Optimize the effectiveness of the Region of Interest (ROI(s)) feature in image analysis and algorithm application training.
Using ROI(s) allows you to define an area of an image in your Prisma image analysis or your IKOSA AI training for further analysis or processing. This enables you to analyze solely parts ROI(s) of an image or to reduce the annotation effort, by annotating only parts ROI(s) of an image.
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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
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application 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 an ROI(s).
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Reducing the input data by using an ROI means that the training algorithm applications 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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Whether you use ROI(s) or not, the greater amount of non-annotated background area available for algorithm application training, the better the resulting model will be.
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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 an ROI the trained algorithm model 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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Now you know the most important facts on drawing regions of interest and applying them in image analysis and application training!
Share this information with your team to optimize your app training and image analysis.
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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).
📑 FAQ
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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. |
Now you know the most important facts on drawing regions of interest and applying them in image analysis and algorithm training!
Share this information with your team to optimize your algorithm training and image analysis.
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