20111005 ihc-neuro
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Quantification of soma cells in IHC images
Preliminary results september 2011Rodrigo Rojas-Moraleda
Segmentation of IHC images based on Haarlike features and SVM classifier
Quantiffication of IHC expression by Vesselnes.
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.
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)
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
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
Experimental Setup
One of the most frequently used pharmacological PD modelin rodents is the unilateral injection of 6hydroxydopamine (6OHDA) 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).
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.
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.
Image Processing
Neuron segmentation
Change into color model which maximize de information to the segmentation.
Change of the color space
The images were transform from de RGB color space to HSV table.
Hue : encode the color0º = 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
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
Haar-like features
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.
Preliminary SegmentationResults
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.
StatisticsImagen 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
Preliminary Results
GFP_WT1_SN1GFP_WT1_SN4
GFP_WT2_SN2GFP_WT3_SN1
GFP_WT4_SN1GFP_WT4_SN4
XBP1_WT2_SN3XBP1_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
IHC Expresion
HessianVesselness filter, in order to segment shapes like vessels or ridges.
Quantify the IHC by VesselnessArea 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
Quantify the IHC by Vesselness
GFP_WT1_SN1GFP_WT2_SN1
GFP_WT2_SN4GFP_WT4_SN1
XBP1_WT2_SN1XBP1_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
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.