WO2000062241A1 - Procede et appareil permettant de determiner le type de preparation d'un echantillon en vue d'un examen microscopique - Google Patents

Procede et appareil permettant de determiner le type de preparation d'un echantillon en vue d'un examen microscopique Download PDF

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Publication number
WO2000062241A1
WO2000062241A1 PCT/US2000/009987 US0009987W WO0062241A1 WO 2000062241 A1 WO2000062241 A1 WO 2000062241A1 US 0009987 W US0009987 W US 0009987W WO 0062241 A1 WO0062241 A1 WO 0062241A1
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WO
WIPO (PCT)
Prior art keywords
slide
score
array
preparation type
biological specimen
Prior art date
Application number
PCT/US2000/009987
Other languages
English (en)
Inventor
James J. Boisseranc
Andrew D. Silber
Michael A. Levine
Mark Shuxing Sun
Richard K. Johnson
Original Assignee
Tripath, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tripath, Inc. filed Critical Tripath, Inc.
Priority to AU42402/00A priority Critical patent/AU4240200A/en
Publication of WO2000062241A1 publication Critical patent/WO2000062241A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts

Definitions

  • the invention for the first time recognizes that the impact of slide preparation type on the biological specimen indicates that manual and automated procedures must be tailored to slide preparation type to accomplish successful diagnosis. Furthermore, intermingling slides of different slide preparation types should be avoided or handled. Additionally, slide preparation type should be checked prior to processing to assure accurate slide-processing results.
  • the slides often contain silk screen markings on the surface .
  • the marks must be differentiated from the cellular material in the specimen.
  • the liquid preparation is sometimes annular or contains irregular edges .
  • the conventional preparations may contain circular swirls near the center of the slide making it difficult to distinguish from liquid preparations .
  • the slides may contain bubbles in the adhesive under the coverslip, which partially obscure portions of the specimen.
  • Figure 7 shows the method of the invention to determine the presence of fiducial marks on a slide.
  • Figure 8 shows the method of the invention to determine the presence of dust on a slide.
  • Figure 10 shows an alternate method of the invention to automatically determine slide preparation type using a slide preparation type feature classifier.
  • the slide preparation type determination and slide preparation type based classification apparatus of the invention comprises an imaging system 502, a motion control system 504, an image processing system 536, a central processor 540, ' and a workstation 542.
  • the imaging system 502 is comprised of an illuminator 508, imaging optics 510, a CCD camera 512, an illumination sensor 514 and an image capture and focus system 516.
  • the camera may be a high-resolution camera as is well known in the art .
  • the image capture and focus system 516 provides video timing data to the CCD cameras 512, the CCD cameras 512 provide images comprising scan lines to the image capture and focus system 516.
  • FIG. 2 shows the method of the invention to determine the slide preparation type from a scan of a biological specimen slide, such as a Pap smear slide.
  • the slide is scanned at low power magnification to generate a digital representation 15 of the slide 12.
  • the digital representation 15 may be a field of view representation using an orthogonal grid where each field of view has an associated field of view score.
  • a geometric analysis of the slide generates geometric features 22 from the digital representation 15.
  • Zones with specimen material will generally have higher SIL scores than empty space, dust and fiducial marks.
  • SIL score could be replaced with other FOV scores that indicate the presence of specimen material, such as cell count or cell group scores .
  • the SIL scores are stored in a two-dimensional array, called the score array 86, dimensioned such that each of the 2Ox zones is represented by an element in the score array 86.
  • the indexes of the elements of the score array correspond with a 2Ox zone on the slide. For example, shown on Figure HA is a 20x zone 215 with an index of (0,21) with a SIL score of 1.
  • step 354 the mean 42 of the pixel values in the image 350 is calculated.
  • step 356 the process flows to calculate the standard deviation 44 of the pixel values in the image 350.
  • step 358 the skewness 46 of the pixel values in the image 350 is calculated.
  • step 360 the kurtosis 48 of the pixel values in the image 350 is calculated.
  • These statistics are a reflection of the texture of the image 350. For example, the more texture a FOV has, the higher the standard deviation of the pixel values in a FOV will be. A blank field will have a texture score similar to a field so covered in cells that nearly all light is blocked from transmission. A FOV with many well-separated cells will have a high texture measurement.
  • the texture features are further processed to determine the slide preparation type.
  • the determination of a texture measure may also start with some smoothing of the image to remove low frequency power.
  • the step of convolving an image with a kernel may be used to smooth an image .
  • kernels that may be used include a 3x3, 7x7 and 15x15 kernel. Each kernel comprises an array of ones . The larger the kernel, the more the image is smoothed.
  • the image 350 is convolved with a 3x3 array of ones to produced a 3x3 convolved image 33.
  • the 3x3 convolved image 33 is subtracted from the original image obtained in step 372 to create a 3x3 convolved subtracted image 34.
  • the result is to create an image with the high frequency content intact.
  • further statistics may be calculated from the smoothed images .
  • the 3x3 convolved subtracted image 34 is processed with the statistical analysis step 40B which has been described with reference to Figure 5 to generate additional texture features. Additional smoothing operations may also be performed.
  • the image is convolved with the 7x7 array of ones to generate a 7x7 convolved image 37.
  • step 392 the image is convolved with a 15x15 array of ones to provide a 15x15 convolved image 35.
  • the 15x15 convolved image 35 is subtracted from the original image 350, in step 393, to generate a 15x15 convolved subtracted image 38.
  • Statistical analysis 40D described with reference to Figure 5, is performed on the 15x15 convolved subtracted image 38 to generate additional texture features 28.
  • the central processor 540 implements the processes described in Figures 4 and 5 in software .
  • a rich FOV is an FOV with many cells.
  • Two methods of determining slide preparation type from slide features, such as texture features, are described in detail with reference to Figures 9 and 10.
  • Figure 6 shows the method of the invention to create a mask array 94 from a score array 86.
  • step 118 setting all array elements to zero creates a blank binary mask array 124.
  • This mask array is a binary homologous image of the score array.
  • the dimension of the array is the same as the score array 86 from the 4X processing where there is a one-to-one correspondence between the elements of the binary mask array 124 and the score array 86.
  • step 120 shows the method of the invention to determine the location of fiducial markings on a slide, step 120 in Figure 6.
  • step 120 the following procedure is used to determine whether or not a value of one is placed in the binary mask in the corresponding position for array elements in the score array 86.
  • step 152 an element of the score array 86 is checked for a score of one or two. Those skilled in the art will recognize that other scores indicating a fiducial may be used. If the element has a score of one or two, a 3X5 neighborhood scan centered on the element is implemented in step 154. One by one each neighbor of the element in the score array 86 is checked. If, in step 158, a neighbor has a value equal to the value of the center element, then the process increments a counter in step 160.
  • Figure 12A shows the THINPREP map or the field of view scores for x, and y.
  • Figure 12A shows a THINPREP pattern 260 with the field of view scores in substantially the same array configuration as Figure HA However, there is a circular area in the center, which is the THINPREP preparation and the fiducial marks 262 and 264 with field of view scores of 1 or 2.
  • Figure 13D shows the resulting exclusive OR of the original map with the mask showing that the only material left for the analysis is the preparation itself.
  • This single image or these multiple images may be logically divided or combined into any number of subimages and analyzed to provide a set of scores . Multiple images may be combined to create a composite image or combined images that are subsequently analyzed to provide a set of scores.
  • the invention is equally applicable to other types of imaging equipment and to any method that can obtain an equivalent set of scores that may be further processed. Therefore, the field of view score is a term not limited to a single microscope object field but could correspond to any portion of a microscope object field or any composite or combination of a number of microscope object fields.
  • the No Review population cannot exceed the classification rate. If the application of thresholds results in a higher percentage of No Review slides than the classification rate, the Review population is supplemented with the highest scoring slides, Eval score, from the No Review population until the No Review population is less than or equal to the classification rate. The other parameters will be discussed in order to show how determining slide preparation type will effect slide processing.
  • endocervical cells have detected.
  • the endocervical threshold and adjunctive threshold vary based on the slide preparation type.
  • Table G shows examples of thresholds from one embodiment of the invention. This table demonstrates the differences in threshold by preparation type.
  • All actions and indications are based on thresholds specific to the specimen preparation type such as the action to be taken: Review, QC Review or No Further Review, the Squamous adequacy, the Endocervical adequacy, and the Inflammation and Obscuration adequacy.
  • the process review report shows slides that have a processing problem. The report contains slides from only one specimen preparation type where the specimen preparation type appears in the report header. A digest of each report is given below.

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Processing (AREA)

Abstract

L'invention concerne un système permettant de déterminer un type de préparation (20) d'un échantillon biologique sur lame et un système permettant d'effectuer un classement et une analyse automatiques du type de préparation sur lame. Un système d'analyse automatique d'échantillon biologique sert à obtenir une image d'un échantillon biologique sur lame (12). L'image est traitée pour calculer un nombre de résultats d'analyse (86) à partir de portions de l'image de l'échantillon biologique sur lame qui permet d'en déduire les caractéristiques géométriques. Des motifs (106A, 106B, 106C), basés sur les types de préparation d'un échantillon biologique sur lame, sont comparés avec la carte. Grâce au rapport entre le nombre d'éléments présentant une forme d'échantillon connue et le nombre total d'éléments de carte, on obtient une valeur chiffrée. Ce système permet aussi de confirmer une détermination manuelle du type de préparation (20) et avertit des éventuelles erreurs en cas d'incohérences.
PCT/US2000/009987 1999-04-14 2000-04-14 Procede et appareil permettant de determiner le type de preparation d'un echantillon en vue d'un examen microscopique WO2000062241A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU42402/00A AU4240200A (en) 1999-04-14 2000-04-14 Method and apparatus for determining microscope specimen preparation type

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US29151999A 1999-04-14 1999-04-14
US09/291,519 1999-04-14

Publications (1)

Publication Number Publication Date
WO2000062241A1 true WO2000062241A1 (fr) 2000-10-19

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AU (1) AU4240200A (fr)
WO (1) WO2000062241A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2402470A (en) * 2003-04-30 2004-12-08 Image Metrics Plc Method and apparatus for classifying images
CN104751431A (zh) * 2013-12-31 2015-07-01 西门子医疗保健诊断公司 一种基于图像处理的方法和装置
WO2020081504A1 (fr) 2018-10-15 2020-04-23 Upmc Systèmes et procédés d'interprétation de spécimen

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5627908A (en) * 1994-09-20 1997-05-06 Neopath, Inc. Method for cytological system dynamic normalization
US5647025A (en) * 1994-09-20 1997-07-08 Neopath, Inc. Automatic focusing of biomedical specimens apparatus

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5627908A (en) * 1994-09-20 1997-05-06 Neopath, Inc. Method for cytological system dynamic normalization
US5647025A (en) * 1994-09-20 1997-07-08 Neopath, Inc. Automatic focusing of biomedical specimens apparatus

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2402470A (en) * 2003-04-30 2004-12-08 Image Metrics Plc Method and apparatus for classifying images
GB2402470B (en) * 2003-04-30 2005-11-30 Image Metrics Plc A method of and apparatus for classifying images
CN104751431A (zh) * 2013-12-31 2015-07-01 西门子医疗保健诊断公司 一种基于图像处理的方法和装置
WO2015102945A1 (fr) * 2013-12-31 2015-07-09 Siemens Healthcare Diagnostics Inc. Procédé et appareil faisant appel à un traitement d'image
WO2020081504A1 (fr) 2018-10-15 2020-04-23 Upmc Systèmes et procédés d'interprétation de spécimen
EP3867807A4 (fr) * 2018-10-15 2022-08-03 Upmc Systèmes et procédés d'interprétation de spécimen

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Publication number Publication date
AU4240200A (en) 2000-11-14

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