Example dataset MiNTiF semantic segmentation

This data set serves as an example for the application of the MiNTiF plugin (https://github.com/CAiM-lab/MiNTiF). Users can test the data set creation, training, prediction, and reconstruction functionality on this data set. The pre-trained model will predict semantic segmentation masks for Arteries and Sinusoids based using the markers DAPI, Endomucin, CXCL12, Endoglin and Collagen as input. <strong>raw training data</strong> contains the original training samples to train a model. <strong>pretrained model </strong>contains a pretrained model that can be applied on MiNTiF datasets. Use MiNTiF to apply this model to "<em>tiled mintif file.h5"</em>. <strong>test data for prediction </strong>contains a dataset that can be used to test the prediction and reconstruction in MiNTiF <strong>info_channel_indices.txt </strong>contains the marker channels used in this dataset and the corresponding indices to use in MiNTiF . <strong>original_raw_file.ims</strong> is the original image file before conversion to <em>tiled mintif file.h5 </em>. Use this to test the dataset creation <strong>tiled mintif file.h5</strong> is the MiNTiF file converted from original_file.ims. Predict on this file <strong>reconstructed_file_with_prediction.tiff </strong>is the image in <em>tiled mintif file.h5 </em> reconstructed after prediction. It now contains two additional channel with the prediction channels.