Segmentation of Soma Cells

1. Quantification of soma cells in
IHC images
Preliminary results september 2011
Rodrigo Rojas-Moraleda
 Segmentation of IHC images based on Haar­like features and SVM classifier
 Quantiffication of IHC expression by Vesselnes.

2. This laboratory focuses on:
● Understanding the molecular basis of perturbations of subcellular
organelle function.
● Establish relationship to pathological conditions affecting the nervous
system.
● Develop of prototypic therapies to prevent this damage.

3. THE ENDOPLASMIC RETICULUM (ER) STRESS IN
PARKINSON'S DISEASE.
 Parkinson's disease (PD) is the second most
common neurodegenerative disease, affecting
at least 1% of the population over 55 years old.
 The major clinical symptom of PD is impairment
of motor control as a result from extensive dopaminergic neuron
death in the substantia nigra pars compacta (SNpc)

4. The problem
 The mechanism involved in dopaminergic
neuron loss in PD remains speculative.
 Many different molecular mechanisms are
proposed to explain the loss of dopaminergic
neurons in Parkinson Disease (PD), including
oxidative stress and mitochondrial damage.
 Interestingly, increasing evidence from genetic and toxicological
models of PD suggest a possible involvement of endoplasmic
reticulum stress (ER) and the unfolded protein response (UPR)
in disease process

5. The problem
 This study explore the possible impact of targeting XBP­1, one of
thetranscriptional factors involved in the UPR, in the survival of
SNpc under basal andpathological conditions

6. Experimental Setup
 One of the most frequently used pharmacological PD modelin
rodents is the unilateral injection of 6­hydroxydopamine (6­
OHDA) in the striatum(Dauer et al, 2003).
 This toxin acts specifically in dopaminergic neurons, inducing
aretrograde damage, which eventually results in cell dysfunction
and death (Blum et al,2001).

7. Experimental Setup
 Is analized the progression of the hemilateral lesion in the SNpc after
4 and 7 days post­injection of different concentrations of 6­OHDA
 Observing a progressive decrease in the number of
tyrosinehydroxylase (TH)­positive cells in the SNpc of injected
side (ipsilateral) compared to the non­injected side.
 The numberof TH ­ positive neurons is quantified by counting
manually the stained objects in theIHC images.

8. Objectives
 Automate the acounting of neurons in order to
calculate the ratio of surviving neurons inthe
ipsalateral side in the SNpc.
 Compute the ratio of dendrite projection between
the Ipsilateral and Contralateral sides of the
SNpc.

9. Image Processing

12. Neuron segmentation
 Change into color model which maximize de
information to the segmentation.

13. Change of the color space
 The images were transform from de RGB color
space to HSV table.
 Hue : encode the color
0º = RGB(1, 0, 0)
60º = RGB(1, 1, 0)
120º = RGB(0, 1, 0)
180º = RGB(0, 1, 1)
240º = RGB(0, 0, 1)
300º = RGB(1, 0, 1)
360º = 0º
 Saturation: Encode the pureness intensity
Distance to the black and white axis
 Value: Encode the brightness

16. Neuron segmentation
 Increase the problem dimensionality by use of
Haar­like features descriptor
 The features can be derived from the Haar Wavelet.
 Provide a projection basis comparable in some
cases to the projection basis obtained by PCA and
solving the eigen values problem

17. Haar-like features

18. SVM Classifier
 SVM Engine: Libsvm
 There is no gold standard for the neuron morphology
 Exist a ground truth for the quantification
 Gold standard arbitrarily defined by masks drew manually to
achieve the best segmentation possible.
 Training based on pixels and his projected properties.
 92% average accuracy in training.

19. Preliminary Segmentation
Results

26. Preliminary results
 Due is not yet posible segment individual cells we
propose use of area of IHC expression as a
parameter to achieve the Ipsilateral Contralateral
ratio.

27. Statistics
Imagen SVM-Haar Ground Truth
GFP_WT1_SN1 64,15 85,50
GFP_WT1_SN2 64,28 94,70
GFP_WT1_SN3 26,87 60,80
GFP_WT1_SN4 58,91 68,70
GFP_WT2_SN1 52,37 71,60
GFP_WT2_SN1 63,91 89,10
GFP_WT2_SN2 61,70 75,20
GFP_WT2_SN3 95,12 92,10
GFP_WT2_SN4 42,96 56,90
GFP_WT3_SN1 53,55 69,70
GFP_WT3_SN2 49,46 70,10
GFP_WT3_SN3 77,4 81,20
GFP_WT4_SN1 51,49 64,80
GFP_WT4_SN2 43,98 85,10
GFP_WT4_SN3 82,95 99,00
GFP_WT4_SN4 72,25 97,20
Imagen SVM-Haar Ground Truth
XBP1_WT2_SN1 68,11 89,60
XBP1_WT2_SN2 43,66 70,30
XBP1_WT2_SN3 70,49 98,60
XBP1_WT2_SN4 93,63 96,80
XBP1_WT3_SN2 72,51 69,90
XBP1_WT3_SN3 79,85 99,00
XBP1_WT3_SN4 57,74 88,80
XBP1_WT4_SN1 65,73 83,60
XBP1_WT4_SN4 84,04 96,40

28. Preliminary Results
GFP_WT1_SN1
GFP_WT1_SN4
GFP_WT2_SN2
GFP_WT3_SN1
GFP_WT4_SN1
GFP_WT4_SN4
XBP1_WT2_SN3
XBP1_WT3_SN3
XBP1_WT4_SN4
0
20
40
60
80
100
120
Area
Ground Truth
KOLMOGOROV-SMIRNOV
h = 1
p = 0.0013
k = 0.5200
COEF CORRELATION
0.75

29. IHC Expresion
 HessianVesselness filter, in order to segment shapes
like vessels or ridges.

32. Quantify the IHC by Vesselness
Area Ground Truth
GFP_WT1_SN1 85.91 85.50
GFP_WT1_SN2 88.88 94.70
GFP_WT1_SN3 69.80 60.80
GFP_WT1_SN4 87.34 68.70
GFP_WT2_SN1 80.01 71.60
GFP_WT2_SN1 76.30 89.10
GFP_WT2_SN2 72.66 75.20
GFP_WT2_SN3 98.17 92.10
GFP_WT2_SN4 53.55 56.90
GFP_WT3_SN1 76.10 69.70
GFP_WT3_SN2 68.27 70.10
GFP_WT3_SN3 82.13 81.20
GFP_WT4_SN1 78.03 64.80
GFP_WT4_SN2 93.38 85.10
GFP_WT4_SN3 84.27 99.00
GFP_WT4_SN4 77.50 97.20
Area Ground Truth
XBP1_WT2_SN1 88.73 89.60
XBP1_WT2_SN2 82.02 70.30
XBP1_WT2_SN3 88.91 98.60
XBP1_WT2_SN4 96.53 96.80
XBP1_WT3_SN2 77.78 69.90
XBP1_WT3_SN3 93.91 99.00
XBP1_WT3_SN4 82.44 88.80
XBP1_WT4_SN1 83.49 83.60
XBP1_WT4_SN4 96.90 96.40

33. Quantify the IHC by Vesselness
GFP_WT1_SN1
GFP_WT2_SN1
GFP_WT2_SN4
GFP_WT4_SN1
XBP1_WT2_SN1
XBP1_WT3_SN2
XBP1_WT4_SN4
0.00
20.00
40.00
60.00
80.00
100.00
120.00
Area
Ground Truth
KOLMOGOROV-SMIRNOV
h = 0
p = 0.4141
k = 0.2400
COEF CORRELATION
0.73

34. Next Steps
 Deduce a Haar­like features based on the PCA
eigenvalue of a set of neurons images.
 Improve the SVM quantification by template
matching.
 Improve Vesselness quantification of IHC
expresion.