Method and system for automated digital image analysis of prostrate neoplasms using morphologic patterns

• US 20070019854A1
• Filed: 05/10/2006
• Published: 01/25/2007
• Est. Priority Date: 05/10/2005
• Status: Abandoned Application

• Current Assignee: Ventana Medical Systems Incorporated (Roche Holding AG)
• Original Assignee: BioImagene, Inc. (Roche Holding AG)
• Inventors: Jadhav, Prithviraj, Joshi, Aparna, Sawaimoon, Satyakam, Naik, Gauri, Rao, C., Gholap, Abhijeet, Siddheshwar, Chivate
• Application Number:
  US11/431,786
• Publication Number:
  US 20070019854A1
• Time in Patent Office: Days
• US Class Current: 382/133
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

A method and system method and system automated digital image analysis of prostrate neoplasms using morphologic patterns. The method and system provide automated screening of prostate needle biopsy specimens in a digital image and automated diagnosis of prostatectomy specimens.

Claims

1. A method for automated digital image analysis of prostrate neoplasms using morphologic patterns, comprising:

extracting a plurality of features from a digital image of a prostrate tissue sample to which a chemical compound has been applied;

automatically removing selected ones of the plurality of extracted features from further consideration; and

automatically classifying remaining features in the plurality of extracted features using a medical classification scheme to determine a medical classification for the prostrate tissue sample.

2. The method of claim 1 further comprising a computer readable medium have stored therein instructions for causing one or more processors to execute the steps of the method.

3. The method of claim 1 wherein the chemical compounds includes Haematoxylin and Eosin (H/E) stain.

4. The method of claim 1 wherein the plurality of extracted features include size, shape, arrangement, destruction, stroma area, cytoplasm area or Lymphocytes presence.

5. The method of claim 1 wherein the step of automatically removing selected ones of the plurality of features from further consideration includes removing selected ones of the plurality features of an intermediate nature and non-malignant features.

6. The method of claim 5 wherein the step of automatically removing selected ones of the plurality of features from further consideration includes removing areas of cyotoplasm and stroma from the prostrate tissue sample.

7. The method of claim 1 wherein the medical classification scheme includes a Gleason'"'"'s grade and score.

8. The method of claim 1 wherein the medical classification scheme includes a medical classification for a human prostrate cancer.

9. The method of claim 1 wherein the medical conclusion is benign, borderline, or malignant for the prostrate tissue sample.

10. The method of claim 1 wherein the step of extracting a plurality of features from a digital image includes adjusting a contrast of the digital image or removing a mask or artifact from the digital image.

11. The method of claim 1 wherein the extracting a plurality of features includes segmenting lumen pixels by computing a gray scale histogram;

computing a mean and standard and deviation of the gray scale histogram; and

segmenting lumen pixels with an intensity greater than a first constant minus the standard deviation.

12. The method of claim 1 wherein the step of automatically removing selected ones of the plurality of extracted features from further consideration includes segmenting cell pixels by converting a Red-Green-Blue (RGB) model of the digital image into a Hue, Saturation, Intensity (HIS) model;

segmenting blue pixels with a blue pixel value less than a red pixel value and a green pixel value less than a first constant and an intensity less than a second constant;

computing a mean and standard deviation of any segmented pixels; and

re-segmenting blue pixels with a hue greater than a third constant and a blue pixel value less than the mean minus the standard deviation.

13. The method of claim 1 wherein the step of automatically removing selected ones of the plurality of extracted features from further consideration includes segmenting cytoplasm pixels by removing high intensity pixels;

and removing cell pixels and lumen pixels.

14. The method of claim 1 wherein prostrate tissue sample is a needle biopsy tissue sample.

15. The method of claim 1 wherein the step of extracting a plurality of features from a digital image includes extracting a number of glands, an average lumen area, a standard deviation of the lumen area, a standard deviation of the gland size, a distance between glands, a stromal area between glands and a shape of the glands including circularity and elongation.

16. A method for automated digital image analysis of prostrate neoplasms using morphologic patterns, comprising:

creating a neural network for automated analysis of prostrate neoplams;

training the neural network using back propagation training; and

recognizing prostrate neoplasms using back propagation recognition.

17. The method of claim 16 further comprising a computer readable medium have stored therein instructions for causing one or more processors to execute the steps of the method.

18. The method of claim 16 wherein the step of training the neural network using back propagation includes training the neural network with data including gland size variation, gland shapes variation, gland arrangement factors, gland destruction factors, Stroma percentage and Lymphocytes percentage.

19. The method of claim 16 wherein the recognizing prostrate neoplasms includes a Gleason grade from one to nine for a selected prostrate neoplasm.

20. An automated digital image analysis system for prostrate neoplasms, comprising in combination:

means for extracting a plurality of features from a digital image of a prostrate tissue sample to which a chemical compound has been applied;

means for automatically removing selected ones of the plurality of extracted features from further consideration; and

means for automatically classifying remaining features in the plurality of extracted features using a medical classification scheme to determine a medical classification for the prostrate tissue sample.

21. The system of claim 20 wherein the medical classification scheme includes a Gleason'"'"'s grade and score for a human prostrate tissue sample.