Deep magnetic resonance fingerprinting auto-segmentation

A method of segmenting tomographic biomedical images, comprising:

• identifying, by a computing system, a plurality of acquisition parameters derived from training of an image segmentation model and defining an acquisition of tomographic biomedical images;
• receiving, by the computing system, a plurality of tomographic biomedical images of a sample of a subject using the plurality of acquisition parameters and a plurality of tissue parameters, the plurality of tissue parameters associated with the section of the subject corresponding to the plurality of tomographic biomedical images, the section having at least one region of interest;
• applying, by the computing system, the image segmentation model to the plurality of tomographic biomedical images to generate a segmented tomographic biomedical image;
• and
• storing, by the computing system, the segmented tomographic biomedical image identifying the at least one region of interest on the section of the subject.

A system for training models to segment tomographic biomedical images, comprising:

• a computing system having one or more processors coupled with memory, configured to:
• identify a training dataset having:
• a plurality of sample tomographic biomedical images acquired from a section of a subject, a plurality of tissue parameters associated with the section of subject, and an annotation identifying at least one region on the section in at least one of the plurality of sample tomographic biomedical images;
• train an image segmentation model using the training dataset, the image segmentation model comprising:
• a generator to determine a plurality of acquisition parameters using the plurality of sample tomographic biomedical images, the plurality of acquisition parameters defining an acquisition of the plurality of sample tomographic biomedical images from the tissue sample;
• an image synthesizer to generate a plurality of synthesized tomographic biomedical images in accordance with the plurality of tissue parameters and the plurality of acquisition parameters;
• a discriminator to determine a classification result indicating whether an input biomedical image corresponding to one of the plurality of sample tomographic biomedical image or the plurality of synthesized tomographic biomedical images is synthesized;
• and
• a segmentor to generate, using the input biomedical image, a segmented biomedical image identifying the at least one region on the section of the subject;
• and
• store the image segmentation model for use to identify one or more regions of interest in tomographic biomedical images.