Adaptive classification for whole slide tissue segmentation

Patent metadata

Field	Value
Publication number	US-2016335478-A1
Application number	US-201615222889-A
Country	US
Kind code	A1
Filing date	Jul 28, 2016
Priority date	Jan 28, 2014
Publication date	Nov 17, 2016
Grant date	—

Abstract

A method of segmenting images of biological specimens using adaptive classification to segment a biological specimen into different types of tissue regions. The segmentation is performed by, first, extracting features from the neighborhood of a grid of points (GPs) sampled on the whole-slide (WS) image and classifying them into different tissue types. Secondly, an adaptive classification procedure is performed where some or all of the GPs in a WS image are classified using a pre-built training database, and classification confidence scores for the GPs are generated. The classified GPs with high confidence scores are utilized to generate an adaptive training database, which is then used to re-classify the low confidence GPs. The motivation of the method is that the strong variation of tissue appearance makes the classification problem more challenging, while good classification results are obtained when the training and test data origin from the same slide.

First claim

1 . A tissue analysis system comprising:
an image region identifier module configured to receive image data representative of an at least two-dimensional image of a tissue sample and to output image region data that identifies a plurality of subsets of said image data, each image data subset being representative of a unique, spatially contiguous region of said image; an image region classifier module that is configured to classify, for a plurality of said image regions, the respective image region as one of a plurality of tissue types using the respective image data subset for querying a database of tissue characteristics and to determine a confidence score indicative of a confidence of said classifying of the respective image region; a database modifier module that, for a plurality of said image regions having a confidence score falling within a first range, is configured to effect modification of said database such that the resultant modified database comprises data representative of the tissue type classified to the respective image region and tissue characteristic data obtained from the respective image data subset, said first range being a range of confidence scores that includes a confidence score representative of certainty that the classification is correct; and an image region reclassifier module that is configured to reclassify, for a plurality of said image regions having a confidence score falling within a second range distinct from said first range, the respective image region as one of said plurality of tissue types using the respective image data subset for querying said modified database and to output said one of said plurality of tissue types as an analysis result.

2 . The tissue analysis system of claim 1 , further comprising:
a tissue imaging device configured to image a tissue sample to obtain raw image data, wherein said received image data is obtained from said raw image data.

3 . The tissue analysis system of claim 1 , further comprising:
a tissue staining device configured to stain said tissue sample to obtain a stained tissue sample; and a tissue imaging device configured to image said stained tissue sample to obtain said image data, wherein said received image data is obtained from said raw image data.

4 . The tissue analysis system of claim 1 , further comprising:
a data storage system configured to store said database, wherein said database comprises, for each of a plurality of tissue image regions, data representative of an at least two-dimensional image of tissue, data representative of at least one tissue feature, data representative of a tissue type and data representative of a confidence score, wherein the database is a pre-built first training database that is used for the image classification and confidence score determination by the image region classifier module, wherein the database modifier module is configured to generate a second training database that is constituted by the data representative of the tissue type classified to the image regions and the tissue characteristic data obtained from the image data subsets of the plurality of said image regions having a confidence score falling within the first range and to combine the first and the second training database to provide the modified database.

5 . The tissue analysis system of claim 1 , wherein said image region classifier module comprises a support vector machine and is configured to use an output of said support vector machine for determining said confidence score.

6 . The tissue analysis system of any claim 1 , wherein said classifying the respective image region comprises extracting at least one feature from the respective image region using the respective image data subset and said data obtained from said database, said feature belonging to the group consisting of textural features, biological features, intensity features, gradient features, Gabor features, co-occurrence features, and nuclei features.

7 . The tissue analysis system of claim 1 , wherein said reclassifying the respective image region comprises weighting data of the respective image data subset and the data obtained from said modified database using at least one of a spatial proximity value, a confidence score and feature similarity value.

8 . The tissue analysis system of claim 1 , wherein:
said image region classifier module comprises an image channel extractor; and said classifying the respective image region comprises separating, using said image channel extractor, at least the respective region of said image into one or more component channels and extracting at least one feature from the respective image region using any of said component channels of the respective image region and said data obtained from said database, wherein said feature belongs to the group consisting of textural features, biological features, intensity features, gradient features, Gabor features, co-occurrence features, and nuclei features, and said component channel belongs to the group consisting of a hematoxylin channel, an eosin channel, and a luminance channel.

9 . The tissue analysis system of claim 1 , wherein:
said image region classifier module is configured to build classifier logic using data of said database and to apply, for each of said image regions, said classifier logic to said image data subset of the respective image region to determine the respective tissue type and the respective confidence score; said database modifier module is configured to find those image regions having a confidence score falling within said first range and those image regions having a confidence score falling within said second range and to combine said database and the respective tissue types and the respective image data subset of said image regions found to have a confidence score falling within said first range to obtain said modified database; said image region reclassifier module is configured to modify said classifier logic by means of machine learning using data of said modified database and to apply, for each of said image regions found to have a confidence score falling within said second range, said modified classifier logic to said image data subset of the respective image region to determine the respective reclassified tissue type; said system is configured to output the respective tissue type of each of said image regions found to have a confidence score falling within said first range and the respective reclassified tissue type of each of said image regions found to have a confidence score falling within said second range as a classification result.

10 . The tissue analysis system claim 1 , wherein:
said database modifier module is configured to determine, for each of said plurality of tissue types and only for those image regions having a confidence score falling within said first range, the total number of image regions having the respective tissue type, and to effect said modification of said database only for those tissue types for which the total number of image regions having the respective tissue type exceeds a respective threshold number for the respective tissue type.

11 . The tissue analysis system of claim 10 , wherein, for those tissue types for which the total number of image regions having the respective tissue type exceeds a respective threshold number for the respective tissue type, said database modifier module is configured to effect said modification of said database such that said modified database contains solely tissue characteristic data obtained from the respective image data subsets.

12 . A tissue analysis method, comprising:
receiving image data representative of an at least two-dimensional

Assignees

• Ventana Med Syst Inc

Inventors

• Bredno Joerg
• Chefd'Hotel Christophe
• Chen Ting
• Chukka Srinivas
• Nguyen Kien

Classifications

• G06V10/771

  Feature selection, e.g. selecting representative features from a multi-dimensional feature space · CPC title

• G06T7/0012 Primary

  Biomedical image inspection · CPC title

• A61B17/3415

  for introducing tubes or catheters, e.g. gastrostomy tubes, drain catheters (A61B17/3417 takes precedence; body piercing catheter guide needles A61M25/06) · CPC title

• G06F18/2113

  by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation · CPC title

• G06V30/194

  References adjustable by an adaptive method, e.g. learning · CPC title