Computational Biology:

A Measurement Perspectiv

Alden Dima

Information Technology Laboratory Information Technology Laboratory

alden.dima@nist.gov
Problem

• High-throughput technologies are generating 

throughput technologies are generating 

amounts of complex data that is difficult to pand convert into knowledge

• Issues exist throughout data lifecycle:

– Acquisition– Analysis– Archiving– Interchange
Imaging Technologies Imaging Technologies

• Ima

g g g gy g in

g technolo

gies are increasin

gly bein

g u

both as diagnostic tools and as research tools biosciences

– N l h d d df d l 

Novel met

h

o

ds are nee

d

e

d

for automate

d analys

comparisons

– Correlation among studies is difficult at best

– Off-the-shelf methods are not well characterized 

contribute significantly measurement uncertainty

– Need to combine features from images with othe

Need to combine features from images with othe

biological or medical sources
Example from Literature Example from Literature

Original Data –

64% Poorly

Segmented

Only Well 

Segmented Cells

• Relies heavily on cell 

imaging

Segmented

• Gigabytes of images 

collected

• Algorithms treated 

as “black boxes” 

• Man

pub

rarerare

• Poo

segsign

Source:

effe

High Content Screening

Hill, LaPan, Li, Haney (Wyeth Research)Impact of image segmentation on high content screening data quality for SK-BRBioMed Central Bioinformatics 2007
Computational Biology: Single Cell An

Intracellular molecular reactions and interactions that control the respons

and fate of cells and organisms cannot be unambiguously compared and combined due to a lack of standards and validated protocols

NIST RoleProvide the measurement tools and standards that enable quantifiable and 

reproducible measurements of cells and their interactions through:

• Standard data/metadata Standard data/metadata format for image capture storage retrieval format for image capture, storage, retrieval, analys

analys

• Software to enable high throughput cell image analysis and interoperability• Standards and validation required to ensure reproducible image analysis• Multi-site experiments to test software and validation protocols

Technical Approach•Create and evaluate an integrated data collection, organization and analysis 

infrastructure for cellular imaging 

•Experimentald l ists an

d computational scientists - f h h l dd 

focus on t

he p

hysical stan

dar

ds an

protocols for data collection, image processing and analysis, storage of data and metadata, and evaluation of results
Segmentation Evaluation Segmentation Evaluation

• Determine which segmentation techni

Determine which segmentation techni

and associated parameters can be used 

reliably determine the morphology of reliably determine the morphology of 

the purposes of comparing cell lines as 

of a new standard procedure under of

a new standard procedure under

development 
Variability Across Method

Variability Across Method

Different segmentation techniques can change results
Cell 1 was an edge

cell in one case Red = k-means (k = 2)

Blue = OtsuGreen = Maximum Entropy

Cell should White = Ground Truthhave been i d gnore

Prel 

iminary e

shows that rvary by more 

A10 Cell LineTh diff t 

In one casethis cell was segmented

ree different segm

techniques

as three particles
Variability p Across Implementat

Different implementations of the same technique can change result

Comparing two 

implementations 

across three across three

images

“Software as measurement”
Experimental Design Experimental Design

• Two cell lines Two cell lines

– A10 & 3T3 A10

& 3T3

• Cell preparation follows ASTM docume• Different image exposure and filter leve

Different image exposure and filter leve

• No flat-field correction• M lti l li t t i M

ulti

ple sampling 

to cap

ture noise

• Ground truth: expert manual segmenta

• Result: approximately 8000 images 

– 80 

which are used for initial evaluation wo
Red Channel Images Channel Images - A10

Low Exposure, Optimal Filter Medium Exposure, Optimal F

High Exposure, Optimal Filter Medium Exposure, Non-optimal 

16-bit Gray-scale Images
Red Channel Images Channel Images - 3T3

Low Exposure, Optimal Filter Medium Exposure, Optimal F

16-bit Gray-scale Images

High Exposure, Optimal Filter Medium Exposure, Non-optimal 
Pipeline Code Generator

ROI

Normalization

ImageJScripts

ImageJ Masks,

Statistics

Images

Postprocessing Plug-in Wrappers

Reference

Seg Data Set .

Methods
Planned Progression of Evalua

Planned Progression of Evalua

Basic Algorithms Basic Algorithms

Edge-based Methods

Advanced Techniques Advanced Techniques
Algorithms Evaluated Algorithms Evaluated

Basic Methods Basic Methods

Otsu Maximum Otsu

K

-Means Clustering

NIST I J

Entropy

NIST I J

K Means Clustering

K 2

Threshold 

Cl t

Combined 

ith Ot

Comb

with M

NIST ImageJ NIST ImageJ K=2 Clusters 

(K=3,4,5)

with Otsu 

(K=3,4,5)

with Otsu 

(K=3,4,5) Entro

(K=3,

NIST ImageJ NIST ImageJ Imag



Live Cell Tracking Live Cell Tracking

• There are few automated image analys

There are few automated image analys

options available to the cell biologists to 

quantify live cell image data quantify live cell image data

• Segment and track cells in an image seq

to quantify quantify the total fluorescence total fluorescence inten

inten

individual cells over time
Manually Tracking NIH 3T3 C

Manually Tracking NIH 3T3 C

Phase contrast on left, GFP on right 
CompBio Cell Tracker Cell Tracker

Live Cell Images Live Cell Images Live Cell Images Live Cell Images Live Cell Images Live Cell Images

Segmentation Segmentation Segmentation Segmentation

Cell Tracking

Cell Lineage Intensity Plot Other Out

MigrationVelocity, e
Automatic Tracking of NIH 3T3 C

Automatic Tracking of NIH 3T3 C
Automatic Tracking of NIH 3T3 C

Automatic Tracking of NIH 3T3 C
Thank You! Thank You!

alden.dima@nist.gov