Optimize the effectiveness of the Region of Interest (ROI(s)) feature in image analysis and algorithm training.
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📑 How to draw ROI(s)?
Draw ROI shapes
Switch to “Region of Interest drawing mode” 
in the Image Editor from the right-hand toolbar.
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By selecting one of these tools you can draw your ROI on the image by clicking and dragging your mouse cursor.
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Modify ROI shapes
Previously drawn ROI shapes can be modified:
Click the “Edit“
tool in the upper part of the right-hand toolbar.Select a given ROI path - its editable anchor points appear as white squares.
Click and drag the anchor pointsto adjust the shape.
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Delete ROI
Option 1:
Select
aan ROI you wish to be removed.Click the “Trash-bin”
tool in the upper part of the right-hand toolbar. You will be asked to confirm this action.
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Click the “Trash-bin”
tool in the upper part of the right-hand toolbar.Click a an ROI you wish to be removed. You will be asked to confirm this action.
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Important: When choosing a ROI to delete, make sure the borders of the ROI are bold. Otherwise you might be deleting an annotation under the ROI. The “delete” window will inform you, if the selected object was an annotation or a ROI. |
📑 How to draw ROI(s) for image analysis with IKOSA Prisma?
To restrict your image analysis to parts of the image, you can draw multiple ROI(s) within an image. IKOSA Prisma applications with ROI features will calculate results for each of the ROI(s) separately.
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Important: On multi-dimensional images (e.g. time-series) ROI(s) can be drawn on individual time lapses. For those time lapses with ROI(s), only ROI areas will be analyzed. For time lapses without ROI(s), the whole image will be analyzed. |
📑 How to draw ROI(s) for algorithm training with IKOSA AI?
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If only part of your image contains useful objects and annotations or annotating the whole image would be too much effort, you can reduce the area of your image that is actively used in training and validation by defining a an ROI(s).
Issue
Reducing the input data by using a an ROI means that the training algorithm will have less information to work with than in cases where the entire image has been selected and annotated. In addition, when using ROI(s) you might unintentionally exclude important aspects of your images: e.g. you may only select regions showing the objects, but only very few regions showing background.
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In general, when using ROI(s) within training (and validation-) images the same rules apply as for a whole image. Make sure that all objects within your ROI(s) are annotated. This also applies to the edges of your ROI(s), where also objects which are only partially inside the boundary should be annotated. When defining a an ROI the trained algorithm does not register the areas outside the ROI, so annotations - or their absence - do not matter there.
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Tips on how to draw ROI(s) to achieve high-quality training
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📑 FAQ
Question | Answer |
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Is it a problem when a ROI is smaller than the objects inside of it? | It is not a problem for IKOSA AI, if your ROI(s) exclude parts of objects, e.g., at borders of ROI(s). However, it is important for high quality training, that ROI(s) are defined precisely and include all the necessary areas. For example, regions around your objects are also important for the training. |
Do the shape and number of ROI(s) affect the training? | No, they do not affect the training. However, it is vital that all areas of interest are annotated appropriately. |
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If you have any questions, please send us an email at support@ikosa.ai (copy-paste your training ID in your email if the question is related to your training).
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