During the prediction, IKOSA AI applications will assign an ID to each found object listed in the analysis job results. These The IDs not only determine the order of objects in which the instances are listed in the CSV or XLSX output , but they can also help find the instance also aid in locating objects within the visualization.

This page explains the unique aspects of the way instance object numbering works in the visualizations.

Order of the

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objects on small images

The instances objects on small images (smaller than a “tile” , more on this under in the next pointfollowing section) are ordered by a line-dominant scheme. This means the image is read line by line and from left to right when numbering the instancesobjects. The instance’s object’s relevant point is the upper left corner of its bounding box. Simplified, this can be imagined like this:

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Object order on large images

Large images are split within the application into tiles (dashed line on the image below). The numbering on each of those tiles follows a line-dominant pattern (described in the previous section). However, neighboring tiles are numbered in sequence from left to right and top to bottom. This can result in a pattern like the one below.

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The default tile size for IKOSA AI Applications is 2048 x 2048 Pixels. So, if your image is more significant than 2048 Pixels in any direction, expect your instance object numbering to follow this pattern.

Numbers are omitted on small

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objects

When labeling small instancesobjects, at some point, their instance ID hides IDs hide them behind its their index visualization. To avoid this, a visualization threshold for the instance object number exists. Numbers will not be displayed if the bounding box is over 5 times larger than the instance object area.

Limit the number of

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objects to be numbered at all

When predicting instances objects on large images or whole slide images (WSI), the predicted number can exceed what can be reasonably analyzed visually. There is a maximum number of instances objects until which the instance object numbers for all of them will be displayed. The reason for this is that instances objects with high indices would require a large size to display their value.

By default, this number threshold is 100.000 instancesobjects. So, if your prediction contains more than that, no instance object number will be shown in the visualization.

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If you still have questions regarding your application training, feel free to send us an email at support@ikosa.ai. Copy-paste your training ID in the subject line of your email.

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