Tissue staining and sequential imaging of biological samples for deep learning image analysis and virtual staining

A computer-implemented method of generating ground truth images from a ground truth multi-record dataset comprising:

• segmenting biological visual features of a first image of a first record using an automated segmentation process;
• mapping the segmentation of the features of the first image to a second image of the first record;
• for a respective segmentation of the second image:
• computing intensity values of pixels within and/or in proximity to a surrounding of the respective segmentation indicating a visual depiction of at least one biomarker in the second image;
• and
• classifying the respective segmentation with one or more ground truth labels by mapping the computed intensity value using a set of rules to a classification category;
• wherein the ground truth multi-record dataset comprises multiple records, and
• wherein a second record comprises:
• a first image of a sample depicting a first group of cells,
• a second image of the same sample comprising the same group of cells, sequentially stained with a stain configured to stain a group of cells presenting at least one biomarker, and
• one or more ground truth labels indicating a respective category for at least one sub-group of cells in the second images.

A computer-implemented method of automatically generating ground truth labels for biological objects, comprising:

• feeding unlabeled sets of images of samples of tissue of a plurality of sample individuals into a ground truth generator machine learning model, wherein the unlabeled sets of images depict biological objects respectively presenting at least one first biomarker and at least one second biomarker different than the at least one first biomarker;
• and
• obtaining automatically generated ground truth labels as an outcome of the ground truth generator machine learning model,
• wherein the ground truth generator machine learning model is trained on a ground truth multi-record training dataset wherein a record comprises:
• a first image of a sample of tissue of a subject depicting a first group of biological objects presenting at least one first biomarker,
• a second image, comprising at least a portion of the same sample of tissue comprising one or more biological objects in the first group of biological objects, sequentially stained with a stain configured to stain a second group of biological objects presenting at least one second biomarker different from the at least one first biomarker, and
• ground truth labels indicating a respective biological object category for at least one sub-group of biological objects in the first and second images.