System and method for three-dimensional imaging of samples using a machine learning algorithm

An image processing system, the image processing system comprising:

• one or more processors configured to execute a set of program instructions stored in memory, the set of program instructions configured to cause the one or more processors to:
• receive a plurality of training images of one or more portions of one or more samples, the plurality of training images including a plurality of training multi-modality focal stacks;
• receive ground truth three-dimensional data for each training multi-modality focal stack of the plurality of training multi-modality focal stacks;
• train a machine learning algorithm based on the plurality of training images and the received ground truth three-dimensional data;
• receive a multi-modality focal stack of a sample from an optical assembly, the optical assembly configured to illuminate the one or more portions of the sample using two or more illumination modalities, wherein the two or more illumination modalities include at least a first illumination modality and a second illumination modality, the first illumination modality including a first set of illumination angles, the second illumination modality including a second set of illumination angles, at least the second set of illumination angles is at least partially different from the first set of illumination angles, the multi-modality focal stack including a plurality of images acquired at two or more distances between the sample and a front focal plane, wherein at least a first image of the plurality of images is acquired using the first illumination modality and at least an additional image is acquired using the second illumination modality;
• and
• generate a depth map of the sample using the trained machine learning algorithm and the received multi-modality focal stack.

A method, the method comprising:

• receiving a plurality of training images of one or more portions of one or more samples, the plurality of training images including a plurality of training multi-modality focal stacks;
• receiving ground truth three-dimensional data for each training multi-modality focal stack of the plurality of training multi-modality focal stacks;
• training a machine learning algorithm based on the plurality of training images and the received ground truth three-dimensional data;
• receiving a multi-modality focal stack of the one or more samples from an optical assembly, the optical assembly configured to illuminate the one or more samples using two or more illumination modalities, wherein the two or more illumination modalities include at least a first illumination modality and a second illumination modality, the first illumination modality including a first set of illumination angles, the second illumination modality including a second set of illumination angles, at least the second set of illumination angles is at least partially different from the first set of illumination angles, the multi-modality focal stack including a plurality of images acquired at two or more distances between the sample and a front focal plane, wherein at least a first image of the plurality of images is acquired using the first illumination modality and at least an additional image is acquired using the second illumination modality;
• and
• generating a depth map of the one or more samples using the trained machine learning algorithm and the received multi-modality focal stack.