Transcript NAM PowerPoint - Digital Pathology Association

What difference does a
difference make?
Elizabeth Little, Ph.D.
26-Oct- 2010
Talk overview
• Introduction
• Tissue thickness variation
– Using best histological practices
• Stain intensity variation due to tissue thickness
• The difference matters
– Could impact algorithm functionality
Systems integration
source: www.vagabondish.com
The Hematoxylin & Eosin (H&E) slide
• Numbers
– In 2009, 330 million histology slides were produced in the United States
– 83% (274 million) were stained with H&E
• Pathologist
– Potential first look at the disease state
• Cost
– Dollars vs. thousands of dollars for more advanced testing
Impacts of H&E stain variability
• Pathologist workflow is impacted by staining variability
– Repeat slides
• Imaging workflow is also impacted by staining variability
– Algorithms can by impacted by stain variability
Antecedents that are helpful for H&E slide
image analysis
• Control of the stain variation
– Under best practices we can control stain variability to a certain degree
• Algorithms that are robust against stain variation
Staining variables we cannot control
- tissue type affects stain intensity
Grey scale intensity differences - skin vs.
kidney
10000
Pixel
count
(N)
8000
6000
Kidney
Skin
4000
2000
0
250
200
150
100
Intensity Level
50
0
Staining variables that we have some control
over - tissue thickness
impacts stain intensity
2 micron
4 micron
Grey scale intensity difference due to tissue thickness
5000
4000
Pixel
count
(N)
2 micron slice
3000
4 micron slice
2000
1000
0
255
204
153
102
Intensity level
51
0
Talk overview
• Introduction
• Tissue thickness variation
– Using best histological practices
• Stain intensity variation due to tissue thickness
• The difference matters
– Could impact algorithm functionality
Possible sources of variations in section
thickness in the histology laboratory
• Fixative
• Duration of fixation
• Tissue processing
• Paraffin
• Tissue block
• Microtome
• Histologist
Objective – measure the sectioning
process impact on tissue thickness
• 1 tissue block used
• 1 microtome
• 2 settings
– Automated (32 slides per histologist)
– Manual (32 slides per histologist)
• 2 histologists
– 22 years of experience vs. 4 years of experience
Tissue thickness variability testing outline
• Section
– Tissue was sectioned using a microtome
setting of 4 microns
• Measure Section Thickness
– Interferometry
• Stain
– H&E
• Measure intensity
– Whole slide imaging
Measuring tissue thickness using vertical
scanning interferometry
source: cnx.org
Tissue thickness using interferometric
measurements
• Glass vs. paraffin
• Tissue was not measured
•Interferometer limitation
•Glass level variability
• Measurements taken at 6
locations repeatedly
How well are we using the interferometer?
Source
Standa %
deviati
Total
(gage)
0.29
0.80%
Repeatability
equipment
0.29
0.79%
Reproducibilit 0.03
operator
0.01%
Slide variation 3.20
99.20%
Total
100.00%
3.21
How good is our tissue thickness
measuring system? - gage R & R
Equipment
variation – 0.79%
Operator
variation – 0.01%
Equipment Variation
Operator Variation
Sample
variation – 99.20%
Tissue Thickness
Variation
Slice thickness variation – by histologist
Histologis Number
slides
Combined 128
1
2
64
64
Measured
average
S.D. (mm)
4.74 ± 0.16
4.65 ± 0.10
4.84 ± 0.16
• Nominal setting was 4
microns
• Both Histologists cut
significantly thicker than 4
microns
• Both Histologists cut at
significantly different
thicknesses from each other
Manual vs. automated microtomy impact
on tissue thickness
Histologist Microtome
setting
Measured
thickness
± S.D. (mm)
1
Automated
4.65 ± 0.13
Manual
4.65 ± 0.08
Automated
4.91 ± 0.16
Manual
4.76 ± 0.12
2
• Histologist 1 mean thickness was not impacted by microtome
setting
• Both histologists had statistically significant more variability using
the
automated setting as compared to the manual setting
Block influences tissue thickness
Tissue Measured
block average ±
(um)
Tissue 4.65 ± 0.13
(n=32)
Tissue 4.60 ± 0.12
(n=16)
Tissue 4.36 ± 0.12
three
(n=16)
• Histologist 1 was the cutter
• Automated setting used
• Tissue 3 was cut significantly
thinner than tissues 1 & 2
Summary of tissue thickness measurement
results
1. Histology (location within block, slice selection, soaking, etc.)
•
Difference in mean tissue thickness
2. Microtome setting – automated vs. manual
•
Both histologists were impacted by setting
3. Block
•
Blocks 1 and 2 were cut more thickly than block 3
Talk overview
• Introduction
• Tissue thickness variation
– Using best histological practices
• Stain intensity variation due to tissue thickness
• The difference matters
– Could impact algorithm functionality
Stain intensity variation due to tissue thickness - normal breast
lymph node study
3 micron
4 micron
Objective – measure tissue thickness impact
on stain intensity
• Tissue was sectioned and measured for
thickness
• All slides were stained using the same
method
• All slides were scanned using whole slide
imaging and their average intensities
were measured
Lymph node – 1 micron makes a measurable difference
Effects of tissue thickness on intensity
250
Intensity
200
150
Intensity
100
Linear Fit
50
0
2
2.5
3
3.5
4
Tissue thickness ( mm)
4.5
5
Talk overview
• Introduction
• Tissue thickness variation
– Using best histological practices
• Stain intensity variation due to tissue thickness
• The difference matters
– Could impact algorithm functionality
Grey scale intensity differences
Effects of tissue thickness on binning
5000
Pixel count
4000
2.62 micron
3.32 micron
3.43 micron
4.37 micron
(N)
3000
2000
1000
0
255
204
153
102
Intensity level
51
0
Summary
• Expected vs. measured is different
• The difference is quantifiable
– Tissue thickness
– Stain intensity
• The difference matters
– Could impact algorithm functionality
• Tissue thickness and stain intensity correlate as expected
Further studies
• Intensity vs. tissue type
• Microtome bounce
• Histology vs.
–
–
–
–
Drift
Knife
Location in block
Degrees of fixation
Acknowledgments
Cindy Connolly
Wendy Lange
Allison Cicchini
Heather Free
Aaron Ewoniuk
Jonathan Hall
Mike Cohen, Ph.D.
David Clark, Ph.D.