Feature Identification for Colon Tumor Classification

UCI Interdisciplinary Computational and Applied Mathematics Program Representative:

Anthony HouJoint Work with Melody Lim, Janine Chua, Natalie Congdon

Faculty Advisors: Dr. Fred Park, Dr. Ernie Esser, and Anna Konstorum

Problem Statement

Tumor spheroids

Control Chemical Added

Biological BackgroundHepatocyte Growth Factor (HGF) has been shown to be increased in colon tumor microenvironment (in vivo)

Increased HGF is correlated with increased growth & dispersiveness

Tumor spheroids

Control +HGF

Experimental Approach

Data obtained from the Laboratory of Dr. Marian Waterman, in the Department of Microbiology at UC Irvine

Cell line used: primary, ‘colon cancer initiating cells’ (CCICs)

Cultured CCICs trypsinized and spun down

Experimental Approach (cont.)

Single cells plated in 96 well ultra-low attachment plates with DMEM, supplement, and with or without HGF at various concentrations

CCICs imaged at 10x resolution once a day for 12 days

Spheroid grown in media + 50ng/ml HGF, day 8

Our Motivational GoalHaving a set of data, biologists can see the qualitative effect when the concentration of HGF is high and when the concentration of HGF is low.

We want to find the feature(s) that can discriminate between a tumor spheroid that has high and low concentrations of HGF.

We hope this discovery can indicate which features are useful in helping biologists measure the amount of HGF in a certain colon tumor spheroid

Image Processing/Computer

Vision BackgroundClassification

We humans have an innate ability to learn to identify one object from another

Control +HGF

Now, how can we automate this process with respect to biological

images?

Classification ApproachImage Processing

Mathematical featuresShape features: Area, Perimeter/Area, Circularity Ratio, Texture features: Total Variation/Area, Average Intensity, EccentricityWhy these 6 features?

Given feature: Day

Fisher’s Linear Discriminant (FLD) Classification

Raw +HGF tumor

Segmented +HGF tumor

Thresholdedbinary image

Boundary of +HGF tumor

Binary image with boundary applied

Processing Data

Shape Information

Features from Given Shape• Area• Perimeter/Area• Circularity Ratio• Eccentricity

HGF Binary

Image Information

• Total Variation

• Average Intensity

Features from Given Image

HGF Segmented

Classification

<V1,V2, …Vn>

Tumor gets mapped to feature vectors, which get mapped to points in high dimensional space. Now how do we separate the 2 groups?

Fisher’s Linear Discriminant

Describe mapping

Fisher’s Linear Discriminant: maximize ratio of inter-class variance to intra-class variance

Project OverviewDevelop classification scheme for colon tumor spheroids grown in media with and without HGF

Broader goal is to obtain quantitative understanding of HGF action on tumor spheroids.

Feature vectors can be utilized to quantify HGF action on tissue growth in vitro.

ResultsRan FLD code on 6 features: Area, Circularity Ratio, Average Intensity, Eccentricity, Perimeter/Area, TV/Area

Train on half the data

Repeated Random Sub-sampling Cross Validation was used on all tests

ResultsRan FLD code on 6 features: Area, Circularity Ratio, Average Intensity, Eccentricity, Perimeter/Area, TV/Area

Percent Correct for Control: 91.50%

Percent Correct for +HGF: 90.99%

Results: Adding DayGood results, but our goal is to maximize percentage correct, so included time (day)

Features used: Area, Perimeter/Area, TV/Area, Eccentricity, Average Intensity, Circularity Ratio, Day

Observed some tumors similar in shape and size, so we needed a descriptor to separate those. Caused by larger control tumor from later phase having similar area & perimeter to earlier-stage HGF tumor.

Results: Adding DayGood results, but our goal is to maximize percentage correct, so included time (day)

Features used: Area, Perimeter/Area, TV/Area, Eccentricity, Average Intensity, Circularity Ratio, Day

Observed some tumors similar in shape and size, so we needed a descriptor to separate those. Caused by larger control tumor from later phase having similar area & perimeter to earlier-stage HGF tumor. Percent Correct for Control: 98.88%Percent Correct for +HGF: 100%

Next ApproachExcellent results, but curious to see if same results can be obtained using less features

Plot all separately to get an idea of their individual classifying potential

Area

Due to area differences between tumors from control and +HGF

Control=blueHGF=red

Circularity Ratio Description

C1 = (Area of a shape)/(Area of circle) where circle has the same perimeter as

shape

Circularity Ratio

Given data are relatively circular from both groups (control and +HGF)

Control=blueHGF=red

Average Intensity Description

Average Intensity: sum of the image intensities over the shape divided by area

Inversely related to density.

Smaller values indicate less light passing through, suggesting a denser object

+HGF 10ng/ml Day 11 (10x)

Control Day 8 (10x)

Average IntensityControl=blueHGF=red

• Control Group is similar in Average Intensity, whereas +HGFs are denser

• Not all are very dense, so there are some overlap with controls

Eccentricity Description

Measure of elongation of an object

Eccentricity

Due to most tumors from both groups being circular except for a few outliers

Control=blueHGF=red

Perimeter to Area Ratio

Why Normalize Perimeter by Area?

We do so because a small, jagged object may have the same area as a large, circular object. Thus, we divide by area, creating a more effective classifier.

Perimeter to Area Ratio

This is to be expected because the +HGF tumor spheroids have more dispersion, resulting in greater area, in contrast to the control tumor spheroids.

Control=blueHGF=red

Total Variation to Area Ratio Description

At every point, estimate its gradient (difference in intensities in x and y direction). Use discretization of Total Variation. Also normalized by area.

Texture+HGF 10ng/ml Day 12 (10x)

Control Day 11 (10x)

Total Variation to Area Ratio

Due to similar densities/intensities in tumors from both groups

Control=blueHGF=red

Intuition Through Trial and Error

Given the individual results, we combined the two strongest features, area and perimeter/area, and plot them both using a scatter plot

Area vs. Perimeter/Area

Control=blueHGF=red

ResultsWe obtained reasonably accurate results, having only two controls on the +HGF side if we draw an imaginary line to separate the two groups

Ran FLD code on Area and Perimeter/Area

ResultsWe obtained reasonably accurate results, having only two controls on the +HGF side if we draw an imaginary line to separate the two groups

Ran FLD code on Area and Perimeter/Area

Percent Correct for Control: 89.03%

Percent Correct for +HGF: 96.92%

EvaluationReasonably decent results, but decided to add the feature Day

EvaluationReasonably decent results, but decided to add the feature Day

Results: Area, Perimeter/Area, Day

Percent Correct for Control: 100%

Percent Correct for +HGF: 100%

“Bad” FeaturesPlotting graphs of “good” features and running FLD showed how strong those features really are.

Our first thoughts: Were the “good” features too strong that the “bad” features couldn’t exhibit their full potential as classifiers?

CR, TV/Area, Average Intensity, Eccentricity

IntuitionDecided to run FLD test to see if they perform better as a group by themselves

Results: CR, TV/Area, Average Intensity, Eccentricity

IntuitionResults: CR, TV/Area, Average Intensity, Eccentricity

Percent Correct for Control: 75.33%

Percent Correct for HGF: 55.27%

Why?

Final ThoughtsOur belief: “bad” features are not necessarily useless. Data sets vary; some may include tumors with different textures, shapes, area, and so on

Our set of features are extremely versatile

After feature identification, features can be used to further pursue broader goals such as the quantification of a certain chemical’s effect on their tumors

ConclusionEffectiveness of area vector is obviously in accordance with biological hypothesis that HGF increases cellular mitosis rate, resulting in larger tumors.

Effectiveness of perimeter/area vector quantifies contiguous cell spread, supporting hypothesis stating HGF results in a spheroid with greater perimeter/area ratio.

Tried a lot of fancy ways, but turns out the strongest features were the simplest ones that also agreed with biologists’ intuition.

Conclusion (cont.)Including Day Vs. Not Including Day

Day + less features = better resultsLess features (without day) = worse resultsUse more features (without day) = good results; separation in high dimensions

Future GoalsDevelop methods to quantify cell spread for cells that are no longer attached to the tumor.

Develop an automated segmentation scheme

OcclusionsExisting strong methods worked, but needed more preprocessing

+HGF 10ng/ml Day 13 (10x)

Future ExperimentsEXPERIMENT IDEA #1:

Run experiment w/ different concentrations of HGF We want to quantify how HGF acts with respect to increasing concentration Utilize developed feature vectors to classify images from different concentrations of HGF.

Future ExperimentsEXPERIMENT IDEA #2:

Stain spheroids for proteins associated with stem and differentiated cell compartments Stains can be incorporated into new feature vectors to identify whether HGF-induced changes in stem / differentiated cell concentrations are significant enough to improve image classification.

AcknowledgementsNSF

Professors Jack Xin, Hongkai Zhao, Sarah Eichorn

Advisors: Dr. Fred Park, Dr. Ernie Esser, and Anna Konstorum

Laboratory of Dr. Marian Waterman

Group: Janine Chua, Melody Lim, Natalie Congdon

MBI

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[2] Caroline Coghlin and Graeme I Murray. Current and emerging concepts in tumour metastasis. J Pathol, 222(1):1{15, Sep 2010.

[3] A De Luca, M Gallo, D Aldinucci, D Ribatti, L Lamura, A D'Alessio, R De Filippi, A Pinto, and N Normanno. The role of the egfr ligand/receptor system in the secretion of angiogenic factors in mesenchymal stem cells. J Cell Physiol, Dec 2010.