Application Name	Sparkfinder
Version	1.1.1
Documentation Version	02.01.2023 - 1
Input Image(s)	Multichannel Images (standard and/or WSI); 10 channels; 8 Bit or 16 Bit
Input Parameter(s)	Channel properties channel name channel color channel type (cell nuclei / intensity) for channel type cell nuclei: max. outgrowth of virtual cytoplasm in the range of 0 - 99 pixels. Regions of interest (optional)
Keywords	fluorescence, multichannel, DAPI, Hoechst, immunofluorescence, multiplex, phenotype, microscopy
Short Description	Measurement of channel intensities in images from fluorescence microscopy, including an optional segmentation, extraction of morphometric parameters, and intensity/distance/density measures of cell nuclei (DAPI/Hoechst staining) and corresponding (virtual) cells/cytoplasm.
References / Literature	-

IKOSA Prisma Sparkfinder

You can use this image analysis application or any of our other applications in your account on the IKOSA platform. If it is not on the list of available applications, please contact your organization's administrator or our team at support@ikosa.ai.

Application description

This application automatically measures individual channel intensities of multichannel fluorescence microscopy images. Optionally, cell nuclei stained with DAPI, Hoechst or similar dyes are automatically segmented in one or multiple channels. Intensities of individual channels at the cell nuclei and virtual cytoplasm areas, morphometric features of the objects as well as measures for densities and distances are provided.

In the following, the prerequisites for an accurate analysis are outlined and the output of the application is described.

Input data requirements

Input image(s)

Input for this application is the following image data:

Image type and format	Color channels	Color depth (per channel)	Size (px)	Resolution (μm/px)

Image type and format

Color channels

Color depth (per channel)

Size (px)

Resolution (μm/px)

Multichannel (standard and/or WSI)

Check image format https://kmlvision.atlassian.net/wiki/spaces/KB/pages/3406725124

max.10

8 Bit or 16 Bit

WSI (CZI) formats: arbitrary

Standard images: max. 6,144 x 6,144

Typically: 0.1 -1.1

Image content

Fluorescence microscopy image

Additional requirements

Up to 5 channels can be of type cell nuclei.
Cell nuclei size (diameter) should be in the range of 4-260 pixels.
Cell nuclei size (area) has to be larger than 7 pixels in order to be detected.

Important:

For all images, the following requirements apply:

The illumination must be constant throughout the image(s).
The sample must be in focus, i.e. no blurry regions in image(s).

Input parameter(s)

Required input parameters:

channel name (e.g. “channel-DAPI”)
channel color (e.g. “00FF00”)
channel type (cell nuclei / intensity)
- For channel type cell nuclei, the maximum outgrowth of a virtual cytoplasm can be defined in pixels. Supported value range: 0 to 99 pixels.

As an optional parameter, single or multiple regions of interest (ROIs) can be defined in which the analysis should be performed (‘inclusion ROIs’).

Description of output files and their content

Files

	File format	Description

	File format	Description
1	csv	results.csv A csv file containing the overall analysis results for the input image or all inclusion ROIs.
2	csv	results_cell_nuclei.csv A csv file containing the analysis results for all detected objects (cell nuclei) in the optionally selected channels of the input image or inclusion ROIs. Please note: This file is only created if the type of at least one channel was set to cell nuclei.
3	jpg	results_vis/cell_nuclei_<cc>_<channel-name>_vis.jpg (multichannel; no ROI), or results_vis/cell_nuclei_<cc>_<channel-name>_t<time-step>_z<z-layer>.jpg (for time series, or z-stack; no ROI), or results_vis/cell_nuclei_<cc>_<channel-name>_<roi-id>.jpg (multichannel; ROI <roi-id>), or results_vis/cell_nuclei_<cc>_<channel-name>_t<time-step>_z<z-layer>_<roi-id>.jpg (for time series, or z-stack; ROI <roi-id>) A visualization of the analysis result for each channel <cc> with channel name <channel-name> for which the channel type was set to cell nuclei, for a specific time step (of a time series), or z-layer (of a z-stack), for either the whole image (if no inclusion ROIs selected for analysis) or each individual inclusion ROI. Each visualization includes three parts: input, all channels input image/ROI, channels blended with set channel colors to RGB image instance contours, all channels blended RGB image (same as first part) instance contours of detected cell nuclei and virtual cytoplasm contours calculated by outgrowth from nuclei are indicated by white solid lines instance contours on <channel-name> image showing intensity of channel that was set to type cell nuclei (i.e. used for nuclei instance segmentation) instance contours of detected cell nuclei and virtual cytoplasm contours calculated by outgrowth from nuclei are indicated by white solid lines Input image Results visualization for all channels Results visualization for single channel Reference: FFG-BRIDGE Precision Histology Project, Ao.Univ.Prof. DI Dr. Johannes Schmid and PhD Bernhard Hochreiter, Center for Physiology and Pharmacology, the Institute of Vascular Biology and Thrombosis Research in Vienna, Austria. Please note: These files are only created if the type of at least one channel was set to cell nuclei and qualitative result visualization was requested when submitting the analysis job.
4	json	annotation_results.json: JSON file containing positions of detected nuclei. The position is measured from the left upper corner (1,1) of the image.
5	json	roiMeta.json A json file containing all information regarding the ROIs defined for the analysis job to ensure reproducibility.
6	jpg	rois_visualization.jpg, or t<time-step>_z<z-layer>_rois_visualization.jpg: An overview visualization to show locations of all analyzed ROIs for the time step <time-step> of a time series or z-layer <z-layer> of a z-stack. Please note: This file is only created if inclusion ROIs were defined for analysis.
7	json	jobResultBundleMeta.json A json file containing all information regarding the analysis job (application name and version, project, etc.) to ensure reproducibility. Please note: This file is only included if bundled or merged analysis jobs are downloaded.

Please note:

In the case of inclusion ROIs that are partially outside of the image, the ROIs are cropped to the areas that lie inside the image.
In the case of inclusion ROIs that are completely outside of the image, no analysis is performed. However, they are still listed in corresponding results files.
A <roi-id> is generated automatically by the application corresponding to the creation date of a ROI. The location of a ROI within an image with its specific <roi-id> can be seen in the file “rois_visualization.jpg.” ROIs that are completely outside of the image are not shown in this file.
All visualizations are downscaled to 25 megapixels (MP), if the original image or inclusion ROI is larger than 25 MP.
ROIs or images larger than the application’s underlying tile size (4,096 x 4,096 pixels) can experience inconsistent behavior when objects are located directly on the tile border. These objects are split, affecting the total number of instances found, the individual area values and individual distance values. Output parameters derived from these metrics will naturally be affected. To remedy this we suggest defining ROIs smaller than the tile size where this is possible.

Content

results.csv

Single CSV file.

A row is generated for the whole image or for each inclusion ROI, displaying the total intensity of each channel (columns 41+). In addition, for each channel having its channel type set as cell nuclei, a row including all results with descriptions is provided.

If one or more time steps (of a time series image) or z-layers (of a z-stack image) have been selected, the results in a specific row refer to the time step or z-layer specified in the corresponding column.

If one or more ROIs have been selected, the results in a specific row refer to the ROI specified in the corresponding column. Otherwise , i.e. in cases of empty ROI columns, the results refer to the whole image.

Column NO.	Column name	Examples	Value range	Description

Column NO.	Column name	Examples	Value range	Description
1	t	3	1 -	Time step, i.e. the position of the image in the time series.
2	z	5	1 -	z-layer, i.e. the position of the layer in the z-stack.
3	roi_id	ROI-03	ROI-01 -	<roi-id> starting from “ROI-01”. Empty, if no inclusion ROI is specified and the whole image was analyzed.
4	roi_name	“central”	any text	Custom text to identify the ROI. Empty, if no inclusion ROI is specified and the whole image was analyzed.
5	roi_size [Px^2]	1212212	1 - #pixels	Size of the ROI that was analyzed in pixels^2. The size of the whole image is given if no inclusion ROI is specified and the whole image was analyzed.
6	object_channel_index	2	1 - #channels	Index of selected channel for object detection
7	object_channel_name	“my_channel”	any text	Name of selected channel for object detection
8	object_type	“cell nuclei”	“cell nuclei”, or “image/ROI”	Type of “object” analysis (basis for intensity results calculation) . Can either be “cell nuclei” or “image/ROI”.
9	outgrowth_type	“virtual cytoplasm”	“virtual cytoplasm”, or ““ (empty)	If an outgrowth is defined, it shows the outgrowth type.
10	object_max_outgrowth [Px]	2	0 - 99	Maximum outgrowth in pixels.
11	total_num_objects	788	1 -	Total number of objects which are detected in the image/ROI.
12	density_num_objects [1/Px^2]	0.0007514953	0 - 1	Total number of objects divided by area of image/ROI.
13	total_area_objects [Px^2]	101863	8 - #pixels	Total area of objects which are detected in the image/ROI.
14	density_area_objects	0.0971441268	0 - 1	Total area of objects divided by area of image/ROI.
15	total_area_outgrowth [Px^2]	125326	0 - #pixels	Total area of object’s outgrowth which are detected in the image/ROI.
16	density_area_outgrowth	0.11952018737	0 - 1	Total area of outgrowth divided by area of image/ROI.
17	total_area_objects_incl_outgrowth [Px^2]	227189	8 - #pixels	Total area of objects including outgrowth which are detected in the image/ROI.
18	density_area_objects_incl_outgrowth	0.2166643142	0 - 1	Total area of objects including outgrowth divided by area of image/ROI
19	mean_area_objects [Px^2]	14.324523	8 - #pixels	Mean area of detected objects in Pixels^2.
20	stddev_area_objects [Px^2]	1.213133	0 - #pixels	Standard deviation of area of detected objects in Pixels^2.
21	median_area_objects [Px^2]	13.983252	8 - # pixels	Median area of detected objects in Pixels^2.
22	mean_area_outgrowth [Px^2]	21.363824	0 - #pixels	Mean area of outgrowth in Pixels^2.
23	stddev_area_outgrowth [Px^2]	2.573912	0 - #pixels	Standard deviation of areas of outgrowth in Pixels^2.
24	median_area_outgrowth [Px^2]	20.874623	0 - #pixels	Median area of outgrowth in Pixels^2.
25	mean_area_objects_incl_outgrowth [Px^2]	35.836274	8 - #pixels	Mean area of detected objects including outgrowth in Pixels^2.
26	stddev_area_objects_incl_outgrowth [Px^2]	2.309003	0 - #pixels	Standard deviation of area of detected objects including outgrowth in Pixels^2.
27	median_area_objects_incl_outgrowth [Px^2]	35.473222	8 - #pixels	Median area of detected objects including outgrowth in Pixels^2.
28	mean_distance [Px]	13.5016475693	0 -	Mean distance (center-to-center) between detected objects in Pixels. The distance is calculated by the nearest-neighbor distance to other objects of the same type.
29	stddev_distance [Px]	5.5485792042	0 -	Standard deviation of distances between detected objects in Pixels. The distance is calculated by the nearest-neighbor distance to other objects of the same type.
30	median_distance [Px]	12.529964086	0 -	Median distance between detected objects in Pixels. The distance is calculated by the nearest-neighbor distance to other objects of the same type.
31	mean_bbox_area [Px^2]	202.03426395	8 -	Mean area of bounding box of detected objects in Pixels^2.
32	mean_area_ratio [%]*	0.0124492385	0 - 100	Mean area of detected object as percentage of overall image area or ROI area inside the image.
33	mean_perimeter [Px]*	40.434010152	8 -	Mean perimeter of detected objects in Pixels.
34	mean_circularity*	0.92114213197	0 - ~1	Mean circularity factor of detected objects. Circularity = 4piarea/(perimeter^2). The circularity of a circle is 1. Note: Detected objects with a circularity of infinity are ignored when calculating mean_circularity.
35	mean_solidity*	0.9310659898	0 - 1	Mean solidity value of all detected objects. Solidity is calculated out of the ratio of an object’s area to the area of its convex hull.

1.1.1 Sparkfinder Application Documentation

Table of contents