Amounts of labels are well-balanced between all classes.Borders between classes are clearly defined.Regions that are hard to label are more important than regions that are easy to label. Large homogeneous regions, like the background being far away from cell structures can be learned with very few labels. For laminin-stained muscle sections, we found that the Global strategy of thresholding, which calculates a single threshold value based on the unmasked pixels of the input image and use that value to classify pixels above the threshold as foreground and below as background, and the RobustBackground method for finding thresholds. He focussed on heterogeneous regions, such as small cavities and tiny.Note the following about the labeling strategy: He defined 3 classes: background (red), neurite (green) and nucleusīelow you see one example of the training images. YAPiC reads label data directly from ilastik project files (ilp). If you were previously letting CellProfiler calculate the value, you should check the adaptive window size as the current default value (10) in most cases will be significantly different. (only single time frames) from different samples by using Adaptive thresholding no longer has an option to let CellProfiler guess the adaptive window size based on the image size it must now be set explicitly. A few words on training data collectionįor training the classifier, Max manually labeled approx. Or by using the AnalyzeSkeleton module ofĬellProfiler. To measure the actual length of neurites, an object detection should beĪpplied. Output class (red: cell soma, green: neurite). Look at the two example movies below: On the left, you see the input data. The model is quite robust and can be applied to cells of different morphology and varying imaging conditions. He trained a model with YAPiC using unet_2d for detection of To quantify outgrowth in primary neuronal cells, Max acquired various time This project was executed by Max Schelski from Frank Bradke’s Lab at DZNE Bonn. Detection of neurites and cell soma in phase contrast time lapse movies Yet Another Pixel Classifier (based on deep learning) View on GitHub Detection of neurites and cell soma in phase contrast time lapse movies Detection of neurites and cell soma in phase contrast time lapse movies | YAPiC Skip to the content.
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