Accuracy in Fluorescent Cell Image 

Segmentation Algorithms

Adele Peskin, Alden Dima, Joe Chalfoun, and James J. Filliben

National Institute of Standards and Technology

Boulder, CO. and Gaithersburg, MD. USA
Outline – Segmentation Accuracy

 Image data created for this purpose

 Initial segmentation and visualization studies

 New segmentation method to create reference data 

for larger scale testing

 Extended Edge Neighborhood: new metric to 

analyze segmentation accuracy
Data Description

 2 cell lines:

A10 smooth rat cells

NIH3T3 fibroblasts

 5 imaging conditions: 

vary illumination; exposure
Imaging Conditions
Initial Segmentation Studies

 Compare 9 segmentation algorithms, across imaging 

condition and cell line, 16 images, 71 cells

 Visualization of image clarity across imaging condition 

and cell line
Bivariate Indexes

 Many comparisons use Jaccard Similarity Index:

 Reference data set A, and segmentation mask B:
Bivariate Indexes

Divides performance into 4 

regions:

Dislocation: TET and TEE 

small

Overestimation: TET large, 

TEE small

Underestimation: TET small, 

TEE large

Good: TET, TEE large
Example of Bivariate Evaluation

 5-means clustering undersegments, in general

 Define a Segmentation Distance to (1.0,1.0) perfect 

segmentation
Initial Results – 71 Cells

 Segmentation accuracy: related to both imaging 

condition and cell geometry
Initial Results: 9 Segmentation 

Methods
Scaling up to 40000 cells

 Reference data: currently collected manually

 New segmentation technique allows us to collect 

reference data automatically, based on human 

vision: how the eye determines a boundary
Consistency in Manual Segmentation

 Analyze reference data sets created manually
Consistency in Manual Segmentation
Defining How Humans Look at Cell 

Edges

 Background pixel intensities should be close to 

one another
Defining How Humans Look at Cell 

Edges

 Edge pixel 

intensities 

should vary 

by a lower 

ratio
Compare with Manual Segmentation
Bivariate Analysis Manual 

Segmentation
Image Features for Consistent 

Manual Segmentation

• Cell Size

• Roundness: Perimeter to Area Ratio
Extended Edge Neighborhood

 Fraction of pixels near cell edge, at risk for 

segmentation error

 Function of edge quality, defined by gradient

 Function of cell geometry: are most pixels at the 

edge?
Edge Quality Calculation

 Based on the pixel 

intensity gradient at the 

cell edge

 Used to measure 

thickness of cell edge
Extended Edge Neighborhood (EEN)

 Edge pixels = thickness of edge * perimeter

 Ratio of Edge pixels/Total pixels

 EEN: 0.1 (high edge quality, large, round) 

EEN: 0.4 (high edge quality, small, spindly) 

EEN: 1.0 (low edge quality, small, spindly)
Previous Studies: EEN
Best Algorithms From Previous 

Study: 40 000 Cells

 First look at cell distributions as a f(EEN)
4 Methods: Best for individual cells
Segmentation Distance vs. EEN
Each Individual Cell Line

 A10 cells: EEN approx. 0.1-0.25 

3T3 cells: EEN approx. 0.2-0.5
Standard Deviations for Clusters
Combine Data from Each Cell Line

 Distance = 0.15 + EEN * 0.477 

correlation coefficient = 0.9815
Conclusions

 40000 cells: see relationship between segmentation 

accuracy and extended edge neighborhood

 In general, cells with higher area-to-perimeter ratios will 

segment with higher accuracy (larger, rounder).

 Edge quality & segmentation accuracy directly related