Image Analysis 2 with Fiji

Ahmed E. Fetit, PhD4 May 2016

1Advanced Imaging Resource.

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1. Quantitation Extracting numerical features out of your image

2. Co-localisation Spatial overlap between 2 labels

Why we are here - Quantification

SegmentationIdentify regions of interest

IdentifyMeasurements of interest

Identify Object of interest 1

Create a spatial mask

Identify objects of interest 2

Programme

• Recap – Analysis using ImageJ(Fiji)• Brightness & Contrast• Thresholding• Measuring • Analyse particles• Making selections

• Example use-cases• Quantify actin signal• Quantify nuclei of interest• Quantify foci• Compare nuclei to cytoplasm• Counting colonies

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• All settings need to be the same. Compare apples with apples.

• Sample preparation needs to be the same

• Microscope control needs to be the same in each. (*) Definitely for expression levels.

Before you start

igmm-imaginghelpdesk@igmm.ed.ac.uk

Recap – Analysis using Fiji

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Bit-depth and dynamic range.

• Computer monitor is 8 bit -> Better visualisation.• Do conversion at the very end for publishing, etc.

256

65,536

Bit-depth and dynamic range.

Brightness and contrast.

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Thresholding.

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Measure thresholded regions.

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1. Set measurements 2. Measure

Analyse particles.

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Analyse Particles - Options

• Add to manager – Displays ROI manager with references to ROIs

• Summarise – Overall summary of measurements

• Display results – Measurement for each ROI

• Show options:

Overlay

Selections.

13Delete Edit -> Selection -> Select None.

Measure thresholded regionswithin selection.

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Example use-cases

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• Image -> cropDraw square

• Image• Colour • Split Channels

• Image• Adjust• Threshold

• Analyse -> Set measurement• Analyse -> Measure

• Quantify my actin (actin AF488) in a specific part of the eye (cornea)? • What do I tell ImageJ – Quantify green signal in user defined ROI?

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• Quantify nuclei using DAPI in a epicardium• What do I tell ImageJ – area of blue signal in specific ROI

• Image• Duplicate

• Image• Color• Split channel

• Draw polygon on original coloured image

• Edit• Selection• Restore selection

• Image• Adjust• Threshold

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Summary 1

Identify channel of interest – DAPI?Identify region of interest/ mask – use same or different channel to define it?Identify what needs to be quantified – area? Number of nuclei? Signal intensity?

Demo showed how ImageJ/Fiji can be used semi-automatically

For automating can use• ImageJ macros• Definiens large-scale

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• Quantify number of foci in nuclei (Colocalisation)• Quantify objects, identify channel, find maxima

• Image• Color• Split Channels

Select blue• Image• Adjust• Threshold

• Analyse• Particles(add to manager)

Select green• In ROI manager -> Show all

• Process• Find maxima

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Summary 2

Identify channel of interest – DAPI?Identify region of interest/ mask – use same or different channel to define it?Identify what needs to be quantified- FociWhat distinguishes Foci – in this case bright signal intensity?

Demo showed how ImageJ/Fiji can be used semi-automatically

For automating can use• ImageJ macros

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Quantify cytoplasm vs. nuclei

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Counting colonies (RGB image)

• Image• Color• Split channels

• Process• Binary• Make binary

• Place oval ROI• Process, Binary, Watershed

• Analyse Particles (100-2000 pixels)

Show: outlines

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Summary 3

RGB image – channel of interest is not so importantSegmenting overlapping objects is more importantIntensity can’t be used for segmentation

Demo showed how ImageJ/Fiji can be used semi-automatically

For automating can use• ImageJ macros

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Conclusion

• Identify clear aim for image analysis experiment

• Break down problem to manageable tasks: Identify channels, segmentation, identify objects

• Covered a number of scenarios:• Quantify actin signal• Quantify nuclei of interest• Quantify foci• Compare nuclei to cytoplasm• Counting colonies