System and methods for inferring thickness of anatomical classes of interest in two-dimensional medical images using deep neural networks

A method comprising:

• receiving a two-dimensional (2D) medical image of a first region of an imaging subject;
• receiving a three-dimensional (3D) medical image of the first region of the imaging subject;
• annotating voxels of the 3D medical image with object class labels for a first object class of interest to produce a first plurality of annotated voxels;
• projecting the 3D medical image along a plurality of rays onto a plane to produce a synthetic 2D medical image matching the 2D medical image;
• projecting the first plurality of annotated voxels along the plurality of rays onto the plane to produce a first plurality of thickness values for the first object class of interest;
• producing a first ground truth thickness mask for the first object class of interest from the first plurality of thickness values;
• and
• training a deep neural network to learn a mapping between 2D medical images and thickness masks for the first object class of interest by:
• mapping the 2D medical image to a first predicted thickness mask for the first object class of interest;
• determining a loss for the first predicted thickness mask based on a difference between the first predicted thickness mask and the first ground truth thickness mask;
• and
• updating parameters of the deep neural network based on the loss.

A medical imaging system comprising:

• an imaging device;
• a display device;
• a memory, storing:
• a feature extractor;
• a first trained convolutional neural network (CNN);
• a second trained CNN;
• and
• instructions;
• a processor communicably coupled to the imaging device, the display device, and the memory, and when executing the instructions, configured to:
• acquire a two-dimensional (2D) medical image of an anatomical region of an imaging subject via the imaging device;
• extract features from the 2D medical image using the feature extractor;
• map the features to a segmentation mask for an object class of interest using the first trained CNN;
• map the features to a thickness mask for the object class of interest using the second trained CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image;
• apply the segmentation mask to the thickness mask to produce a segmented thickness mask;
• and
• display the segmented thickness mask via the display device.