Classifications

• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
  • B01L—CHEMICAL OR PHYSICAL LABORATORY APPARATUS FOR GENERAL USE
  • B01L3/00—Containers or dishes for laboratory use, e.g. laboratory glassware; Droppers
  • B01L3/50—Containers for the purpose of retaining a material to be analysed, e.g. test tubes
  • B01L3/502—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures
  • B01L3/5027—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures by integrated microfluidic structures, i.e. dimensions of channels and chambers are such that surface tension forces are important, e.g. lab-on-a-chip
  • B01L3/502715—Containers for the purpose of retaining a material to be analysed, e.g. test tubes with fluid transport, e.g. in multi-compartment structures by integrated microfluidic structures, i.e. dimensions of channels and chambers are such that surface tension forces are important, e.g. lab-on-a-chip characterised by interfacing components, e.g. fluidic, electrical, optical or mechanical interfaces
• • 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
  • 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
  • G01N21/21—Polarisation-affecting properties
  • G01N21/23—Bi-refringence
• • 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
  • G01N21/59—Transmissivity
  • G01N21/5907—Densitometers
• • 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
  • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
  • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
  • G01N21/64—Fluorescence; Phosphorescence
  • G01N21/645—Specially adapted constructive features of fluorimeters
  • G01N21/6456—Spatial resolved fluorescence measurements; Imaging
  • G01N21/6458—Fluorescence microscopy
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
  • B01L—CHEMICAL OR PHYSICAL LABORATORY APPARATUS FOR GENERAL USE
  • B01L2300/00—Additional constructional details
  • B01L2300/08—Geometry, shape and general structure
  • B01L2300/0809—Geometry, shape and general structure rectangular shaped
  • B01L2300/0816—Cards, e.g. flat sample carriers usually with flow in two horizontal directions
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
  • B01L—CHEMICAL OR PHYSICAL LABORATORY APPARATUS FOR GENERAL USE
  • B01L2300/00—Additional constructional details
  • B01L2300/08—Geometry, shape and general structure
  • B01L2300/0861—Configuration of multiple channels and/or chambers in a single devices
  • B01L2300/0864—Configuration of multiple channels and/or chambers in a single devices comprising only one inlet and multiple receiving wells, e.g. for separation, splitting
• • 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
  • G01N15/01—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells
  • G01N2015/016—White blood cells
• • 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
  • G01N2015/1006—Investigating individual particles for cytology
• • 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
  • G01N21/59—Transmissivity
  • G01N21/5907—Densitometers
  • G01N2021/5957—Densitometers using an image detector type detector, e.g. CCD
• • 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
  • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
  • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
  • G01N21/64—Fluorescence; Phosphorescence
  • G01N2021/6417—Spectrofluorimetric devices
  • G01N2021/6419—Excitation at two or more wavelengths
• • 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
  • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
  • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
  • G01N21/64—Fluorescence; Phosphorescence
  • G01N2021/6417—Spectrofluorimetric devices
  • G01N2021/6421—Measuring at two or more wavelengths
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
  • G06V2201/03—Recognition of patterns in medical or anatomical images
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
  • G06V2201/04—Recognition of patterns in DNA microarrays

Definitions

• the present invention relates to the field of medical devices. More particularly, the invention relates to an apparatus and method for automatically detecting and identifying pathogens, and particularly parasites, in bodily fluid or tissue samples.
• Parasites represent a group of extremely abundant human pathogens, which is estimated to infect around one third of the world population. These diseases are source of immense suffering and millions of deaths annually worldwide. In the US alone 11 million new cases of parasitic infections are diagnosed each year. The economic burden imposed by parasitic infections is immense and impossible to calculate.
• the "gold standard" for diagnosis of most types of parasites is manual identification under a microscope of stained smears of biological fluids. Most frequently, peripheral blood, is used, or in other instances, lymphatic fluid and cerebrospinal fluid (CSF). This method is laborious and requires highly trained personnel, and as a result it is typically low-throughput and expensive.
• the method of the invention can be utilized to detect the presence of parasites, or other microorganisms.
• the method of the invention includes steps of automated microscopy and machine-vision algorithms.
• the method of the invention can be utilized, for instance, to detect the presence of blood parasites, such as those associated with Malaria and Babesiosis.
• the invention provides an apparatus for automatic detection of pathogens within a sample, comprising:
• a cartridge support frame for receiving and supporting a cartridge having a sample within or thereupon;
• an optical imaging system having an optical path and comprising: at least one light source, at least one lens, and at least one digital camera; wherein the cartridge support frame is located within or may be moved into the optical path, such that images of the sample may be captured by the at least one digital camera;
• At least one processor interacting with and activating at least one of components a)-b); wherein the processor is adapted to perform image processing using classification algorithms on visual classification features to detect one or more suspected pathogens, when present, in the sample.
• the apparatus wherein the cartridge support frame is coupled to a moveable stage, movable in at least one dimension.
• optical imaging system further comprises a focus actuator for focusing upon an image.
• optical imaging system further comprises a plurality of objective lenses of varied magnification.
• optical imaging system further comprises: at least one optical filter or at least one dichroic beamsplitters/mirror, for exciting or detecting fluorescence.
• optical imaging system further comprises an actuator to switch the optical filters or the dichroic beamsplitter/mirror in and out of the optical path.
• the apparatus wherein the at least one processor performs at least one of the following:
• the apparatus wherein at least one of the at least one processor outputs a result that includes at least one of the following: the presence or absence of a pathogen; the species of pathogen; the number or concentration of pathogens detected; the life stage of the pathogen; a finding of anemia; a finding of an unusual white blood cell count; and information on the quality of the sample.
• the apparatus wherein the processor outputs one or more images of suspected pathogens.
• pathogen is a parasite
• the apparatus wherein the processor is present in an external computer wired to at least one of the following: to one or more internal processors, and to the digital camera.
• the apparatus wherein the processor is connected via a data network to at least one of the following: to one or more internal processors, and to the digital camera..
• the apparatus wherein one or more images captured by the apparatus are sent to a remote server for image processing at a remote location.
• the invention provides a cartridge for supporting a sample, wherein the cartridge comprises at least one microfluidic channel upon the cartridge.
• the cartridge wherein the cartridge has dimensions of 25mm x 75mm.
• the cartridge having a plurality of microfluidic channels, and the channels are connected to one another to facilitate simultaneous filling.
• the cartridge wherein the microfluidic channel has a channel height permitting only a single layer of cells to fill the channel height, thereby presenting a monolayer for imaging.
• the cartridge wherein the channel is manufactured with a hydrophilic material or is treated to promote capillary filling.
• a staining reagent present in a form selected from: a liquid, solid, a coating, and dried within the cartridge.
• the cartridge wherein the cartridge comprises a secondary application orifice allowing addition of an additional reagent to the sample after sample application is completed.
• the cartridge wherein the cartridge is sterile.
• the invention also provides a method for automatic detection of pathogens within a sample, comprising: performing image processing of at least one digital image captured using classification algorithms, the image processing including extracting visual classification features from the image.
• the method comprising the step of obtaining one or more digital images of a sample using an automated microscopy system comprising at least one light source, at least one lens, and at least one digital camera; the images are obtained prior to image processing.
• the sample is a slide selected from one or more of the following: a blood smear (thin or thick), a fecal smear, a lympathic smear, a cerebrospinal fluid smear, and a tissue biopsy.
• the method comprising the step of preparing a sample within a cartridge, performed prior to the imaging
• the cartridge comprises microfluidic channels for creating a monolayer of cells in at least one area, in order to facilitate imaging.
• the preparing the sample further comprises staining the sample with one or more stains.
• the method, wherein the one or more stains are selected to affect a change in at least one of the following: optical absorption, opaqueness, scattering, and color of structures within a sample.
• the one or more stains include acridine orange, and wherein a plurality of the one or more digital images are taken using configurations that allow discerning respective staining of DNA and RNA.
• the method, wherein the one or more stains include Giemsa, Romanowsky or related stains.
• the method wherein the sample is anticoagulated before or after application to the slide or cartridge.
• the method wherein the sample is anticoagulated by preloading one or more corresponding reagents onto the cartridge.
• the method, wherein the obtaining one or more digital images includes moving the slide or cartridge with respect to the automated microscopy system in order to image multiple locations within a sample.
• the method, wherein the obtaining one or more digital images includes automatically focusing the sample for the digital images.
• the focusing includes moving at least one of the following: the at least one lens, the slide or cartridge, and the at least one component of the at least one digital camera.
• the method wherein the obtaining one or more digital images employs at least one of the following: brightfield, darkfield, phase-contrast, any interference-contrast, and fluorescence microscopy and any combination thereof.
• the method wherein the obtaining one or more digital images employs a plurality of objective lenses of varied magnification.
• the method wherein the obtaining one or more digital images employs one or more optical filters or one or more dichroic beamsplitters/mirrors for exciting or detecting fluorescence.
• the method, wherein the obtaining one or more digital images includes obtaining a plurality of digital images for at least one sample location and employing a plurality of microscopy methods.
• the method wherein the plurality of microscopy methods comprise different fluorescence excitations and/or emissions.
• the method wherein the obtaining one or more digital images includes a processor that interacts with and activates mechanical and optical components by performing at least one of the following:
• the image processing outputs a result that includes at least one of the following: the presence or absence of a pathogen; the species of pathogen; the number or concentration of pathogens detected; the life stage of the pathogen; a finding of anemia; a finding of an unusual white blood cell count; and information on the quality of the sample.
• the method wherein at least one of the one or more digital images is captured at a first location and is processed using a processor located at a second location.
• classification features include one or more of the following: motion, size, shape, coloring, contrast, location in respect to additional biological structures, presence of internal structures, presence of extracellular structures, the aspect ratio, the optical density, florescence at predetermined wavelengths, optical birefringence, clustering behavior, and pattern matching.
• the image processing includes searching at least one of the one or more digital images for at least one patch which is likely to contain a target in the digital image and marking it as a candidate.
• the image processing further includes at least one of the following:
• the method wherein the searching for at least one patch which is likely to contain a target is performed using at least one of the following: pattern matching, model matching, motion detection, high florescence segmenting and clustering, and multi-frame tracking.
• the method wherein the image processing is performed using at least one of the following modules: a single frame classification cascade module; a multi- frame candidate construction module; a multi-frame candidate tracking module; a multi-frame candidate classification module; a sample classification module; a motion field construction module; an image verification module; a camera control model; and a masking module.
• the method wherein the image processing includes finding the motion vector of the background of the image of the sample, and where the motion vector is used to reconstruct the image in order to compensate for the background motion.
• the method wherein the image processing identifies at least one region within at least one of the digital images as a region not likely to contain a target.
• the invention provides computer readable storage medium that includes software capable of performing the image processing method of the invention
• the invention also provides computer readable storage medium that includes software capable of activating the apparatus of the invention.
• Fig. 1 is an external view of the apparatus of the invention.
• Fig. 2 is an isometric diagram of the central internal components of the apparatus.
• Fig. 3 illustrates a cartridge for analysis, resting within a cartridge support frame.
• Fig. 4 is an isometric view is shown, in which upper components have been removed for optimal viewing of the internal elements of the device.
• Fig. 5 is a side view of the right side of the apparatus, showing components such as the battery, base for mounting and adjusting angled mirror, support components and sliding connectors of moveable stage.
• Fig. 6 is a rear view of the apparatus, showing communication ports and electrical power inlet.
• Fig. 7 is an image captured showing Trypanosoma brucei parasites in a peripheral blood sample, for analysis using the invention.
• Fig. 8 is a florescent image of Trypanosoma brucei parasites. Automatic detection of the parasites was successful using the apparatus of the invention.
• Fig. 9 illustrates an enlarged view of a cartridge, according to one embodiment.
• the present invention discloses an automated apparatus for detection of parasitic and other pathogenic infection in a bodily fluid, human tissue or human waste product.
• images of known pathogens Prior to sample testing, images of known pathogens are saved in a database, and image processing software of the invention is activated on the images to extract visual characteristics which are typically associated with each known pathogen. Classification features are constructed manually, automatically extracted or refined from a database of known pathogens, or a combination thereof.
• the apparatus captures one or more digital images from a sample undergoing analysis.
• the apparatus then utilizes image analysis software to locate putative appearances of the pathogen in the image.
• the apparatus compares the characteristics of a suspected pathogen present in the image, to a succinct set of characteristics extracted from images of known pathogens.
• the characteristics termed "classification features" herein, may include, but are not limited to, typical motion of live parasites, their typical shape, size, their coloring, their contrast, and their location with respect to other elements of the biological sample (for example, if the pathogen is located within a mammalian cell). Additional classification features are enlarged upon hereinbelow.
• the analysis is rapid, and in certain instances may be performed in less than 1 second per image or less than 2 minutes per sample.
• Images taken may include still digital images, video images in digital format or simulated video images. One or more images may be utilized from each sample, as deemed necessary.
• the sensitivity of the present invention relates, in part, to the number of images captured of various areas within the sample. By preselecting this parameter, the user can set the sensitivity as needed during a given analysis. By choosing a sufficiently large number of imaged locations, therefore, the test described herein can exceed the sensitivity of the current gold standard.
• Another advantage of the invention over prior art techniques for detecting pathogens is the ability to identify the presence of several pathogens by performing a single run of the sample in the apparatus of the invention. Since the algorithm of the apparatus can contain classification features associated with several known pathogens, a single test of the sample can be sufficient to identify a wide range of pathogens.
• the apparatus of the invention thus simplifies and expedites the diagnostic procedure, by using a single test in the apparatus to identify a plurality of pathogens.
• the invention grants an advantage over prior art techniques for identification of parasites, as the invention is not dependent upon, for instance, an antibody binding to a specific epitope that may disappear from the surface of the parasite after mutation occurs.
• the invention maintains its efficacy, since parasite visual form tends to stay conserved despite rapid antigen mutation. Even if parasite visual form changes, the classification features may be updated to suitably detect the new form, and these classification features may be disseminated to all users of the invention.
• Sensitivities and specificities greater than 99% were achieved using the apparatus and software of the invention on several test cases, in which a known parasite sample was analyzed in order to test the accuracy of diagnosis. This accuracy is greater than the 97% specificity achieved using prior art ELISA methods to identify parasitic samples.
• the term "cartridge” refers to a support upon which a sample of human bodily material may be placed, after which the cartridge may be inserted into the apparatus of the invention, for analysis.
• the cartridge may resemble a traditional slide for a light-microscope in general appearance and size, typically 75X25mm, and is typically for a single use per sample.
• the cartridge may be a specialized sample support element, and may have dimensions of l"x3", or may resemble a multi-well plate.
• bodily material a material originating in the human or mammalian body, and from which a portion may be readily removed for analysis for the presence of pathogens or for visually apparent changes related to disease progression.
• Non-limiting examples include: blood, feces, saliva, plasma, serum, sweat, urine, milk, tears, pus, lymphatic fluid, cerebrospinal fluid, and mammalian tissues.
• pathogens refers to disease causing organisms, including parasites, bacteria, fungi and viruses.
• the apparatus of the invention can identify visual changes in bodily tissues and in fluids, which may occur as various diseases progress.
• classification features refers to visually apparent characteristics of a particular pathogen or of disease progression.
• the classification features may be used to identify a particular pathogen. Non-limiting examples include: typical motion of a pathogen (i.e. direction and velocity), size, typical shape, coloring, contrast, autofluorescence with or without staining, derived fluorescence, the aspect ratio, internal or external structures (organelles), etc. Additional classification features are described hereinbelow.
• field refers to a region of the sample supported by the cartridge that may be viewed by the microscope and camera.
• clip refers to a series of images captured in rapid succession by the camera.
• patch refers to a region within an image, e.g. a set of adjacent pixels, which is focused upon during processing.
• target refers to a real appearance of a pathogen in the image
• candidate refers to a patch which, during the algorithmic processing stages, is suspected to contain a pathogen
• classification algorithm refers to an algorithm that is composed of two phases.
• the first is the "pre-processing" training phase, during which numerous examples of the data of interest, containing both "positive” and “negative” examples are analyzed manually, automatically or in combination thereof, and a model for separating these examples is computed.
• a positive example is a patch depicting a pathogen
• a negative example is one that does not depict a pathogen.
• the actual classification takes place in the second phase.
• the algorithm Given a novel candidate, the algorithm uses the separation model computed in the previous phase and extracts classification features to determine whether the candidate is a target or not.
• the first "pre-processing" step typically occurs while the apparatus is customized and configured for particular parasites, and the resulting separation model is not usually modified by the clinical user, with the exception of potential software updates.
• Software updates can, for example, be used to improve classification results or to introduce new diagnostic capabilities.
• Fig. 1 illustrates an external view of an apparatus for detecting pathogens, according to an embodiment of the invention.
• the apparatus 100 is preferably covered by a rigid casing 400, e.g. plastic or metal, for protecting the inner components of the apparatus.
• Apparatus 100 includes a hinged cover 300 which may be opened to reveal a cartridge support frame (best shown in Fig. 3) for receiving a cartridge upon which a sample has been applied.
• the cartridge support frame for supporting a cartridge, is designed to protect the apparatus from direct contact with the tissue or sample undergoing analysis.
• Cartridge support frame is located such that after insertion of a cartridge containing a sample, cartridge is present within optical viewing path of microscope elements of the invention (described hereinbelow).
• Cartridge support frame rests on a moveable stage, and both of which can be automatically moved to capture images from different areas of the cartridge.
• Display screen 200 may display the analysis results.
• Display screen 200 may be a touch screen, which may be used to interact with the device.
• LCD touch screen is the "Thunderpack TAO-3530W” manufactured by TechnexionTM of Taiwan, including its interface board.
• touch screen may be replaced with a display screen and keys for interaction with device.
• Fig. 2 is an isometric diagram of the central internal components of the apparatus, according to an embodiment of the invention.
• Apparatus 100 includes touch screen 200, which is in wired communication with processor 210 and controller 220.
• Controller 220 may be a printed circuit board, designed to time and control the various operations of various other components.
• Light source 340 is part of an optical viewing path, which includes angled mirror 520, beam splitter 350 and digital camera 500. Additional components of the optical viewing path are described in relation to Fig. 3. Optical viewing path acts to reflect an image from a cartridge undergoing analysis, after cartridge has been placed in cartridge support frame. Image is reflected towards digital camera 500.
• Processor 210 is configured to receive images from the digital camera 500. Processor 210 then utilizes software of the invention to perform image analysis and compares a sample image to images stored in electronic memory, within a database of known images pertaining to known pathogens or known tissue views.
• processor and controller may be a single processing unit. Alternatively, any number of processors may be included in the invention, as deemed necessary.
• activation of the apparatus may be controlled by an external computer, such that the processor is located in the external computer.
• Lateral movement servo 330 and longitudinal movement servo are configured to move the cartridge support frame 310 including a cartridge, when present, in 4 horizontal directions, thus allowing scanning of the cartridge and capturing of several images from the entire area of a cartridge.
• a cartridge 380 is depicted, resting within a cartridge support frame 310. Hinged cover 300 (not shown) has been removed for ideal viewing. In this position, cartridge 380 is located within optical viewing path, as mirror 392 and LED circuit board 394 rest above cartridge 380 and are born by hinged cover 300 (not shown). Lens 396 is located beneath cartridge 380, thus focusing and continuing the optical viewing path. Referring to Fig. 9, an enlarged view of the cartridge is seen.
• Cartridge 380 receives and supports a sample for analysis, replacing a traditional slide. After placing a sample of human bodily material upon the cartridge, the cartridge may be inserted into the apparatus of the invention, for analysis.
• the cartridge may resemble a traditional slide for a light-microscope in general appearance and size, typically 75X25mm, and is typically for a single use per sample.
• branched tracks 360 extend from an arched application recess 370. Branched tracks 360 act as microfluidic channels, to ensure diffusion of the sample over the majority of the cartridge 380 area by capillary force and can create a single layer of cells that is highly suitable for microscopic imaging. This novel cartridge design obviates the need for trained personnel to prepare the sample. Microfluidic channels typically have a maximal depth of 1mm.
• the cartridge is typically disposable, however in certain embodiments it may be washed and reused.
• the cartridge may additionally be pre-coated with various coatings useful for sample-preparation, such as staining, coatings for maintaining sample freshness or viability, for processing or pre-processing of the sample, and for facilitating imaging.
• the cartridge may be packaged to maintain the sterility and/or the quality of preloaded stains or reagents to prevent degradation during storage.
• the cartridge may have a secondary orifice to allow addition of another reagent after sample application has been performed.
• a bodily sample is applied to the arched application recess 370 of a cartridge 380.
• the cartridge 380 is inserted into cartridge support frame 310, which is affixed to a moveable stage 320, and hinged cover 300 is closed.
• the controller 220 will activate light source 340, to emit a light beam to illuminate the cartridge.
• the light beam may be emitted from one light source or a number of different light sources each emitting light beams of different wave lengths, or a combination thereof.
• Light source is located internal to light source heat sink 610.
• a white light source 340 and a blue light source 620 are included in the apparatus, and are manufactured by Quadica Developments Inc. of Ontario, Canada:
• White light for transillumination is provided by a Luxeon® LXML-PWNl- 0120 Rebel Neutral White high power LED, producing 120 lumens of light at 350mA, and 220 lumens at 700mA.
• Blue light for detecting epifluorescence is provided by Luxeon® Blue Rebel LED, 23.5 Lumens at 350mA and 48 Lumens at 700mA pre-soldered to a MCPCB base.
• the MCPCB base is a mounted heat sink. Additional components (not shown) may be included in the apparatus that allow florescence microscopy, such as an excitation filter, a dichroic mirror or beamsplitter, and an emission filter.
• one or more of the following light sources may be used: a light emitting diode (LED), laser, halogen lamp, an arc lamp, white light, blue light, yellow light, green light, red light, ultraviolet light, and infrared light, to facilitate fluorescence and non-fluorescence imaging.
• LED light emitting diode
• laser halogen lamp
• an arc lamp white light, blue light, yellow light, green light, red light, ultraviolet light, and infrared light
• the controller 220 may select from among one or more emitting light sources, in order to change the wave length of the light beam according to the requirements and needs of a specific analysis.
• the controller 220 selects the exposure time, namely how long the light source will be on, how long the shutter time of the digital camera will be, the specifics of moving the moving stage (timing and direction of movement), focus and zooming in of digital camera, adjustment of the angle of angled mirror (for adjusting angle of beam, thus obtaining a depth- perspective of the sample).
• Controller 220 co-ordinates timing and directional movement of the cartridge support frame 310 and moveable stage 320 bearing the cartridge, with the timing of activation of light source and with image capture of the digital camera, to ensure proper sequence is maintained and to ensure images are initially captured from different areas of the cartridge. Subsequently, when images have been processed and certain areas of the sample have been tagged as requiring additional analysis, controller may move the cartridge support frame 310, may move the stage 320, or may instruct camera to zoom in on these areas, may replace or add optical filters, or may illuminate the area of interest with a different light source to gather additional information.
• Digital camera may be any electronic camera.
• digital camera was monochrome 5Mpixel 12bit, CMOS (complementary metal-oxide- silicon) camera. Model no. BTE-B050-U, manufactured by Mightex Systems of Toronto Canada and California USA.
• Camera includes a CCTV lens 510, or may have other type of lens.
• lens is a CCTV lens of 5 MP resolution, catalog number SV-5014H manufactured by NET of Finning, Germany.
• the resolution obtained was approximately 0.5 micron per pixel with a lOx objective. This resolution is sufficient to detect parasites such as T. brucei, which are typically 20 microns in length. In other embodiments, the resolution obtained was approximately 0.15 micron per pixel with a 40x objective, which is sufficient to detect parasites such as Plasmodium falciparum.
• objective lens 396 is located above angled mirror 520, and below cartridge 380. Angled mirror 520 reflects the illumination from the cartridge 380 in support frame 310 to the camera 500.
• the viewing path may include any of the following (not shown): a field, aperture and/or condenser diaphragm, one or more shutters, a condenser lens, a plurality of objective lenses of different magnifications, and a plurality of fluorescence filters of different optical passbands.
• the digital image is then transmitted to processor 210 which is designed to process and analyze the image.
• processor 210 which is designed to process and analyze the image.
• the image processing techniques used for analyzing the picture are described hereinbelow in the section titled "Image Processing Modules”.
• lateral movement servo 330 and longitudinal movement servo 730 are depicted. Controller (not shown) can instruct lateral movement servo 330 to move the moveable stage 320, in order to scan and capture images from different areas of the cartridge (when present in the apparatus).
• Controller (not shown) can instruct lateral movement servo 330 to move the moveable stage 320, in order to scan and capture images from different areas of the cartridge (when present in the apparatus).
• servo is "Dynamixel AX-12A Robot Actuator” manufactured by Trossen Robotics LLC of Illinois, USA.
• Servo includes a gear reducer, DC motor and servo regulating circuitry.
• the depicted cartridge support frame 310 is born by two horizontal rods 640a, 640b fixed to the upper surface of the moveable stage 320.
• Moveable stage 320 is also supported by two horizontal rods 690a, 690b which are perpendicular to the upper two rods, thus it is possible to shift the cartridge when present in its support frame 310, in all 4 cardinal directions, when lateral movement servo 330 and longitudinal movement servo 730 act to slide the movable stage 320 and/or cartridge support frame 310 on the rods.
• moveable stage may be moved using any of the following components: stepper motor, servo motor, lead screw, belt drive and worm drive.
• the controller 220 can instruct servos 330, 730, to move the cartridge in all the planar directions, either in a preset pattern or according to contemporary needs, whether for image capture of the whole cartridge or for picturing specific areas of interest on the cartridge.
• the images may be analyzed one by one or may be aggregated to be analyzed together. Images may be captured from each area one or more times, and may be sequential or non- sequential.
• an additional servo termed the "autofocus servo" 650 is a focus actuator that acts to focus objective lens.
• lithium ion battery 660 is depicted, and acts to provide power to the apparatus when the apparatus is used in a remote location.
• apparatus may be connected to the electricity power grid using power inlet 670 for electrical cord, best shown in Fig. 6.
• Display screen 200 is shown in side view.
• Strut 700 and stand 710 support horizontal rod 690b, which extends towards moveable stage 320.
• Sliding connectors 720a, 720b surround horizontal rod 690b, and are fixed to moveable stage 320, allowing stage 320 to slide upon rod 690b when longitudinal servo 730 exerts directional force upon stage 320.
• processor 210 stores the images obtained, in local memory, and image analysis is performed within the apparatus.
• the apparatus may send the images or portions thereof to be stored in an outer repository and/or analyzed on a remote computer.
• Ethernet port jack 740 is included at the rear of the apparatus, and provides the apparatus with the option to be wired into a local area network or any other communication network for sending images obtained to a remote computer, or for communicating any other information.
• remote computer may send and update images of known parasites for storing within the memory of the apparatus.
• USB port 750 additionally allows two way data transfer, such as of images captured.
• Power inlet 670 is provided to connect the apparatus to the electrical grid, when desired.
• Power switch 770 is used to switch the apparatus on/off.
• Cooling fan 780 cools interior electrical components of the device.
• the remote computer may be a central server which constantly receives images from apparatuses utilized at various locations, and server may be programmed to disseminate images of various new parasitical species to all users.
• the images are uploaded to the remote server, where image processing and analysis is performed, and the final decision and pathogen identification is returned to the apparatus for display on the display screen.
• the image processing and analysis software of the invention may be run using processing hardware that may be included in the device, or can be collocated on a standalone computer, or may be run on a remote computer in communication with the device, such as over the internet.
• the computer software makes use of machine vision algorithms that detects the presence or suspected presence of parasites and optionally other information about the parasites. Some embodiments of this software are described herein below.
• Image analysis can take place following, alongside and/or intermittently with image capture. AN ALTERNATIVE EMBODIMENT OF THE APPARATUS
• the apparatus comprises the following central components:
• Components manufactured by Olympus Corporation included: microscope BX43, manual florescence illuminator BX3-URA, Trinocular tube U-CTR30-2-2, Camera adapter with C-mount, 0.5x U-TV0.5xC-3-7, Quintuple revolving nosepiece U-5RE-2, Abbe condenser U-AC2, True color LED light source U- LHLEDC, Power Cord 1.8 M UYCP, FITC filter cube U- FBW, UPLFLN20X /0.5 Universal Plan Fluorite objective with 20x magnification, and UPLFLN40X/0.75 Universal Plan Fluorite objective with 40x magnification.
• Optiscan XYZ stage (Cat. No. ES103PS) comprising: two 10-position filter wheels (for 25 mm diameter filters), probe encoder for Z, travel XY stage, focus drive with adapter, joystick, RS232 and USB cables.
• Lumen 200 florescence illumination system (Cat. No. L200OL2)
• classification features which are associated with specific pathogens, in order to reach an algorithmic decision whether a pathogen is identified in the sample or not.
• Each pathogen is associated with specific visually identifiable classification features.
• These classification features can be collected when known samples are imaged using brightfield, darkfield, phase-contrast, any interference-contrast, or fluorescence microscopy. Samples can be treated to induce fluorescence, and samples can be viewed either with or without suitable staining.
• classification features include:
• Presence of intracellular structures associated with the pathogen e.g. nucleus, kinetoplast, granularity.
• Extraceullular structures associated with the known pathogen e.g. flagella, knobs.
• ⁇ Aspect ratio the ratio between the length/width of suspected structures.
• each pathogen is associated with specific florescence which can be viewed upon illumination and emission-filtering at predetermined wavelengths.
• Optical birefringence illumination in a specific wavelength results in detection of internal structures in certain parasites.
• the set of classification features is relatively small for each known pathogen, thereby their use for classification of a suspected pathogen is efficient and rapid.
• the method and apparatus may be used on biological samples from various tissue sources or their combinations
• sample materials can include but are not limited to blood (peripheral or otherwise), lymphatic fluid, cerebrospinal fluid (CSF), urine, fecal matter, saliva, and tissue biopsies (for example, muscle, liver, etc.)
• the biological sample is prepared for imaging using methods known in the art, such as thick or thin peripheral blood smears, or using a cartridge as presented herein.
• the sample may be stained with a sample-appropriate stain, before the sample is applied to the slide or cartridge, for example acridine orange may be added to peripheral blood samples.
• the sample may be stained after application to the slide or cartridge, for example by dipping a thin or thick smear preparation into a stain.
• a staining reagent may be present within the cartridge. Certain samples are best analyzed without undergoing staining.
• Images are obtained using one or more imaging modalities to illuminate the sample, including for instance, brightfield, darkfield, phase-contrast, any interference-contrast and fluorescence microscopies.
• One or more optical filter combinations may be included in the device, and used for example, in excitation and emission light paths.
• One or more light sources may be used.
• One or more magnification powers may be utilized, and images may be obtained at one or more locations within the sample. Images may be captured using one or more focus depths for each sample or for each imaging location.
• Fluorescence microscopy offers unique advantages in the context of the invention. Most notably, by employing a suitably chosen fluorescent staining or by imaging suitable autofluorescence channels, the resultant images can emphasize pathogen markers. For example, when blood is stained with acridine orange, fluorescence images reveal only white blood cells and parasites, due to their nucleic-acid content; red blood cells remain invisible. Such emphasis can greatly ease the computation burden on machine-vision algorithms. Furthermore, fluorescence and autofluoresence permit the identification of defined sample or cell components (such as DNA, RNA, or cell membranes). This significance can be used to inform machine -vision algorithms, thereby yielding substantially improved results.
• Microscopic imaging can take advantage of autofocus capabilities. These can be implemented, for example, by providing the objective (or potentially any other lens in the optical path) with an actuated stage, actuating the sample support stage in the optical direction or by providing focus control in the camera. Focus information for the control of such actuators can be computed based on captured image sharpness, which may optionally be automatically obtained for this purpose, or with a hardware-based autofocus system, such as one based on laser return (e.g. Prior Scientific LF210).
• the invention can take advantage of objectives with high depth of fields or with one of a number of computational and hardware techniques to extend depth of field that are known in the art ("Extended depth of field" methods). Different embodiments may take advantage of one or several stains or staining methods.
• Some stains include but are not limited to; acridine orange, Giemsa stain, Romanowsky stain, Leishman stain, H&E stain, Jenner stain, Wright stain, Field stain, silver stain, Papanicolaou stain, Sudan stain, Masson's trichrome, Gram stain, eosin, orange G, DAPI, Ethidium bromide, Hoechst, SYBR stains, and other nucleic acid stains.
• the components, partial compositions or the combinations of these stains are possible.
• Giemsa stain produces both a color that is visible in brightfield microscopy and a fluorescence signature that is visible in epifluorescence microscopy.
• eosin is typically used for its chromogenic effect, it also carries a distinct fluorescent signature, which can be advantageous.
• single or multiple stains can be imaged for fluorescence using one of more excitation wavelengths and imaged using multiple emission filters to yield multiparametric image data.
• samples stained using acridine orange can be illuminated using blue fluorescent excitation (e.g. wavelengths 460nm to 495nm) and imaged, either sequentially or simultaneously, using an emission filter or filter combination for yellow-green light (e.g. bandpass filter for 515nm to 535nm) and an emission filter or filter combination for red light (e.g. longpass filter for 600nm and up).
• an emission filter or filter combination for yellow-green light e.g. bandpass filter for 515nm to 535nm
• red light e.g. longpass filter for 600nm and up
• Such multiparameteric biologically- meaningful data can be used to identify various parasites, using the software algorithms of the invention.
• multiple fluorescence images are obtained in part by mechanically switching optical filters and/or dichroic beamsplitters/mirrors in and out of the optical path.
• the optical path is split at least once using dichroic or partial beamsplitters/mirrors and multiple fluorescence images (or fluorescence and non-fluorescence images) are obtained on multiple cameras.
• the one or more cameras are high- sensitivity CMOS cameras, such as those based on the Aptina/Micron MT9P031 sensor family.
• any of the cameras can be CCD, cooled CCD, intensified CCD, or electron-multiplied CCD.
• a cartridge or a traditional slide is used to support the sample for analysis within the apparatus of the invention.
• the cartridge is intended to simplify sample preparation, and is the presently preferred embodiment.
• the cartridge may be designed to present a single layer of cells in order to ease microscopic imaging.
• Use of a cartridge presents an improvement over prior art, since prior art sample preparation is known to require training, experience and time. For example, thick blood smears typically take over an hour to dry, whereas thin blood smear require significant operator skill in order to yield large useful area.
• the cartridge may be disposable or reusable. In particular embodiments the cartridge has dimensions similar to a typical microscope slide, 1" x 3" or 25mm x 75mm.
• the cartridge has one or more fluidic or microfluidic channels, which may optionally be connected to each other to facilitate simultaneous filling. These channels may be comprised of at least one section with a channel height that permits only a single layer of cells to fill it, hence presenting a monolayer for imaging. In the preferred embodiment, the channels may be designed for capillary filling, for example, by choice of materials, coatings or postprocessing, as is known in the art.
• the cartridge may be prefilled or pretreated with a staining reagent or reagents, which may be stored as a liquid, solid, a coating or dried within the cartridge. In particular embodiments, the cartridge is preloaded or pretreated with one or more anticoagulation reagents.
• stains and/or anticoagulation reagents are added to the sample before loading onto the cartridge.
• the cartridge is sterile or sterilized.
• the cartridge is packaged to maintain the sterility or the quality of preloaded stains or reagents to prevent degradation during storage.
• the cartridge permits washing, staining or otherwise treating the sample using reagents that are provided externally or that are preloaded.
• the sample may be prepared, loaded onto a cartridge or microscope slide, or stained using one or more automated instruments.
• Each such instrument may either be separate from or may be included in the apparatus of the invention.
• the apparatus may employ a Prior ScientificTM PL200 slide loader to automatically load microscope slides of cartridges with a microscope-slide form factor of l"x3".
• the slide loader may be modified to additionally dip the slide into appropriate staining and washing solutions.
• a sample of ⁇ is sufficient in volume for analysis.
• a finger prick can be utilized to obtain this minute quantity, with blood being collected into a capillary, thus obviating the need for trained blood technicians for taking a sample from a patient.
• the capillary can then be brought into contact with the cartridge to apply the blood sample to the cartridge.
• Suitable processors for implementation of the invention include, by way of example, both general and special purpose microprocessors.
• a processor will receive instructions and data from a read-only memory and/or a random access memory.
• the apparatus may include one or more mass storage devices for storing data files (such as images obtained or images of known pathogens).
• mass storage devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks.
• Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM disks.
• the cover 300 may be opened and the cartridge may be taken out and disposed of, after which, the apparatus 100 is ready for a new analysis.
• Sample removal can be automated, for example, using a Prior Scientific PL200 slide loader.
• a number of modules are described below for processing an image and finding pathogens.
• the apparatus described above may utilize one or more of the following image processing modules.
• the apparatus invokes the following modules in a preset sequence.
• the following description is a presently preferred order of the processing steps, although alternative embodiments may use a different order:
• Parasite Candidate Detection Module This module scans an entire image, which depicts a field within the sample, and searches for patches in the image within which it is likely that a pathogen of interest, referred to hereinafter as the "target", appears.
• One or more of the following methods may be used to detect these candidate patches: Pattern matching - if the general form of the target is well defined, a pattern describing this form is constructed in a pre-processing stage: numerous examples of its form are manually collected, and the general pattern is extracted. When processing an image, this pattern is then matched at every location, and those locations which exhibit high similarity to the pattern are taken as candidates.
• a human operator collects a number of images, containing a known target microorganism, and marks the target on these images. Then these marked images are fed to a processor adapted to extract the general pattern of the target, based on the numerous images of the target.
• This pattern matching module which is invoked by the apparatus, can use the resultant pattern of the target for finding other similar patterns in the image.
• the threshold of high similarity may be flexibly defined using trial and error, which may be steered by the application, or it may be defined rigidly as a pattern which has 90% similarity to the general pattern of the pathogen.
• Various image-processing filters e.g.
• wavelettes may be applied to enhance the features in the image which are relevant to the target's pattern and suppress those which are not.
• an edge-avoiding wavelette may be used to smooth out noise, while keeping the sharp features of the pathogen's boundary.
• Model matching if the general pattern of the target can be described by a simple model of a few image parameters (e.g. a blood cell can be described by a circle or an ellipse), patterns of this model are sought out in the image.
• Hough transform may be used to transform the image into the parameter space, and parameter sets which gain support from numerous pixels are taken as candidates.
• Motion detection - Using the Motion Field Detection Module (described below) the image can be segmented into pixels which are part of the ambient motion field, and pixels which move differently, i.e. move in other directions. The latter are clustered together spatially.
• the clustering algorithm takes into account the form and movement of the desired target. Clusters which confer to the characteristics of the target, such as size, are taken as candidates.
• High fluorescence The fluorescence image, which roughly overlays the intensity image, is segmented and clustered in a manner analogous to the motion detection above. Instead of looking for patches which move differently from the background, patches whose fluorescence is higher than the background, e.g. patches having high SNR, are sought.
• High fluorescence can refer to high fluorescence intensity values, or high sum fluorescence, e.g. as integrated over an area.
• Multi Frame - Using the Tracking Module (described below), multi- frame candidates (described below as well) are tracked to frames in which they are likely to appear. If the tracking is successful, the location to which the candidate was tracked is taken as a candidate in that frame.
• multi-frame candidates are tracked to temporally- adjacent frames, leading to new candidates. This is done iteratively to enhance the construction of multi-frame candidates.
• multi-frame candidates are found and processed as the images are being streamed into the processing device. They can be tracked only forward in time, and the process is performed only once.
• the second step of the sequence may be:
• Single Frame Classification Cascade Module may also be called “Multi Frame Classification Cascade Module”. This module receives candidates, from the first step, in a single frame, and computes the likelihood that they indeed contain an occurrence of the desired target.
• Classifying algorithms are machine-learning algorithms which may operate according to the following techniques used in sequence:
• classification features functions of the data to be classified - are determined. Therefore, these classification features must be relevant to the objects which are being classified. In our case these classification features may include the intensity and gradient of the candidate, the fluorescence of the candidate and/or motion of the candidate. Additional classification features are described hereinabove, in a separate section.
• the images are then processed to create rotationally-invariant images, to make the classifying features independent of the viewing angle.
• Magnification also affects the type of classification features used. In low magnification, the contour of the microorganism is an important hint, while in high magnification, finer details, such as pathogen-specific internal or external structures, are used.
• the classifying algorithm is "trained" to differentiate between sets of classification features which describe the target and sets which do not. This is done in a so-called supervised manner, where clips in which all occurrences of the desired target are manually tagged.
• the Parasite Candidate Detection Module is then used on these clips.
• the candidates the module outputs, along with their manual tags, are used as input to train a maching-learning algorithm, such as a Support Vector Machine (SVM).
• SVM Support Vector Machine
• An SVM is a known classifier, which uses tagged inputs as above to construct a separator in the features space between true and spurious candidates. Other machine-learning algorithms are known in the art.
• SVM Support Vector Machine
• the Single Frame Classification Cascade Module may employ a cascade of classifying algorithms. Candidates which are found to be highly likely to contain a target are passed to the next classifying algorithm. This cascade allows using more powerful (and computationally intensive) classifying algorithms along the cascade— the fewer candidates remaining, the more powerful the classifying algorithm can be, while maintaining reasonable running times. Importantly, the pre-processing training of a classifying algorithm is done on the output of the previous classifying algorithm up the cascade.
• the Single Frame Classification Cascade Module may also align them to canonical coordinates. This augments and complements the selection of rotationally-invariant features, allowing some flexibility in the latter. That is, classification features which are not invariant to rotation are still sometimes used, and the alignment phase rotates the patches to a canonical angle.
• classification features and/or the separator may be determined or modified manually or with non-machine-learning analysis algorithms.
• the human expert knowledge of a trained medical professional may be transcribed into classification features or separator algorithms that may be used independently or to supplement machine-learning based processing.
• the third step of the sequence may be:
• Multi-Frame Candidate Construction Module This module clusters together appearances of the same target in multiple frames.
• Single-frame candidates may be matched to other such candidates based on their location in the image. If the target is relatively static, then candidates are matched if they are roughly in the same location in the image (or, more precisely, in the same relative location within the motion field). More generally, if the target is moving, it is tracked using the Tracking Module (described below), and is matched to candidates appearing in roughly the same location to where it is tracked.
• the Tracking Module described below
• the Multi-Frame Candidate Construction Module constructs a graph with the single-frame candidates as its vertices, and matches defining edges. Connected components within this graph are checked for coherency, i.e. that the target appears "the same" in all occurrences. In an iterative implementation, temporal gaps within these components may be filled by extrapolating the locations of missing occurrences, and processing them with the Single-Frame Classification Cascade Module. In this case, the entire set of single-frame classifying algorithms may be used, rather than conditioning on pervious results, since only a small number of candidates are processed this way.
• the coherent, and possibly gap-filled, backbone of the connected components is the multi-frame candidate— a collection of all single-frame appearances of a specific target along with the results of its classification cascade.
• Multi-Frame Candidate Tracking Module partially-constructed multi- frame candidates may be tracked to frames temporally-adjacent to the ones in which their single-frame constituents were identified in the previous step. This can be done by various computer-vision algorithms, such as mean-shift or differential methods.
• this module facilitates initial candidate detection when other signals are weak. Candidates are created in location to which multi-frame candidates were tracked, as described in the Parasite Candidate Detection Module, above.
• tracking defines the matching of single-frame candidates, on which the construction of multi-frame candidates is based.
• the multi-frame construction can be seen as a form of agglomerated clustering: initially all single-frame candidates are distinct multi-frame candidates. They are then iteratively tracked, matched, and merged together into bigger clusters.
• Multi-Frame Candidate Classification Module Once the entire set of target occurrences is determined, it may be classified as a whole. Technically, this is similar to the single frame classification, but here features from multiple images are used, as well as the relations between them (e.g. the trajectory of the target).
• This module determines, for each multi-frame candidate, the likelihood that it's a true occurrence of the desired target.
• Non-pathogen Sample Content Classification Module This module may be used to identify sample elements that are not the pathogens themselves but are useful in determining pathogen presence. For example, red blood cells may be identified in malaria diagnosis in order to determine whether a suspected target is located within a red blood cell. Similarly, white blood cells may be identified in order to rule out their nuclei as suspects. This module may itself take advantage of the same algorithms and modules that are described for the identification and analysis of pathogen suspects.
• Sample Classification Module Based on the set of classifications of all multi-frame candidates, the sample is classified as either containing the defined microorganism or not containing it.
• Motion Field Construction Module The purpose of this module is to construct the image of the blood ambient background at each image-capture time point. When the blood is not moving, several frames are taken around the time point, and for each pixel taking the median value at its location. More generally, dense optic flow may be used to determine the motion vector of the background from frame to frame, by taking the mean motion value. The background image is constructed by taking the median value of pixels in the same location relative to the background motion.
• Image Verification Module The purpose of this module is to recognize and report poor samples, such as images taken from a blocked lens or damaged camera or images in which the sample does not appear at all.
• Camera/Microscopy Control Module The purpose of this module is to control image capturing.
• the location and magnification can be adjusted to clear ambiguities in the image.
• the camera can be adjusted to zoom in on a candidate, to visualize it in finer detail.
• the module may control exposure, illumination parameters and aperture parameters in order to obtain optimal images.
• Masking Module The purpose of this module is to identify regions in the image in which targets are not likely to occur. Such regions are ignored by the Parasite Candidate Detection Module.
• the invention is embodied in any suitable programming language or combination of programming languages, including Google Web Toolkit, JAVA, database managers and MySQL.
• Each software component can be implemented in a high-level procedural or object-oriented programming language, or in assembly or machine language if desired.
• the programming language may be a compiled or interpreted language.
• Images are entered into and saved in a database which may be any suitable database for storing data objects and metadata relating thereto. Any suitable database program may be used.
• the database is a relational database and a key/value database.
• database is a modified relational database.
• the search logic used for subsequent retrieval of experiments from the database is any suitable step, process, function or series of steps, processes and functions known in the art for searching a database.
• the software of the invention may include a graphical user interface (GUI).
• GUI graphical user interface
• the contents of the screens, the functionality of the system and the work process may be adjustable to a user's needs.
• the screen designs, terms and work process reflect the medical field and are user-friendly since they display and interact with the user in syntax familiar to medical technicians. Thus use of the system may appear intuitive.
• the final analysis result, which is displayed onscreen may include the presence or suspected presence of parasites, as well as parameters regarding parasites detected, such as: their type or species, parasite load or number, life stage or maturity and any other medically relevant information.
• the software may report medically pertinent information obtained from the biological sample yet unrelated to parasites, (such as detection of anemia, or detection of an unusual white blood-cell count). Information relevant to the quality of the test or sample may be displayed.
• the separator or separator may be configured such that the provided images are only highly enriched for potential pathogens, enabling the user to make a determination based on a condensed subset of information. In these latter cases, the algorithm can be tuned to provide very high sensitivity at the cost of lower specificity.
• Trypanosoma Cruzi is the parasite responsible for the potentially fatal Chaggas disease.
• One of its life cycle stages (Trypomastigotes) occurs in the blood, where it has a worm-like shape - an elongated body and a flagellum— which constantly twirls and spins in the blood. This motion is the cue for the detection algorithm.
• an image captured shows Trypanosoma brucei parasites (indicated by arrows), surrounded by red blood cells.
• Extract classification features for each database entry For example, compute a Census Transform of the entry: divide the result into equal- sized rectangular regions (e.g. 9 non-overlapping squares) and compute a histogram of the census transform in each region.
• the feature vector for the entry is a concatenation of these histograms.
• Plasmodium are parasites responsible for Malaria disease; Babesia are parasites responsible for Babesiosis disease. Both types of parasites infect red blood cells (RBCs) and in the initial stages of the infection form ring-like structures. Importantly, normal RBCs expel their organelles, and in particular their nucleus, before entering the blood stream. Hence the goal is to detect RBCs which contain a significant amount of DNA, indicating parasitic infection of Plasmodium or Babesia.
• RBCs red blood cells
• Plasmodium and Babesia species of interest include P. Falciparum, P. Vivax, P. Ovale, P. Malariae, B. Microti and B. Divergens.
• Detection Algorithm Initially, the blood is stained with fluorochromatic dyes such as Acridine Orange, Giemsa or Ethidium Bromide. Dyes which stain DNA and not RNA or which create a contrast between the two, are preferable.
• a thin blood smear is prepared upon a cartridge, images are captured, and the images are divided into fields, as explained in the first example. Each field is analyzed independently by recording two images from that field - a "bright field” (BF) image (visible light) and "florescence image” (FL) radiated with a wave length appropriate to cause the dye to fluoresce.
• BF dark field
• FL fluescence image
• BF-image For each BF-image, verify its validity using the Image Verification Module. If more than a specified number of images are poor (e.g. > 10%) report an error on this field.
• a Hough Transform of its vicinity indicates that it's contained inside a circle in the image whose size is appropriate for a RBC. This is, effectively, the use of the Model Matching method of the Parasite Candidate Detection Module.
• a contour detection algorithm of its vicinity indicates that it's within a convex shape in the image, whose size is appropriate for a RBC.
• the Pattern Matching method of the Parasite Candidate Detection Module processing the patch's vicinity locates a strong RBC pattern overlapping the patch.
• each candidate RBC is classified as to whether or not it's a RBC containing a parasite.
• Extract classification features for each database entry For example, compute the Census Transform of the entry; divide the result into equal-sized rectangular regions (e.g. 9 non-overlapping squares) and compute a histogram of the census transform in each region.
• the feature vector for the entry is a concatenation of these histograms.
• the apparatus and method of the invention answer a long-felt need for automatic identification of pathogens, and especially of parasites within a mammalian sample.
• the apparatus and method allow rapid identification of parasites that previously required use of expensive resources and trained personnel that are unavailable in many third world countries.
• the invention now allows blood donations and blood tests to be screened for parasites, such that a single run through the apparatus will identify a great many parasites, representing maximal efficiency.
• the apparatus and method overcome the difficulty of parasites constantly evolving, as an image of the new species may be easily uploaded into the database of known images and the characteristics of the new species may be analyzed to allow its identification in future.

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