Systems and methods for quantitative evaluation of optical map quality and for data augmentation automation

An apparatus comprising:

• at least one processor configured to:
• obtain at least one image and a ground truth map associated with the at least one image;
• generate multiple optical maps using multiple algorithms and the at least one image;
• and
• for each algorithm, identify at least one score for the algorithm using one or more of the optical maps generated using the algorithm and the ground truth map;
• wherein the ground truth map identifies one or more boundaries associated with one or more foreground objects in the at least one image;
• and
• wherein the scores identify how well the optical maps generated using the algorithms separate the one or more foreground objects from a background in the at least one image.

A non-transitory computer readable medium containing instructions that when executed cause at least one processor to:

• obtain at least one image and a ground truth map associated with the at least one image;
• generate multiple optical maps using multiple algorithms and the at least one image;
• and
• for each algorithm, identify at least one score for the algorithm using one or more of the optical maps generated using the algorithm and the ground truth map;
• wherein the ground truth map identifies one or more boundaries associated with one or more foreground objects in the at least one image;
• and
• wherein the scores identify how well the optical maps generated using the algorithms separate the one or more foreground objects from a background in the at least one image.

A method comprising:

• training, using at least one processor, an augmentation network based on multiple first images, the augmentation network trained to generate additional images having one or more photometric characteristics that match or are similar to one or more photometric characteristics of at least some of the first images;
• providing, using the at least one processor, multiple second images to the augmentation network;
• generating, using the at least one processor, multiple third images by the augmentation network based on at least some of the second images;
• and
• training, using the at least one processor, a machine learning model to perform an image processing operation using at least some of the second and third images.