Claims
- 1. A method of characterizing the condition of a region of a tissue sample, the method comprising the steps of:
(a) determining at least one of:
(i) whether a region of a tissue sample lies outside a zone of interest; and (ii) whether optical data obtained from said region are affected by an obstruction; (b) processing a set of optical data obtained from said region to determine one or more tissue-class probabilities; and (c) characterizing a condition of said region based on results of said determining step and said processing step.
- 2. The method of claim 1, wherein said optical data are spectral data.
- 3. The method of claim 1, wherein said condition is selected from the group consisting of indeterminate, CIN 2/3, NED, and necrotic.
- 4. The method of claim 1, wherein tissue-class probability is a probability that said region comprises tissue of a predetermined type, wherein said type is selected from the group consisting of CIN 1, CIN 2, CIN 3, CIN 2/3, normal squamous, normal columnar, necrosis, NED, metaplasia, and cancer.
- 5. The method of claim 1, wherein said one or more tissue-class probabilities comprise a normal squamous probability, a normal columnar probability, a CIN 1 probability, a CIN 2/3 probability, and a metaplasia probability.
- 6. The method of claim 1, wherein said condition is indeterminate if said region is determined to lie outside said zone of interest.
- 7. The method of claim 1, wherein said condition is indeterminate if spectral data obtained from said region are determined to be affected by an obstruction.
- 8. The method of claim 1, wherein said processing step comprises weighting spectral data in a statistical classification technique.
- 9. The method of claim 1, wherein said one or more tissue-class probabilities are weighted according to a likelihood that a point within said region lies outside said zone of interest.
- 10. The method of claim 1, wherein said one or more tissue-class probabilities are weighted according to a likelihood that spectral data obtained from said region are affected by an obstruction.
- 11. The method of claim 1, wherein said determining step is based at least in part on image data obtained from said region.
- 12. The method of claim 11, wherein said image data comprise data of a type selected from the group consisting of RGB intensity, red intensity, green intensity, blue intensity, grayscale luminance, and measured radiant power.
- 13. The method of claim 1, wherein said determining step is based at least in part on spectral data obtained from said region.
- 14. The method of claim 1, wherein said determining step is based at least in part on image data and spectral data obtained from said region.
- 15. The method of claim 1, wherein said determining step comprises identifying from said tissue sample at least one member selected from the group consisting of a region of interest, a vaginal wall area, a smoke tube area, an os area, and a cervical edge area.
- 16. The method of claim 1, wherein said obstruction comprises at least one member selected from the group consisting of mucus, fluid, foam, a portion of a speculum, glare, shadow, and blood.
- 17. The method of claim 1, further comprising obtaining a first set of data and a second set of data from said region, and determining whether either of said first set and said second set is affected by an artifact.
- 18. The method of claim 17, wherein said second set is redundant with said first set.
- 19. The method of claim 17, wherein said first set comprises spectral data obtained from said region using light incident to said region at a first angle, and said second set comprises spectral data obtained from said region using light incident to said region at a second angle.
- 20. The method of claim 17, wherein said first set and said second set comprise reflectance data.
- 21. The method of claim 1, wherein said processing step comprises using spectral data to evaluate a necrosis metric, and wherein said characterizing step comprises characterizing the condition of said region as necrotic if said metric is satisfied.
- 22. The method of claim 1, wherein said processing step comprises using spectral data to evaluate an NED metric, and wherein said characterizing step comprises characterizing the condition of said region as NED if said metric is satisfied.
- 23. The method of claim 1, wherein said processing step comprises applying a statistical classification technique to determine tissue-class probability.
- 24. The method of claim 23, wherein said statistical classification technique comprises a principal component analysis method.
- 25. The method of claim 23, wherein said statistical classification technique comprises a feature coordinate extraction method.
- 26. The method of claim 1, wherein said processing step comprises applying a plurality of statistical classification techniques to determine tissue-class probability.
- 27. The method of claim 26, wherein said plurality of statistical classification techniques comprise principal component analysis methods.
- 28. The method of claim 26, wherein said plurality of statistical classification techniques comprise a principal component analysis method and a feature coordinate extraction method.
- 29. The method of claim 26, wherein said plurality of statistical classification techniques comprise a DAFE classification method and a DASCO classification method.
- 30. The method of claim 1, the method further comprising the steps of using an optical detection device to obtain spectral data from said region of said tissue sample, and compensating for a relative motion between said tissue sample and said optical detection device.
- 31. The method of claim 1, wherein said characterizing step comprises assigning a tissue-class probability to said region.
- 32. The method of claim 31, wherein said tissue-class probability is a CIN2/3 probability.
- 33. The method of claim 1, further comprising the step of:
(d) displaying tissue-class probabilities of a plurality of regions of said tissue sample.
- 34. The method of claim 33, wherein said tissue-class probabilities are CIN 2/3 probabilities.
- 35. The method of claim 33, wherein said displaying step comprises displaying said tissue-class probabilities overlaid onto a reference image comprising said plurality of regions.
- 36. The method of claim 33, wherein said displaying step is performed in real-time during a patient examination.
- 37. The method of claim 33, wherein said displaying step comprises distinguishing regions of said tissue sample with a high tissue-class probability from regions of said tissue sample with a low tissue-class probability.
- 38. The method of claim 37, wherein said tissue-class probability is a CIN 2/3 probability.
- 39. The method of claim 1, wherein said set of spectral data comprise data of a type selected from the group consisting of reflectance, fluorescence, Raman, and infrared data.
- 40. The method of claim 1, wherein said tissue sample comprises cervical tissue.
- 41. The method of claim 1, wherein said tissue sample comprises tissue of a type selected from the group consisting of colorectal tissue, gastroesophageal tissue, urinary bladder tissue, lung tissue, skin tissue, and epithelial tissue.
- 42. An apparatus for characterizing the condition of one or more regions of a tissue sample, the apparatus comprising:
(a) an optical detection device adapted to obtain spectral data from a plurality of regions of a tissue sample; (b) a memory that stores code defining a set of instructions; (c) a processor that executes said instructions thereby to:
identify spectral data obtained from substantially unobstructed members of said plurality of regions, wherein said members are within a zone of interest; determine tissue-class probabilities using said spectral data; and determine a condition of one or more of said plurality of regions using said tissue-class probabilities.
- 43. The method of claim 42, wherein said optical detection device is adapted to obtain spectral data and image data from said plurality of regions.
- 44. The method of claim 43, wherein said processor is adapted to identify said spectral data using image masking.
- 45. The method of claim 43, wherein said processor is adapted to identify said spectral data using image masking and spectral masking.
- 46. A method of determining the condition of one or more regions of a tissue sample, the method comprising the steps of:
(a) identifying spectral data obtained from substantially unobstructed regions of a tissue sample using image data from said regions, wherein said regions are within a zone of interest; (b) determining tissue-class probabilities corresponding to each of said substantially unobstructed regions using said spectral data; and (c) determining a condition of one or more of said regions using said tissue-class probabilities.
RELATED APPLICATIONS
[0001] This application is related to the following commonly-owned applications: Ser. No. ______ Attorney Docket No. MDS-035A, entitled, “Methods and Apparatus for Displaying Diagnostic Data”; Ser. No. ______ Attorney Docket No. MDS-035B, entitled, “Methods and Apparatus for Visually Enhancing Images”; Ser. No. ______ Attorney Docket No. MDS-035D, entitled, “Methods and Apparatus for Characterization of Tissue Samples”; Ser. No. ______ Attorney Docket No. MDS-035E, entitled, “Methods and Apparatus for Processing Image Data for Use in Tissue Characterization”; Ser. No. ______ Attorney Docket No. MDS-035F, entitled, “Methods and Apparatus for Processing Spectral Data for Use in Tissue Characterization”; Ser. No. ______ Attorney Docket No. MDS-035G, entitled, “Methods and Apparatus for Evaluating Image Focus”; and MDS-035H, entitled, “Methods and Apparatus for Calibrating Spectral Data,” all of which are filed on even date herewith.