# US 10,121,245 B2 · Identification of inflammation in tissue images

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- Number: US 10,121,245 B2
- Document ID: US10121245B2
- Status: Granted
- Kind: B2
- Application number: 15/264,836
- Filed: 2016-09-14
- Published: 2017-03-16
- Granted: 2018-11-06
- Earliest claimed: 2015-09-14
- Assignees: University of Notre Dame
- Applicants: University of Notre Dame; University of California
- Inventors: Danny Ziyi Chen; Jiazhuo Wang; John DeWolfe MacKenzie; Rageshree Ramachandran
- Web page: https://patentgrep.com/patent/US10121245B2

## CPC

- G06T 7/0012: {Biomedical image inspection}
- G06T 7/12: Edge-based segmentation
- G06T 7/162: involving graph-based methods
- G06V 10/46: Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features (colour feature extraction G06V10/56)
- G06V 20/695: {Preprocessing, e.g. image segmentation}
- G06V 20/698: {Matching; Classification}
- G06T 2207/10024: Color image
- G06T 2207/10056: Microscopic image
- G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

Systems and methods are provided for identifying markers for inflammation in a tissue image. The tissue image is captured as an image of a histology slide. Subcellular structures in the tissue image are segmented via a first automated process to identify at least one variety of immune cells within the image. Glands and vilii are identified within the tissue image via a second automated process. Neutrophils are identified within the tissue image via a third automated process. An output representing the identified glands, villi, neutrophils, and other immune cells is provided to a human operator.

## Claims

### Claim 1

A method of identifying markers for inflammation in a tissue image comprising:
capturing the tissue image as an image of a histology slide;
segmenting subcellular structures in the tissue image, via a first automated process, to identify at least one variety of immune cells within the tissue image;
identifying glands and villi within the tissue image via a second automated process;
identifying neutrophils within the tissue image via a third automated process; and
providing an output representing the identified at least one variety of immune cells, the identified glands, the identified villi, and the identified neutrophils to a human operator.

### Claim 2 (depends on 1)

The method of claim 1, wherein segmenting subcellular structures in the tissue image via the first automated process comprises:
constructing a layered graph model, comprising a plurality of vertices and a plurality of edges according to at least one constraint;
determining respective weights for each of the plurality of vertices and the plurality of edges according to at least one photometric prior; and
determining a set of boundaries, represented by a path having a lowest total energy in the layered graph model, for a nucleus and a cytoplasm of a cell represented by the layered graph model.

### Claim 3 (depends on 2)

The method of claim 2, further comprising identifying the cell as one of a plasma cell, a lymphocyte, and an eosinophil from the determined set of boundaries.

### Claim 4 (depends on 1)

The method of claim 1, wherein identifying glands and villi within the tissue image via the second automated process comprises:
generating a superpixel segmentation of the image comprising a plurality of superpixels;
classifying each of the plurality of superpixels as one of epithelium, lumen, and extracellular material;
generating an initial pseudo-probability map from the classified plurality of superpixels;
generating a plurality of candidate objects in the tissue image according to the generated initial pseudo-probability map; and
classifying each candidate object by an associated pattern recognition classifier.

### Claim 5 (depends on 1)

The method of claim 1, wherein identifying neutrophils within the tissue image via the third automated process comprises:
generating a segmentation of the tissue image such that the lobes of each cell of a plurality of cells are grouped into one segment, which contains no lobes from other cells, to provide a plurality of segments;
classifying each of the plurality of segments to identify a plurality of high confidence examples of neutrophils and non-neutrophil cells;
applying a clustering process, utilizing a Voronoi diagram of clusters model, to the classified segments to locate high-confidence and low-confidence examples of neutrophils and non-neutrophil cells;
generating a plurality of classifiers, each representing a Voronoi diagram cell; and
classifying each ambiguous segment of the plurality of segments by at least one of the plurality of classifiers as a neutrophil or a non-neutrophil.