QMic F1 20121009 ut - cb.uu.secarolina/QMicht2012/QMic_F1_20121009.pdf20121009 & 3...

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PhD-‐level course in Quan6ta6ve Microscopy

In collabora6on between the Centre for Image Analysis, SciLifeLab and BioVis October-‐December 2012

Introduc6on and overview 2012 10 09

Carolina Wählby Associate professor in Quan6ta6ve Microscopy at SciLifeLab/the Centre for Image Analysis, Uppsala University

and Principal Inves6gator, the Imaging PlaNorm of the Broad Ins6tute of Harvard and MIT, Cambridge; MA

Carolina Wählby, [email protected]

PreTy pictures, or measurements? Fundamental steps when extracting information from image data

Image acquisition

knowledge about the applica6on


Preprocessing, filtering

Making measurements, feature extraction

LENGTH, WHIDTH, CURVATURE, TEXTURE…

Object classification, interpretation, recognition

Result WORMS ARE ALIVE

Object detection, segmentation (including 3D and tracking over time)

Course ‘themes’

A beTer communica6on between experts of from different fields can greatly improve the scien6fic value of an experiment.


Knowledge about the image forma6on, possibili6es and limita6ons, can greatly improve the scien6fic value of an experiment.

A beTer understanding of digital image processing, possibili6es and limita6ons, can greatly improve the scien6fic value of an experiment.

Today:

•  Some basic concepts in digital image processing and analysis and quan6ta6ve microscopy.

•  An overview of the course. •  Introduc6on of course par6cipants. •  Ini6al discussion of projects.

Carolina Wählby, [email protected] Carolina Wählby, [email protected]

Pixels and image dimensions

2-‐dimensional (2D) gray scale image.

f(x,y)= f(1,1) f(1,2) f(1,3)…. f(2,1) f(2,2) f(2,3)… …

where f(1,1)=17, f(1,2)=16, f(1,3)=16 etc

The value (or intensity) of a pixel is a func6on of the posi6on (x,y).

f(1,1)

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Resolu6on -‐a word with many defini6ons

Resolu6on of a screen: the number of pixels that fit in the x-‐ and y-‐dimension. It is also common to say that the resolu6on of an image is its size in pixels (NOT correct defini6on during this course).


Resolu6on of an image is the shortest distance (in μm) between two points on a specimen (observed as Airy disks) that can s6ll be dis6nguished (in the digital image) as separate en66es.

Illustra6on borrowed from hTp://www.microscopyu.com

Pixel size is the distance in the specimen (in μm) corresponding to the distance between two pixels in the digital image. Note that the pixel size of the digital camera has to be small enough not to limit the resolu6on of the op6cal system. However, a smaller pixel size can not increase the resolu6on beyond the resolu6on limit of the op6cal system. Carolina Wählby, [email protected]

A 2D digital image with 3 color channels

=

Red channel, λ=1 Green channel, λ=2 Blue channel, λ=3

f(x,y,λ)= f(1,1,1) f(1,2,1) f(1,3,1)…. f(2,1,1) f(2,2,1) f(2,3,1)… …

where f(1,1,1)=17, f(1,2,1)=16, f(1,3,1)=16 while f(1,1,2)=0, f(1,2,2)=0 etc

The value (or intensity) of a pixel is a func6on of the posi6on x,y and spectral channel λ.

Spectral informa6on: color images. Densitometric and spectral proper6es

•  What physical property of the specimen is translated to image intensity, and how precise is this transla6on?

•  How can sample prepara6on affect the densitometric proper6es, and what are the sources of variability and noise?

•  Is it possible to op6mize signal/noise proper6es by adjus6ng exposure 6me or similar, and what are the drawbacks?

•  Can image data be normalized or the system be calibrated? •  What are the spectral proper6es of the imaging system (if any)? •  What physical proper6es can be quan6fied using the spectral

informa6on, and what are the sources of varia6on (sample treatment, exposure 6mes, temperature etc).



3-‐dimensional (3D) images.

3D informa6on can be collected using different focal depths (e.g. confocal and two-‐photon microscopy) or a tomographic imaging system. The intensity is now a func6on f(x,y,z,λ). Note that the resolu6on in the z-‐dimension is rarely the same as the resolu6on in x-‐ and y. A 3D pixel is onen called a voxel.

Geometric proper6es

•  How are the three spa6al dimensions of the real world transferred to the dimensions of the digital image?

•  Is it a projec6on, a slice through the object or a surface?

•  How exact is the imaging? •  Can geometric proper6es of the imaged object be distorted affec6ng quan6ta6ve measurements?

•  What resolu6on can be achieved, and what physical proper6es limit the resolu6on?

•  Is the resolu6on the same in all spa6al dimensions?


Time-‐lapse images (movies) t=0 t=1 t=2

If we follow dynamic events, we add a 6me dimension: f(x,y,z,λ,t)

Example: 6me series (movie) of dividing cell. Spa6al informa6on: x,y,z (z always=1). Spectral informa6on: brighNield + fluorescent channel Temporal informa6on: images captured every 10 minutes (temporal resolu6on = 10 minutes)

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Temporal proper6es

•  Is it possible to image live samples, and what are the limita6ons when it comes to sample prepara6on and handling?

•  What is the temporal resolu6on in rela6on to signal and noise?


image forma6on

A digital image is characterized by •  geometric proper6es and resolu6ons (x,y,z) •  densitometric and spectral proper6es and resolu6ons (λ)

•  temporal proper6es and resolu6ons (t) of the imaging system. An image is also characterized by the prepara6on of the imaged sample.


Lectures on image forma6on, possibili6es and limita6ons

•  Bright field microscopy, slide scanners and staining techniques. Anna Asplund, IGP, UU. •  Fluorescence, confocal and two-‐photon microscopy. Fluorescence labeling and staining.

Dirk Pacholsky, BioVis, IGP, UU. •  Spectral aspects in microscopy; the rela6on between illumina6on, absorp6on, emission

and reflec6on in rela6on to imaging sensors features. Ewert Bengtsson, CBA, UU. •  Live cell imaging. Göran Månsson, CLICK facility, KI. •  Transmission Electron Microscopy (TEM) and biological sample prepara6on. Anders

Ahlander, BioVis, UU. •  Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM) and sample

prepara6on in material science. Åsa Kassman Rudolphpi, Dept. Eng. Science, UU. •  S6mulated emission deple6on microscopy (STED). Daniel Rönnlund, Exp. Biomolecular

Physics, KTH. •  X-‐ray microtomography and op6cal tomography. Alexandra Pacureanu, CBA, UU. •  High-‐throughput imaging systems and quan6ta6ve microscopy in pharma-‐industry. Alan

Sabirsh, AstraZeneca.


fundamental steps in image processing and analysis

Image acquisition

knowledge about the applica6on


Preprocessing, filtering

Making measurements, feature extraction

LENGTH, WHIDTH, CURVATURE, TEXTURE…

Object classification, interpretation, recognition

Result WORMS ARE ALIVE

Object detection, segmentation (including 3D and tracking over time)

Carolina Wählby, [email protected] Carolina Wählby, [email protected]

Image segmenta6on -‐to par66on an image into regions of interest

2-‐dimensional (2D) gray scale image.

Thresholding: define an object as connected pixels brighter than a fixed intensity threshold f(x,y)>T

T=20


Image segmenta6on -‐to par66on an image into regions of interest (ROI)

What is the best intensity threshold value for dividing the intensity histogram into foreground and background pixels?

Here?

Or here?

Pixel intensity

Freq

uency

raw input image

binary image: 0=background 1=objects

labeled objects: each connected component is a ROI, represented by a random color.

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Clustered and overlapping objects may be further separated using more advanced segmenta6on methods that make use of more informa6on, such as shape, edges and intensity distribu6ons within the regions of interest.

Input: segmented blobs (from red) segmented nuclei (from blue)

Example: Coun6ng ‘blobs’ per cell by using the segmenta6on result from one image channel to extract informa6on from another channel.

Outline of ‘cytoplasms’ blobs assigned to ‘cytoplasm’

summary: Image segmenta6on Segmenta6on is onen the most difficult problem to solve in image analysis. No universal solu6on exist, and this is a large and ac6ve field of research. The result of an analysis task relies heavily on the robustness of the image segmenta6on (including 3D segmenta6on and tracking). The problem can be simplified by choice/op6mizing the sample prepara6on and seyngs of the imaging system •  choice of sensor •  Illumina6on •  background


Once the image is segmented and the ROIs have been identified: Making measurements; feature extraction

•  Topological features •  Size and shape features

–  Based on a binary mask of object/objects •  Intensity features

–  Based on histogram of object/objects and color/multiple fluorescent channels

•  Texture features –  Statistics and spatial gray scale variations

•  Relational/structural features –  How objects (parts of objects) relate to one another

•  Counts –  How many sub-object (dots) per main object

Note that image segmentation may not always be necessary: features can also be measured on a per-image basis.


Object classification, interpretation and recognition


Example: Define nuclei by intensity thresholding of blue image channel. Define a cytoplasms as a region surrounding a nucleus at a fixed distance. x1=mean pixel intensity in green channel within cytoplasm. x2=mean pixel intensity in green channel within nucleus.

Using the measured features, objects can be grouped or iden6fied as belonging to a par6cular class or recognized as having a given phenotype.

x1 and x2 are ‘features’, or measurements made on the objects. Each object is represented by a symbol in ‘feature space’.


Example: a simple adjustment of the microscope parameters (focus) made cell segmenta6on much easier to automate, though the visual difference may not be so large between the two images.

Lectures on digital image processing and analysis

•  Extrac6ng quan6ta6ve informa6on from microscopy data. Cris Luengo and Ida-‐Maria Sintorn, CBA, UU/SLU.

•  Introduc6on to sonware for digital image processing focused on quan6ta6ve microscopy. Carolina Wählby CBA

•  Independent work on projects in pairs or groups of 3, with individually scheduled guidance/discussions with Carolina Wählby, CBA, UU.

•  Oral presenta6on and discussion of projects.


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What are the colors of the cells?

Why don’t we just look at the images?

Same brightness…

A B Which nucleus is brighter?

A B

Which nucleus is larger?

Same size…

Same color…

Why quan6ta6ve microscopy? •  Reproducible measurements: by sharing our ‘analysis

pipeline’ anyone can reproduce the results on our image data. •  Measurements from large volumes of image data can be

automated, leyng us focus on more challenging problems. •  Measurements from larger numbers of images will increase

the significance of our results. •  The measurements will not be biased by the observer.


-‐With the aid of informa=cs, microscopy is in the midst of a crucial evolu=on into a more quan=ta=ve and powerful technique.

Daniel Evanko, editor of Nature Methods, June 2012

Introduc6on of course par6cipants


Johanna Näslund <[email protected]>


Sultana Jahan <[email protected]>


Diana Telessemian <[email protected]>


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Omer Ishaq <[email protected]>


Arash Sanamrad <[email protected]>


Kris6na Lidayová <[email protected]>


Yinghua Zha <[email protected]>


Amir Motevakel <[email protected]>


Yu Zhang <[email protected]>


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Amol Bhandage <[email protected]>


Haixia Liu <[email protected]>


Benjamin Holmgren <[email protected]>


Amanuel Abraha <[email protected]>


Azadeh Fakhrzadeh <[email protected]>


Ellinor Spörndly-‐Nees <Ellinor.Sporndly-‐[email protected]>


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Gustaf Kylberg <[email protected]>


Beyna Ryl <[email protected]>


Anna Åsman <[email protected]>


Sanna Hede <[email protected]>


Rui Miao <[email protected]>


Hanneke Marjolijn Peele <[email protected]>


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Andreas Kårsnäs <[email protected]>

Carolina Wählby <[email protected]>

MSc in Molecular Biotechnology at UU, thesis at CMB, KI: Using GFP constructs to study PML-‐bodies and protein targe6ng

Cell detec6on and quan6fica6on of ruffling at Rac1-‐ac6va6on: a consultancy study sponsored by Amersham Biosciences for the first version of INCell Analyser.

3h post plating 96h post plating Tacking of unstained stem cells in collabora6on with Chalmers and Department of Clinical Neuroscience, Göteborg University and wavelet-‐based cell classifica6on for Histogenics. PostDoc, Dept. Gene6cs and Pathology, UU. Detec6on and classifica6on

of signals from different variants of padlock and proximity probes in cells, 6ssue and on glass surfaces.

wt mutant

PhD in digital image analysis at the Centre for Image Analysis, UU: Collabora6on with CCK, KI on quan6fica6on of cyclin expression in normal vs cancer 6ssue.

Associate Professor (docent) in Digital Image Processing 2009

Principal Inves6gator, Imaging PlaNorm, Broad Ins6tute of Harvard and MIT (since 2009)

Associate Professor (lektor) in Quan6ta6ve Microscopy, SciLifeLab & Centre for Image Analysis, Uppsala University

Projects •  Op6onal 1-‐5p; the number of points will be decided jointly aner

project presenta6on. •  Work in small groups, preferably partners with different

backgrounds, with the aim to act as a ‘teacher’ for your project partner(s).

•  Access to a desired imaging system will be organized if needed. •  Get started discussing/working on projects in parallel with course

work – projects may be closely related to your current research. •  November 13-‐December 10 Independent work on projects, with

individually scheduled guidance/discussions with Carolina. •  Tuesday December 11

10-‐15 Oral presenta6on of projects, approximately 20 min/group, and handing in of wriTen reports (template will be provided).


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