Annotating Images with CellProfiler and GIMP

Annotated image data is valuable for assessing the performance of an image processing pipeline and as training data for machine learning methods such as deep learning. When assessing the performance of a CellProfiler pipeline, for example a pipeline that segments nuclei, the annotated image data are used as the ground truth. The performance of the pipeline can be quantified by comparing the segmentation output to the ground truth and calculating a comparison metric, such as the Jaccard Index or F1 Score. Annotated images are also essential for deep learning applications as training data, for example see the 2018 Data Science Bowl; an in-depth discussion on how the Data Science Bowl images were annotated can be found on the Kaggle forum. Continue reading