Histomorphometry
Or
How to get numbers out of slides
Stephen Greenwald
Pathology Group, Institute of Cell & Molecular Science
Barts & The London School of Medicine & Dentistry
Outline



What is morphometry?
Why histomorphometry?
Measurement methods



Standard processes in computerised histomorph.





“Manual”
Computerised
Image capture
Enhancement
Thresholding
Measuring
Micro CT of stented arteries
What is morphometry?
A body of methods for obtaining numerical
information about the shape and size of a
structure in terms of quantities such as:
volume
 surface area
 relative amounts of each component
 orientation, interconnections
 distribution of substructures
 etc.

Why histomorphometry?

When applied to biological tissue
examined microscopically it useful in
correlating structure and function e.g.
Alveolar or gut villus surface area
 Arterial composition and elasticity
 Quantification of

Hyperplasia, dysplasia, hypertrophy
 Immunohistochemical or flourescent markers


Area or intensity
Major challenge

To extract information about large 3-D
structures from microscopic
measurements on thin 2-D sections

To do this histomorphometry uses the:
Delesse Principle
Delesse Principle
“In a rock composed of a number of
minerals, the area occupied by any given
mineral is proportional to the volume of the
mineral in the rock”
Repeated determinations of the area fraction
will yield an estimate of the volume fraction.
 The more determinations; the better the
estimate.

Delesse A. (1847) Procede mechanique pour determines la composition des roches.
Comptes Rendus de l’Academie des Science (Paris) 25, 544)
How to estimate area fraction


paper cutting and weighing
planimetry
Planimeter
How to estimate area fraction



paper cutting and weighing
planimetry
dot counting
Dot counting
Nuclear area/cell area = number of dots in nuclei/number of dots in cell
Absolute area of a structure = number of dots in structure x area of dot square
How to estimate area fraction




paper cutting and weighing
planimetry
dot counting
square counting
Square counting
34 squares
7 squares
How to estimate area fraction





paper cutting and weighing
planimetry
dot counting
square counting
pixel counting in a digital image

semi- or fully automatic system
Computerised histomorphometry
TV camera
ADC
Microscope
LUT
Video
memory
ADC
Image
processor
Main
processor
Storage
LUT
User.
mouse,
light-pen
Stimulants
Pixel counting
Pixel counting
Recognising objects by colour
More difficult measurements

length


surface area


alveoli, gut villi etc.
counting discrete objects


seminiferous tubules
cells, nuclei, alveoli, elastic lamellae etc.
Size distribution

cells, nuclei, tumours etc.
How to estimate length
(Buffon’s needle problem)

If you drop a nail/needle
on the floor, what is the
probability it will come to
rest over a crack between
the floor boards?
Louis le Clerc, Compte de Buffon,
(1707 -1788)
French naturalist & polymath
Buffon needle problem

d
l
The probability (p) of the needle (or
nail) landing on a join depends on
the length of the needle (l), the
width of the boards (d) and the
angle it makes with the direction of
the boards (). The angle
determines the projected (i.e.
effective) length of the nail, (lproj)
p
lproj
where l proj is the average projected lengt h
d


lpro
j
lproj 



2l

2
 cos.d
0
2l

p
2l
d
Inverse problem;
i.e. throw the grid at the nails

Imagine a contour of
length L composed of
small elements, l
Probability (p) of an intersection :is
2l
p
d
Number of "throws" is:


Now throw the grid
(spacing, d) at the
nails (i.e. the small
elements)

L
l
Number of int ersections
N int is the number of
"throws" t imes the probability of an intersect ion
L 2l
N int  .
l d
Rearranging t he above expression,
we can calculate the total length
L)(
d.N int
L
2
Villus and crypt length measurements
How to measure surface area

Measure absolute volume (V) of entire organ


Estimate tissue volume fraction from area fraction


Archimedes, weight (knowing density)
calculate tissue volume
Count intercepts (Nint) using grid of total length (L)
SA 

2VN int
L
Pattern recognition

normal v abnormal morphology

displasia, metaplasia
Normal nucleus:
Area = 10m2, perimeter =14m

Abnormal nucleus:
Area = 10m2, perimeter =26m
counting poorly stained structures

nuclei, nuclear organelles, leucocytes
Standard processes


Image capture
Enhancement



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contrast/colour
background correction
Thresholding (identifying structures of
interest)

10kB
colour
intensity
shape
Measurement

1.5MB
area, perimeter, counting
1kB
Image enhancement:
shade correction
Uneven background illumination
Image enhancement:
shade correction
Original image with uneven illumination
Image enhancement:
shade correction
Shade corrected image
Contrast enhancement:
Original
Enhanced
Thresholding by colour
Thresholded
Enhanced
Measurement
Section Field Area [sq micron] Elastin [%] Collagen [%] VSMC [%] E+C [%] V+E [%] V+C [%] Unthreshed [%] Total [%]
155-05-c2
1
22437.95
19.29
43.8
31.37
0
0
2.29
7.53
100.23
2
17619.23
22.46
39.39
29.52
0
0.47
0
8.3
100
3
17424.26
21.83
37.6
32.66
0
0.31
1.56
8.71
99.97
5
16765.18
26.06
40.76
31.91
0.02
0
0.56
2.58
101.78
18561.655
22.41
40.3875
31.365
0.005
0.195 1.1025
6.78
100.495
152-05-d
1
2
3
4
21366.93
18108.37
18895.89
20573.07
19736.065
23.61
19.64
18.56
20.61
20.605
43.85
55.43
48.15
60.18
51.9025
29.32
35.24
30.12
26.84
30.38
0
0.87
0
0
0.2175
0
2.37
1.71
1.27
1.3375
0.88
8.99
0
6
3.9675
3.93
1.62
3.21
-1.4
1.84
100.27
101.98
100.52
100.98
100.9375
145-05-d
1
2
3
4
18244.94
20463.62
15651.55
20284.31
18661.105
16.79
18.13
15.13
17.11
16.79
52.13
60.02
62.22
58.67
58.26
24.36
29.14
27.6
28.75
27.4625
0
2.63
1.91
0.34
1.22
1.07
1.64
0.97
1.57
1.3125
0.62
3.51
2.56
4.7
2.8475
8.8
1.72
1.81
3.23
3.89
100.39
101.23
101.33
101.15
101.025
143-05-D
1
2
3
4
17682.66
16752.71
18252.77
16264.18
17238.08
20.24
19.38
18.52
14.28
18.105
54.25
58.03
61.73
55.54
57.3875
24.5
23.25
19.23
24.95
22.9825
1.99
1.9
0
2.23
1.53
0.07
0.18
0.37
0.44
0.265
0.01
0.01
2.3
0
0.58
4.16
2.75
3.38
7.32
4.4025
101.08
101.33
100.99
100.64
101.01
157-05-D
1
2
3
4
14207.13
17832.78
16970.9
15497.26
16127.0175
17.18
18.35
17.73
24.48
19.435
66.19
59.46
56.9
56.19
59.685
12.97
15.65
23.37
17.1
17.2725
2.19
0
0
0.96
0.7875
0.56
0.44
0.49
1.24
0.6825
0
0.29
5.44
0.43
1.54
6.57
6.8
5.03
2.09
5.1225
100.72
100.13
99.71
100.91
100.3675
The Effect of Stent Oversize Stiffness &
Structure on restenosis

In vivo radiographic measurement of stent
dimensions in pig carotid and iliac arteries

Development of a micro CT method for
stented vessel morphometry on excised
arteries
Study aims

To quantify degree of restenosis


Effect of stent oversize and stiffness
To compare two stent types

SMART stent (a standard design)


Major problem is restenosis
Compliant ended stent (a novel design). Developed
by collaborators, J.E. Moore & Colleagues at Texas
A&M
Hypothesis
By matching the compliance of the stent to
that of the “native” artery, flow disturbances
and bending stress at the stent/artery
junction is reduced and hence restenosis is
minimised
Stents used in the Study
SMART stent
Compliant Ended Stent
Compliance Matching Stent


Rigid in the centre to
provide recoil resistance
Parabolic and
cantilevered struts



gradual change in
compliance
reduces stress
concentration and
bending
Less disturbed flow
Methods


65 stents implanted in the iliac and carotid
arteries of 17 Large White pigs
Lumen diameter determined before and
after implantation by angiography
Follow-up angiography on days 3,7 and 28
 At day 28 the arteries were pressure perfused
and removed for histology and CT scanning

Vessel dimensions determined by
automatic edge detecting algorithm
Lumen diameter [mm]
7
6
5
4
0
5
10
15
20
Position along stent [mm]
25
30
35
Micro CT of excised vessels

Vessels pressure fixed in situ (10% formol saline)

Excised and immersed in oil based contrast medium

Custom built Micro CT scanner (Dental Biophysics
QMUL)

Voxel size (30 x 30 x 30µm)

Images processed on custom software developed under
KS400 image analysis system
A trip through a stented artery
QuickTime™ and a
decompressor
are needed to see this picture.
One of about 1200 slices cut
perpendicular to the long axis of the
vessel
Image processing
Original slice
Thresholded
Stent struts
Media/Adventita only
Circle fitted
Slice measurements (CE Stent)
Lumen and stent area [mm2]
Lumen circularity
18
1.00
16
0.95
14
Lumen
Stent
12
0.90
10
8
0.85
6
0.80
4
0
10
20
Distance [mm]
30
40
0
10
20
Distance [mm]
30
40
3D reconstruction
And rendering
Conclusions

Histomorphometry is useful for counting and
measuring clearly defined structures

Limited by a lack of “intelligent” software


For histopathologists, may be valuable for
quantifying prognosis


Extremely difficult to better the human eye-brain
combination for pattern recognition/diagnosis
Measuring ratio or distribution of different tumour
markers
No immediate cause for alarm amongst
histopathologists…but watch this space.