a novel, simplified configuration that allows for tailored optics, improved ease of use and a more compact physical unit. In addition to the new physical configurations, the system utilizes novel image classification algorithms.
  • the ability to integrate a communications system into the invention also makes available a large number of other possibilities such as health reports; remote maintenance and software upgrades; and the remote collection of image samples for scientific purposes. Among other things this will also assist health authorities with, for instance, disease incidence monitoring and optimal allocation of resources.
  • the communications module can utilize a range of current or future communications technologies, such as without limitation: GPRS, satellite communications, GSM, Ethernet or WiFi.
  • GPRS Global System for Mobile communications
  • satellite communications such as Global System for Mobile communications
  • GSM Global System for Mobile communications
  • Ethernet such as Ethernet
  • WiFi Wireless Fidelity
  • the input to the system is a suitably stained slide for the targeted disease.
  • the slides used are thin blood films, which are routinely stained using the quickdiff protocol, although alternative staining protocols such as the more conventional Giemsa stain are also possible.
  • a Gram stain or a commercially available immunoassay such as the MikroTrak Chlamydia trachomatis Direct Specimen Test (Syva Co.) could be used, and for tuberculosis a suitable stain could be a sputum sample stained using, for example, the Ziehl-Neelsen or Auramine stains.
  • the input to the system - a suitably stained slide for the target disease - is the same as a technician using a light microscope would use, it can be seamlessly integrated into any existing diagnostic process that is based on light microscopy. This allows a very simple operating process to be used, whereby a sample is prepared as normal for the diagnosis desired, the slide is then inserted into the device and a diagnostic algorithm selected. It is a significant change from current practice that this device performs the diagnosis rather than a technician using a microscope. This makes the invention very flexible (unlike light microscopy), a trained technician is not required) with a wide scope for use (suitable for any diagnosis where the target can be identified on a stained sample fixed to a slide).
  • a medical practitioner could review the diagnosis whether remotely (such as over the internet or other communications network) or at the place of use by means of a user interface, such as a PC-based graphical user interface (GUI).
  • GUI graphical user interface
  • the innovations disclosed enable vastly increased efficiency, greater robustness, ease of use and accuracy when compared to human diagnosis, and are more cost-effective.
  • the invention also drastically reduces the manpower requirements required for the diagnostic process, leading to savings and releasing capacity from existing diagnostic operations, as well as making it possible to have accurate, point-of-care diagnosis in many areas that previously have lacked the necessary manpower resources.
  • Figure 1 shows a possible configuration of a system embodying the elements of this invention.
  • Figure 2 is a flow chart showing the operation of the image acquisition hardware.
  • Figure 3 shows a comparison of the image classification algorithms for the general case, malaria and chlamydia.
  • Figure 4 shows a design concept that is a possible embodiment of the invention in a robust, portable unit.
  • Figure 5 shows the results from some of the steps of applying the malaria algorithm to a sample image.
  • FIGURE 1 Microscopic images from a suitably stained slide 7 for a target disease are automatically captured by the image acquisition hardware.
  • the hardware consists of a simplified microscope (a tube 5 and lenses 4 with suitable magnification) connected to an image capture device 6; and a stage 1 moveable in the XY-plane to move the microscope field around the slide, and moveable along the Z-axis to allow for autofocus, onto which the slide is loaded.
  • Control software controls the movement of the stage and the operation of the image capture device.
  • a light source 8 and filter 9 is also provided.
  • a possible configuration could be a charge-couple device (CCD) camera connected to a microscope with 1000X magnification, a halogen lamp light source and a stage moveable along the X-, Y- and Z-axes.
  • CCD charge-couple device
  • the operation of the image acquisition software is such that a series of images 11 is captured from the slide 7 and passed to the relevant diagnostic algorithm 12 until the algorithm indicates that a diagnosis has been made and no more information is required.
  • a positive diagnosis could be signaled once several plasmodia have been identified and their species determined, and the level of parasitaemia ascertained.
  • a negative diagnosis could be signaled when, for example, ten thousand fields have been examined without finding any parasites.
  • chlamydia the presence of as few as ten elementary bodies (EB's) could signal a positive sample, while again a negative sample would require that a large number of fields have been investigated without detecting pathogens.
  • the ability to scan and process images sequentially means that the number of fields examined can be increased to as many as is desired, limited only by the area of the sample on the slide. This allows the device to improve upon current diagnostic methods. For example, by scanning a large number of fields in malaria, the sensitivity of the overall diagnosis, which is a function of the sensitivity of the diagnostic algorithm and the number of fields examined, can be made high enough to detect infections even at very low parasite loads. In current manual practice the entire slide is almost never examined.
  • the speed of diagnosis is also increased, since the system is not constrained by factors such as operator fatigue. This also improves consistency of performance.
  • the images, once captured, are passed to an image classification algorithm shown in the figure that processes them sequentially.
  • the general structure of the image classification algorithm is in the form of a pattern recognition and classification system. This consists of a number of stages: image acquisition (implemented by the hardware platform); pre-processing 13 and image analysis 14; image segmentation 15; feature generation 16; and classification 17.
  • image acquisition is implemented by the hardware platform
  • pre-processing 13 and image analysis 14 image segmentation 15
  • feature generation 16 feature generation
  • classification 17 classification 17.
  • each algorithm is unique and specific to a target disease, but at the same time can all be used on the hardware platform since their input is simply a sequence of images of the sample slide.
  • the actual implementation of the image processing algorithms can differ for different diseases.
  • the algorithm identifies erythrocytes (red blood cells) and malaria parasites present in the images, and from this information makes a diagnosis as to whether or not malaria is present, and if present, determines the species of the malaria and calculates the level of parasitaemia.
  • the first stage 17 of the malaria algorithm involves the use of a median filter to remove noise from the image.
  • a morphological area closing filter is also used to generate an image with less detail that is used for certain operations such as erythrocyte segmentation.
  • the pre-processing would also involve using suitable median, morphological or other filter implementations to generate an image that can be used for segmentation.
  • image analysis is not necessarily required for all diseases (for example chlamydia), but is required for diseases such as malaria since the morphological methods used for image segmentation depend on the size and shape of the objects in the image.
  • the size and eccentricity of the erythrocytes is also an important feature for differentiating malaria parasite species.
  • Granulometry is used to generate a pattern spectrum that indicates the size distribution of circular objects present in the image, from which the mean erythrocyte radius can be determined.
  • the average eccentricity of erythrocytes is determined by the ratio of major to minor axes of cells that are adjudged to be free-standing by virtue of their area.
  • An upper limit on the area of an erythrocyte in order for it to be considered free- standing can be calculated from the pattern spectrum, which indicates the mean and standard deviation of the radii of erythrocytes present in an image.
  • the granulometry and eccentricity measures are carried out on binary masks.
  • a threshold that separates the two principal modes - background and foreground - that are present in the image is used to obtain the mask.
  • Figure 5a shows a sample image, with malaria parasites that are to be automatically identified circled.
  • the next stage of the algorithm is image segmentation.
  • image segmentation For chlamydia, for example, this might only involve a threshold selection method to generate a mask of EB's present, based on the staining of the EB's, and a morphological filteY to ' remove any objects of incorrect size.
  • this step is more complicated and requires that potential parasites and erythrocytes are identified and segmented from the image, so that the infected erythrocytes can be extracted.
  • Erythrocytes are identified using a threshold that separates the two principal modes - the background and the erythrocytes - that are present in the image histogram (Figure 5b). Morphological filters are then applied to remove small artifacts and smooth the objects present in the mask ( Figure 5c). Parasites are identified using another threshold technique (Figure 5d), derived from the histogram of the green colour component of the image.
  • the mask of possible parasites is sued as a marker (figure 5e) to eliminate all erythrocytes from the erythrocyte mask that are not suspected of infection (Figure 5c).
  • the infected objects often consist of clumps of overlapping erythrocytes, and it is necessary to separate them.
  • a morphological gradient of the greyscale, morphologically filtered green component of the image is constructed by finding the difference between the dilation and erosion of the image. An intersection of the morphological gradient image and the dilated infected cell cluster is used to generate a mask of infected erythrocytes (Figure 5f).
  • the next step of the algorithm is to generate features that can be used to correctly classify the objects segmented from the image.
  • Our novel approach is to generate first and second order features from characteristics such as texture and colour, and to also generate features that quantify details that a human technician would assess when analyzing a sample. These could include, for example, the relative size of infected cells, the shape and size of possible pathogens and even the spacing between objects that have been identified.
  • relevant features include, amongst others, the colour, shape, size and relative position of objects to each other.
  • features generated from the infected erythrocytes include but are not limited to first and second order statistical measures that provide quantitative measures of the colour and texture of the objects. Other features are generated that are similar to the specific indicators used by a technician to determine parasite species, such as the size of the parasite, the size of the infected erythrocyte and the position of the parasite in the infected erythrocyte. These features are largely generated from binary masks of the plasmodia and infected erythrocytes, and are novel compared to most other feature generation methods in that rather than making statistical measures of image properties, they quantify measures that are consciously assessed by a human technician when making a diagnosis.
  • the final step in the image classification is the classification of the objects based on the feature vectors.
  • a single neural network classifier would be a suitable solution.
  • the first neural network uses predominantly colour and texture information to separate out false positives (Figure 5h), and the second neural network classifies the plasmodia according to species.
  • a number of embodiments of the described invention can be realized. These include, but are not limited to, use as a diagnostic tool, where the device makes an independent diagnosis from the given information, and as a diagnostic aid, where the device makes an assessment that is reviewed by a medical practitioner.
  • Figure 4 One possible realization of the device, embodying the invention described, is shown in Figure 4. As shown in this realization, the invention can be integrated into a robust, portable design that allows it to be used in a wide variety of locations. Since the skill level to prepare slides is far lower than that required to analyse slides and make a diagnosis, the diagnostic process using light microscopy can be extended to many locations where it has previously been unfeasible.
  • the result from the diagnosis may be conveyed using a user interface.
  • a user interface For example, this could take the form of a graphical user interface (GUI) on a liquid crystal display (LCD).
  • GUI graphical user interface
  • LCD liquid crystal display
  • An embodiment of the device could also include a communications module, as indicated in Figure 1 , which allows the invention to form the basis for a wider health monitoring network, for example disease incidence management by health authorities.
  • the ability to store results and the development of novel user interfaces can extend an additional embodiment of the device to include patient management. Parasite load can be provided as further diagnostic information and species as for example in the case of malaria.

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  • Chemical & Material Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
  • General Physics & Mathematics (AREA)
  • Signal Processing (AREA)
  • Engineering & Computer Science (AREA)
  • Optics & Photonics (AREA)
  • Dispersion Chemistry (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

L'invention concerne un procédé et un appareil pour le diagnostic automatisé d'infections, à l'aide d'une plate-forme d'acquisition d'images qui capture des images microscopiques à partir d'une lame porte-échantillon de manière automatisée, systématique et qui traite ensuite lesdites images sur une plate-forme informatique à l'aide d'algorithmes de classification d'images.
EP05762647A 2004-06-11 2005-06-10 Diagnostic automatise de la malaria et d'autres infections Withdrawn EP1787156A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
ZA200404669 2004-06-11
PCT/ZA2005/000082 WO2005121863A1 (fr) 2004-06-11 2005-06-10 Diagnostic automatise de la malaria et d'autres infections

Publications (1)

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EP1787156A1 true EP1787156A1 (fr) 2007-05-23

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US8525091B2 (en) 2008-05-05 2013-09-03 California Institute Of Technology Wavefront imaging devices comprising a film with one or more structured two dimensional apertures and their applications in microscopy and photography
US8660312B2 (en) 2009-01-21 2014-02-25 California Institute Of Technology Quantitative differential interference contrast (DIC) devices for computed depth sectioning
US9743020B2 (en) 2010-03-23 2017-08-22 California Institute Of Technology Super resolution optofluidic microscopes for 2D and 3D imaging

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US20090225319A1 (en) * 2008-03-04 2009-09-10 California Institute Of Technology Methods of using optofluidic microscope devices
US8525091B2 (en) 2008-05-05 2013-09-03 California Institute Of Technology Wavefront imaging devices comprising a film with one or more structured two dimensional apertures and their applications in microscopy and photography
US8660312B2 (en) 2009-01-21 2014-02-25 California Institute Of Technology Quantitative differential interference contrast (DIC) devices for computed depth sectioning
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