# US 7,136,518 B2 · Methods and apparatus for displaying diagnostic data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 7,136,518 B2 - Document ID: US7136518B2 - Status: Granted - Kind: B2 - Application number: 10/418,902 - Filed: 2003-04-18 - Published: 2004-10-21 - Granted: 2006-11-14 - Earliest claimed: 2003-04-18 - Assignees: MediSpectra, Inc. - Inventors: Christopher E. Griffin; Chunsheng Jiang; Jean-Pierre Schott; Kevin T. Schomacker; Ross F. Flewelling; Charles C. Abele - Web page: https://patentgrep.com/patent/US7136518B2 ## CPC - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - A61B 5/0059: {using light, e.g. diagnosis by transillumination, diascopy, fluorescence (photoacoustic A61B5/0093; optical measurement of heart rate A61B5/02416; optical measurement of blood flow A61B5/0261; optical measurement of analytes A61B5/1455)} - G01N 21/274: {Calibration, base line adjustment, drift correction} - G01N 21/31: Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry {(G01N21/72 takes precedence)} - G06T 7/223: using block-matching - G06T 11/26: Drawing of charts or graphs - G06V 20/698: {Matching; Classification} - A61B 5/7257: {using Fourier transforms} - A61B 5/7267: {involving training the classification device} - G06T 2207/10016: Video; Image sequence - G06T 2207/10068: Endoscopic image - G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract The invention provides methods for displaying diagnostic results obtained from a tissue sample. In general, the invention assigns tissue-class probability values to discrete regions of a patient sample, and creates an overlay for displaying the results. The overlay facilitates display of the tissue class probabilities in a way that reflects the diagnostic relevance of the data. For example, methods of the invention comprise applying filtering and color-blending techniques in order to facilitate display of diagnostic results. ## Claims ### Claim 1 A method of displaying diagnostic data, the method comprising the steps of: (a) providing a reference image of a tissue sample, said tissue sample comprising epithelial cells; (b) providing a tissue-class probability corresponding to each member of a plurality of regions of said tissue sample, wherein said tissue-class probability is a probability that a region comprises tissue of a predetermined type, wherein said type is selected from the group consisting of CIN 1, CIN 2, CIN 3, GIN 2/3, metaplasia, NED, and cancer; (c) creating a visual indication of said tissue-class probabilities using color values that correspond to said tissue-class probabilities, wherein said color values vary over a range according to said tissue-class probabilities; and (d) displaying said reference image with said visual indication. ### Claim 2 (depends on 1) The method of claim 1, wherein said creating step comprises assigning grayscale values as a proxy for said tissue-class probabilities. ### Claim 3 (depends on 1) The method of claim 1, wherein said creating step comprises assigning RGB color values as a proxy for said tissue-class probabilities. ### Claim 4 (depends on 1) The method of claim 1, wherein said creating step comprises assigning grayscale luminance values to said tissue-class probabilities and converting said luminance values to RGB color values. ### Claim 5 (depends on 1) The method of claim 1, wherein said creating step comprises blending colors to provide diagnostically relevant information. ### Claim 6 (depends on 5) The method of claim 5, wherein at least one of said colors is yellow. ### Claim 7 (depends on 5) The method of claim 5, wherein at least one of said colors is blue. ### Claim 8 (depends on 5) The method of claim 5, wherein said colors comprise a continuum from yellow to blue. ### Claim 9 (depends on 8) The method of claim 8, wherein said continuum varies from an average tissue color to a first reference color. ### Claim 10 (depends on 1) The method of claim 1, wherein said creating step comprises assigning grayscale luminance values to said tissue-class probabilities, spatially filtering said grayscale luminance values, and converting said filtered grayscale luminance values to RGB color values. ### Claim 11 (depends on 1) The method of claim 1, wherein said creating step comprises spatially filtering values of said tissue-class probabilities, assigning grayscale luminance values to said filtered probability values, and converting said grayscale luminance values to RGB color values. ### Claim 12 (depends on 1) The method of claim 1, wherein said visual indication identifies at least one indeterminate region of said tissue sample. ### Claim 13 (depends on 12) The method of claim 12, wherein said visual indication identifies an indeterminate region without obscuring a corresponding portion of said reference image. ### Claim 14 (depends on 12) The method of claim 12, wherein said indeterminate region is identified using a crosshatch pattern or a trellis pattern. ### Claim 15 (depends on 1) The method of claim 1, wherein said visual indication identifies at least one necrotic region of said tissue sample. ### Claim 16 (depends on 15) The method of claim 15, wherein said visual indication identifies a necrotic region without obscuring a corresponding portion of said reference image. ### Claim 17 (depends on 15) The method of claim 15, wherein said necrotic region is identified using a crosshatch pattern or a trellis pattern. ### Claim 18 (depends on 1) The method of claim 1, wherein said displaying step is performed in real time during a patient examination. ### Claim 19 (depends on 1) The method of claim 1, wherein said displaying step is performed within about an hour of a patient examination. ### Claim 20 A method of displaying diagnostic data, the method comprising the steps of: (a) providing a reference image of a tissue sample, said tissue sample comprising epithelial cells; (b) providing a tissue-class probability corresponding to each member of a plurality of regions of said tissue sample, wherein said tissue-class probability is a probability that a region comprises tissue of a predetermined type, wherein said type is selected from the group consisting of CIN 1, CIN 2, CIN 3, CR4 2/3, metaplasia, NED, and cancer; (c) creating an overlay comprising colors as a proxy for said tissue-class probabilities, wherein said overlay represents a range of tissue-class probabilities as a spectral blend of colorsthat varies over a range; and (d) displaying said reference image with said overlay. ### Claim 21 (depends on 20) The method of claim 20, wherein said creating step comprises assigning grayscale luminance values to said tissue-class probabilities, spatially filtering said grayscale luminance values, and converting said filtered grayscale luminance values to RGB color values. ### Claim 22 A method of creating an overlay for displaying diagnostic data, the method comprising the steps of: (a) providing a tissue-class probability corresponding to each member of a plurality of regions of a tissue sample, said tissue sample comprising epithelial cells, wherein said tissue-class probability is a probability that a region comprises tissue of a predetermined type, and wherein said type is selected from the group consisting of CIN 1, CIN 2, CiN 3, CIN 2/3, metaplasia, NED, and cancer; and (b) creating an overlay comprising colors as a proxy for said tissue-class probabilities, wherein said overlay represents a range of tissue-class probabilities as a spectral blend of colors that varies over a range. ### Claim 23 An apparatus for displaying diagnostic data, the apparatus adapted to: (a) provide a tissue-class probability corresponding to each member of a plurality of regions of a tissue sample, said tissue sample comprising epithelial cells, wherein said tissue-class probability is a probability that a region comprises tissue of a predetermined type, and wherein said type is selected from the group consisting of C114 1, GiN 2, CiN 3, GIN 2/3, metaplasia, NED, and cancer; and (b) create an overlay comprising colors as a proxy for said tissue-class probabilities, wherein said overlay represents a range of tissue-class probabilities as a spectral blend of colorsthat varies over a range. ### Claim 24 (depends on 23) The apparatus of claim 23, further adapted to display a reference image of said tissue sample with said overlay.