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Comment: Updating for Aivia 12

The Exocytosis Detection recipe in Aivia detects exocytotic events in total internal reflection fluorescent (TIRF) microscopy images. The

This recipe provides an option for tracking vesicles associated with exocytosis . The recipe and is tunable for the detection of small, point exocytosis events (e.g., kiss-and-run) as well as large, diffuse events (e.g., full fusion).

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On this page:

Table of Contents
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Parameters and Presets

Parameters

Recipe parameters for Exocytosis Detection and their descriptions are summarized in the table below.

Preset Group

Parameter Name

Min Value

Max Value

Description

Vesicle Detection

Vesicle Threshold

(Vesicle Tracking only)

0

1

255

Adjusts the

detection

sensitivity for vesicle detection; a lower value will detect more vesicles

Min Vesicle Size

(Vesicle Tracking only)

0

100 px2 or µm2

Specifies the minimum size of a detected object to be included in the analysis results based on the

area (in pixels)

areas of the detected objects

Event Detection

Event Intensity Change

0

255 (8-bit)

65,535 (16-bit)

Adjusts the

detection

sensitivity for event detection based on the

amount

size of the intensity change (in number of gray levels) at the event from baseline; a lower value will detect more events

Transience

0

100

Specifies the range of duration for the events by computing a normalized ratio for the difference between the maximum and average intensities to the difference between the average and minimum intensities; a lower value will enable detection of events with longer durations

Bright Detection Frame

(Enable Bright Detection only)

0

255 (8-bit)

65,535 (16-bit)

Specifies the maximum number of frames that an event with extended intensity increase in its surrounding regions may last; this parameter is used only when Enable Bright Detection is selected

Duplicate Events Removal

Not applicable

Toggles the removal of detected events that

occurs

occur in a given region in successive time frames

Event Filter

Event Size Filter

0

1,000 px2 or µm2

Specifies the minimum size of a detected event to be included in the analysis results based on the area of the detected event

Event Mean Intensity Filter

0

255 (8-bit)

65,535 (16-bit)

Specifies the minimum threshold for mean intensity of

the

a detected event region to be included in the analysis results

Event Total Intensity Filter

(Enable Event Total Intensity Filter only)

0

1 x 1018

Specifies the minimum threshold for the total intensity of

the

a detected event region to be included in the analysis results

Presets

There are three preset groups in the recipe: Vesicle Detection, Event Detection and Event Filter

; each

. Each group has three pre-configured parameter groupings to help you get started on the analysis. The default preset values are as follows:

Vesicle Detection

Parameter Name

LowDim

Medium

HighBright

Vesicle Threshold

15

25

40

Min Vesicle Size

0.05 px2 or µm2

0.05 px2 or µm2

0.05 px2 or µm2


Event Detection

Parameter Name

Gross

Short

Long

Event Intensity Change

8 (8-bit)
1,966 (16-bit)

8 (8-bit)
1,966 (16-bit)

8 (8-bit)
1,966 (16-bit)

Transience

0 - 2040

0 - 2040

0 - 4020

Bright Detection Frame

0

2

5

Duplicate Events Removal

Enable Duplicate Events Removal


Event Filter

Parameter Name

SmallSm-Bright

Medium-Medium

Large-Dim

Event Size Filter

0.05 px2 or µm2

0.15 px2 or µm2

0.5 px2 or µm2

Event Mean Intensity Filter

19 (8-bit)
4,981 (16-bit)

12 (8-bit)
3,002 (16-bit)

5 (8-bit)
1,311 (16-bit)

Event Total Intensity Filter

15,000

45,000

50,000

Measurements

The Exocytosis Detection recipe generates intensity measurements for each detected event and, if Vesicle Tracking is enabled, for each detected particle plus track measurements for the particles. You can add additional measurements to the analysis results by using the Measurement Tool in Aivia. The measurements generated by the recipe are as follows.

Measurement Type

Measurement Name

Events

Vesicle Tracks

Morphology

Area

x

Intensity

Mean

x

x

Total

x

x

Track

Total Time

x

First Frame

x

Last Frame

x

X

x

Y

x

Velocity Magnitude (instantaneous)

x

Miscellaneous

Additional measurements

  • Total Events Detected

  • First Event Frame

  • Has Relation (boolean)

  • Events Detected on Track (boolean)

  • Event on Frame (instaneous, boolean)

  • Total Events in Image (Summary)


Tutorial

Before beginning the tutorial, please download the Exocytosis Detection Demo image. For information on how to select presets or modify parameter values, please refer to the tutorial on how to use the Recipe Console.

  1. Unzip the demo file and load the demo image,

ExocytosisDemo

  1. “ExocytosisDemo.tif,” into Aivia.

  2. In the Recipe Console, click on the Recipe selection dropdown menu and select the Exocytosis Detection recipe.

  3. Select the following

preset

  1. presets for each preset group:

    • Vesicle Detection: Medium

    • Event Detection: Gross

    • Event Filter: Large-Dim

  2. Click on the caret to the left of the Event Detection preset group to show the preset parameters.

  3. Modify the parameter value as follows, leaving the other parameters intact:

    • Event Intensity Change (counts): 1,000

  4. Click the Start button or press the F4 key on your keyboard to begin applying the recipe to the image.

The detected particle tracks and

outline

outlines for the events will be overlaid on the image.

Results

ExocytosisDemo

_output.mp4
Image credits

_12_0_0.avi

Measurements

The Exocytosis Detection recipe generates intensity and position measurements for each detected event and, if Vesicle Tracking is enabled, for each detected particle in addition to morphology and advanced count measurements for the particles. You can add additional measurements to the analysis results with the Measurement Tool in Aivia and view measurement definitions on the Measurement Definitions page. The measurements generated by the recipe are given in the table below.

Object Set

Morphology

Intensity

Position

Advanced

Events

None

  • Pixel-based Mean Intensity

  • Pixel-based Total Intensity

  • Centroid X

  • Centroid Y

None

Vesicle Tracks

  • Area

  • Pixel-based Mean Intensity

  • Pixel-based Total Intensity

  • Total Time

  • First Frame

  • Last Frame

  • Velocity Magnitude

  • Event on Frame

  • Total Events

Image Credits

Takashi Tsuboi, University of Tokyo, Tokyo, Japan

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