When developing a DL-based segmentation model, the ultimate goal is often to achieve robust segmentation of multiple object classes across various imaging conditions. However, if the parameter space is large and you only provide a few annotated objects, it might be challenging for the algorithm to learn all the complexity at once. To mitigate this challenge, we recommend the following this step-by-step approach:

1. Start by annotating a single class
2. Aim for annotationg approximately 50 objects or regions in similar images
3. After training, inspect the results to evaluate the accuracy of segmentation
4. To improve the accuracy of the algorithm, consider adding images with more variability to your dataset and repeating the earlier steps
5. Once you are satisfied with the performance of the previous class, start annotating for new classes by repeating the earlier steps for each of them

The instructions provided here will not only assist your algorithm in effectively learning the task from the annotated dataset used for training, but they will also aid in creating a segmentation model that generalizes well and performs effectively on data acquired from future standardized experiments.

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