Heidelberg imed-machine learning

Motivation Image segmentation Machine Learning methods Quantification

Segmentation and Quantification of IHC-stained Tissue Images byMachine Learning Methods

Introduction to Statistical Learning Methods

Rodrigo Rojas Moraleda

December 22, 2011

Rodrigo Rojas Moraleda — Segmentation and Quantification of IHC-stained Tissue Images by Machine Learning Methods 1/41


Outline

1 Motivation

2 Image segmentation

3 Machine Learning methods

4 Quantification



Outline

1 Motivation



4 Quantification



Motivation

Automated high resolution scanning microscopes digitize large sets of histological samples, andaccess anatomical features of cells and tissues from the mm range down to a resolution of 230nm.

Different Levels of resolution from a conventional tissue scaner



Motivation

The high quality of the scans allows to collect quantitative morpho-topological features of cellsand tissue from different samples which can be coupled to functional information throughconcomitant immunostaining or fluorescent protein.



Outline

1 Motivation



4 Quantification



SegmentationMeaning

Image segmentation is:

The process of partitioning an image into multiple segments (e.g. in raster images, sets ofpixels).

The goal of the segmentation is to simplify and/or change the representation of an imageinto something that is more meaningful and easier to analyze.

Sample of assited segmentation using minimal surfaces



SegmentationResults in medicine

Segmentation of Cerb2 in breast tissue..

Average continuity of the membrane stain, as a measure about the Immunostainingexpresion.

The geometric and spatial constraints of the antibody stain.

etc.







etc.







etc.







etc.



SegmentationResults in biology

Segmentation of dendrites and somacell from SNpc of mice as a Parkinson’s disease model (PD).

Number of survival soma cells.

Size and morfological characteristics.

Size of dendrite projections.

etc.








etc.








etc.








etc.








etc.



SegmentationThresholding method

Thresholding:The simplest method of image segmentation is called the thresholding method.This method is based on a clip-level (or a threshold value) to turn a gray-scale image into abinary image. The key of this method is to select the threshold value (or values whenmultiple-levels are selected).

Snow segmentation, by treshold over the pixel intensyties.



SegmentationHistogram-based methods

Histogram-based methods: In this technique, a histogram is computed from all of the pixels inthe image, and the peaks and valleys in the histogram are used to locate the clusters in theimage.Color or intensity can be used as the measure.

Segmentation by histogram of colors.



SegmentationLevelset methods

Levelset methods: The central idea behind such an approach is to evolve a curve towards thelowest potential of a cost function, where its definition reflects the task to be addressed andimposes certain smoothness constraints. Lagrangian techniques are based on parameterizing thecontour according to some sampling strategy and then evolve each element according to imageand internal terms.

Curve evolution of active contours and region growing method.



SegmentationMethods

Compression-based methods

Edge detection

Split-and-merge methods

Partial differential equation-based methods

Graph partitioning methods

Watershed transformation

Model based segmentation

Multi-scale segmentation

Enough?



SegmentationMethods

The basis for robust and accurate quantification of structural and functional features is thesegmentation of regions of interest (ROIs) which define different elements within the scans.Due to the diversity of possible targets, segmentation strategies need to be highly flexible inorder to define the ROIs for consecutive feature extraction.



SegmentationStatistical learning methods

Statistical learning methods

Clustering

Machine Learning techniques



Outline

1 Motivation



4 Quantification



Machine learning

Machine learning is a scientific discipline concerned with the design and development ofalgorithms that allow computers to evolve behaviors based on empirical data, such as fromsensor data or databases.

The problem of learning can be viewed as a problem of estimating some unknown phenomenonfrom the observed data.

In the literature, several learning algorithms have been propose,

artificial neural networks, supervised.

decision and regression trees,semisupervised.

connectionist networks, unsupervised.

probabilistic networks unsupervised.

and other statistical models,

fuzzy inference systems,

genetic algorithms,

genetic programming,

inductive logic programming,



Machine learningPreliminary Concepts

The sample generator generates the sample S, the supervisor or target operator establishes thestructure and relations that exist in the sample space Z, and the learning machine constructs anapproximation of the supervisor’s operator.

Supervised learning model,h∗: Unknown concept we want to approximate, hs : Approximation of the concept by imitation or

identification.

Unupervised learning model,h∗: Unknown concept we want to approximate, hs : Approximation of the concept by imitation or

identification.




Empirical Risk Minimization To select the best possible hyphotesis an indirect functional mustbe minimized, given l : HxZ −→ <+

0 , <emp(h) = 1n

∑ni=1 l(h; zi )

Structural risk minimization seeks to prevent overfitting by incorporating a regularizationpenalty into the optimization. The regularization penalty can be viewed as implementing a formof Occam’s razor that prefers simpler functions over more complex ones.Generalization Error Is defined as the absolute difference between the observed error rate<emp(hs) and the expected error <(hs) of the hypothesis hS





0 , <emp(h) = 1n

∑ni=1 l(h; zi )






0 , <emp(h) = 1n

∑ni=1 l(h; zi )






0 , <emp(h) = 1n

∑ni=1 l(h; zi )




Machine learningSupport Vector Machine

Support Vector Machine SVMA support vector machine (SVM) is a concept in statistics and computer science for a set ofrelated supervised learning methods that analyze data and recognize patterns. The standardSVM takes a set of input data and predicts, for each given input, which of two possible classescomprises the input, making the SVM a non-probabilistic binary linear classifier. Given a set oftraining examples, each marked as belonging to one of two categories.




Support Vector Machine SVMAn SVM training algorithm builds a model that assigns new examples into one category or theother. An SVM model is a representation of the examples as points in space, mapped so thatthe examples of the separate categories are divided by a clear gap that is as wide as possible.New examples are then mapped into that same space and predicted to belong to a categorybased on which side of the gap they fall on.




Not linear separable problemsThe idea is to gain linearly separation by mapping the data to a higher dimensional space




Kernel trickWhere φ is a function that maps into another space:




Are explicitly based on a theoretical model of learning

Come with theoretical guarantees about their performance

Have a modular design that allows one to separately implement and design theircomponents

Are not affected by local minima

Do not suffer from the curse of dimensionality



Outline

1 Motivation



4 Quantification



QuantificationQuantification of soma cells in IHC images

Quantification of soma cells in IHC imagesThis study explore the possible impact of targeting XBP-1, one of thetranscriptional factorsinvolved in the UPR, in the survival of SNpc under basal andpathological conditions




Parkinson’s disease (PD) is the second most common neurodegenerative disease, affectingat least 1% of the population over 55 years old.

The major clinical symptom of PD is impairment of motor control as a result fromextensive dopaminergic neuron death in the substantia nigra pars compacta (SNpc)

The mechanism involved in dopaminergic neuron loss in PD remains speculative.

Many different molecular mechanisms are proposed to explain the loss of dopaminergicneurons in Parkinson Disease (PD), including oxidative stress and mitochondrial damage.

Increasing evidence from genetic and toxicological models of PD suggest a possibleinvolvement of endoplasmic reticulum stress (ER) and the unfolded protein response (UPR)in disease process.












































Experimental Setup

One of the most frequently used pharmacological PD modelin rodents is the unilateralinjection of 6-hydroxydopamine (6-OHDA) in the striatum(Dauer et al, 2003).

This toxin acts specifically in dopaminergic neurons, inducing aretrograde damage, whicheventually results in cell dysfunction and death (Blum et al,2001).




Experimental Setup






Experimental Setup






Objective

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 ofthe SNpc.

This work propose a ROI segmentation method by use of a supervised statistical learningclassifier, Support Vector Machine (SVM), under this approach ROI’s segmentation requires:

to find an optimal set of features to represent the images in a multiparametric space.

train an SVM model and perform a robust classification.




Objective









Objective









Objective









Color space

The images were transform from de RGB color space to HSV table.

Hue channel : encode the color0 = RGB(1, 0, 0)60 = RGB(1, 1, 0)120 = RGB(0, 1, 0)180 = RGB(0, 1, 1)240 = RGB(0, 0, 1)300 = RGB(1, 0, 1)360 = 0

Saturation: Encode the pureness intensity Distance to the black and white axis

Value: Encode the brightness




Color space








Color space








Color space








Color space








Feature space

Increase the problem dimensionality by convolution with Gabor Wavelets.

Provide a projection basis comparable in some cases to the projection basis obtained byPCA and solving the eigen values problem




Setup SVM

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 segmentationpossible.

Training based on pixels and his projected properties.

92% average accuracy in training.












QuantificationPixels Classification of IHC-stained Images using SVM

Pixels Classification of IHC-stained Images using SVMThis study is focused on digital image processing of human breast tissues, which present aninvasive ductal carcinoma and have been treated using an specific IHC technique that allows todetect the overexpression of HER2 protein (c-erbB-2 oncoprotein). This detection is veryimportant for prognosis of breast cancer and for the patient treatment as well. A high level ofHER2 overexpression implies a poor prognosis and the development of cancer metastasis.




The general objective of this study is to provide support to pathologists and contribute to thedigital IHC image analysis, using Support Vector Machines (SVMs) for pixel image classification.




Feature Extraction.in this study 10 features were extracted in order to provide useful information for theclassification: mean, standard deviation, entropy, dynamic range, Sobel gradient magnitude, Y(from CMYK) channel pixel, Jensen-Shannon divergence (magnitude and orientation),vesselness, and Gabor wavelet.




Jensen Shannon Divergence Texture analisys maps regions where coherent patterns ofintensities are identified. Patterns results from physical properties such as roughness, orientedstrands or reflectance differences such as the color on a surface.

Jensen Shannon Divergence This feature is based on some filters that are used for theenhancement of vessels structures –ducts or a tubes that contains or conveys a body fluid– inorder to grade the stenoses for the diagnosis of the severity of vascular disease.






Questions ?

Rodrigo Rojas [email protected]


Education

Heidelberg imed-machine learning