System and method for differentiating a tissue of interest from another part of a medical scanner image

A system for differentiating a tissue of interest from at least one other part of a medical scanner image, the system comprising:

• a first device, the first device including,
• at least one first memory configured to store first instructions, and
• at least one first processor configured to execute the first instructions and cause the first device to perform at least,
• generate an eroded segmentation mask for each medical scanner training image of a set of medical scanner training images based on a binary erosion of a ground truth segmentation mask of each medical scanner training image, the ground truth segmentation mask indicating a tissue of interest within the respective medical scanner training image,
• generate label data based on a difference between the eroded mentation mas and the ground truth ion mask of each medical scanner training image;
• and
• train an artificial neural network on the label data, the trained artificial neural network being configured to generate output data based on a reduced-size medical scanner image, the output data indicating a one-dimensional borderline between at last a part of the tissue of interest and at least one other within the medical scanner image by at least one of polynomial parameters, spline parameters, or line parameters;
• an input module configured to receive a medical scanner image;
• a computing device configured to,
• implement a resizing module, the resizing module being configured to reduce a size of the medical scanner image to obtain a reduced-size medical scanner image,
• implement the trained artificial neural network,
• provide the trained artificial neural network with the reduced-size medical scanner image,
• generate a segmentation mask based on the output data;
• and
• an output module configured to output an output signal comprising or based on the output data, wherein the output signal comprises the generated segmentation mask.