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usage:division_detection [2019/11/15 18:35] – [Division detection] pseudomoaner | usage:division_detection [2023/07/05 15:36] (current) – [The Division Detection module] pseudomoaner | ||
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Despite these differences, | Despite these differences, | ||
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===== Model training ===== | ===== Model training ===== | ||
Training the division detection module is very similar to training the tracking module. The processes of feature choice and training link inclusion proportion remain the same as before, although the relatively low number of division events compared to object-object links means that the histogram used to inform the choice of the training link inclusion proportion may not be very informative. One major difference does exist between the modules however: because temporal information is included as a feature, all ' | Training the division detection module is very similar to training the tracking module. The processes of feature choice and training link inclusion proportion remain the same as before, although the relatively low number of division events compared to object-object links means that the histogram used to inform the choice of the training link inclusion proportion may not be very informative. One major difference does exist between the modules however: because temporal information is included as a feature, all ' | ||
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<note tip> | <note tip> | ||
Because there are no other internal values to compare it to, $R$ is not very useful for determining the ease of division assignment. Instead, it is recommended that the accuracy of division detection be assessed following its completion using the **View divisions** panel and the [[usage: | Because there are no other internal values to compare it to, $R$ is not very useful for determining the ease of division assignment. Instead, it is recommended that the accuracy of division detection be assessed following its completion using the **View divisions** panel and the [[usage: | ||
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+ | <note tip> | ||
+ | Division events are much sparser than ordinary frame-frame links between objects, and typically need less feature information for assignment. Prediction of the location of daughter cells in feature space is also more noisy than the prediction of a single object' | ||
</ | </ | ||
Model training is initialised as before, by clicking the **Calculate!** button. Once it has completed, division detection proper becomes available. | Model training is initialised as before, by clicking the **Calculate!** button. Once it has completed, division detection proper becomes available. | ||
- | {{ : | + | ===== Division detection ===== |
- | Selection of the **division threshold** is also very similar | + | In contrast |
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- | To perform initial division detection, simply click the **Find divisions!** button. One of two outcomes will then occur: | + | |
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- | - If division detection completed successfully, | + | |
- | - If division detection resulted in a lineage with a cycle (i.e. a cell marked as its own ancestor), the following warning notice will appear | + | |
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- | In the case of scenario (2), the **division threshold** should be reduced until the warning notice ceases to appear. | + | |
+ | Once training has completed, it is therefore sufficient to simply press the **Find divisions!** button to launch this iterative algorithm and generate your lineage. | ||
==== Validation ==== | ==== Validation ==== | ||
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===== Video demonstration ===== | ===== Video demonstration ===== | ||
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