Machine learning holography for particle field imaging

A method for particle segmentation, the method comprising:

• obtaining input data, the input data comprising at least one of a hologram of a 3-dimensional (3D) particle field, an image reconstructed from the hologram to a longitudinal location, or a minimum intensity projection in a longitudinal direction from a reconstructed optical field of the hologram;
• and
• applying a U-net convolutional neural network (CNN) to the input data to generate output data, wherein:
• the U-net CNN comprises a set of one or more encoder blocks and a set of one or more decoder blocks,
• for each respective encoder block of the set of encoder blocks, the respective encoder block has a residual connection between a first layer of the respective encoder block and a second layer of the respective encoder block that skips over a convolution layer of the respective encoder block between the first layer of the respective encoder block and the second layer of the respective encoder block,
• for each respective decoder block of the set of decoder blocks, the respective decoder block has a residual connection between a first layer of the respective decoder block and a second layer of the respective decoder block that skips over a convolution layer of the respective decoder block between the first layer of the respective decoder block and the second layer of the respective decoder block,
• output of a last-occurring encoder block of the set of encoder blocks is input to a first-occurring decoder block of the set of decoder blocks, and
• the output data includes a first output image and a second output image, the first output image comprising a particle size map for particles in the 3D particle field, and the second output image indicating locations of centroids of the particles in the 3D particle field.

A system comprising:

• processing circuitry;
• and
• a computer-readable storage medium coupled to the at least one processor, the computer-readable storage medium configured to store instructions that configure the processing circuitry to:
• obtain input data, the input data comprising at least one a hologram of a 3-dimensional (3D) particle field, a depth map of the 3D particle field, or a maximum phase projection of the 3D particle field;
• and
• apply a U-net convolutional neural network (CNN) to the input data to generate output data, wherein:
• the U-net CNN comprises a set of one or more encoder blocks and a set of one or more decoder blocks,
• for each respective encoder block of the set of encoder blocks, the respective encoder block has a residual connection between a first layer of the respective encoder block and a second layer of the respective encoder block that skips over a convolution layer of the respective encoder block between the first layer of the respective encoder block and the second layer of the respective encoder block,
• for each respective decoder block of the set of decoder blocks, the respective decoder block has a residual connection between a first layer of the respective decoder block and a second layer of the respective decoder block that skips over a convolution layer of the respective decoder block between the first layer of the respective decoder block and the second layer of the respective decoder block,
• output of a last-occurring encoder block of the set of encoder blocks is input to a first-occurring decoder block of the set of decoder blocks, and
• the output data includes a first output image and a second output image, the first output image comprising a channel in which pixel intensity corresponds to relative depth of particles in the 3D particle field, and the second output image indicating locations of centroids of the particles in the 3D particle field.

An imaging system comprising:

• processing circuitry;
• and
• a computer-readable storage medium coupled to the at least one processor, the computer-readable storage medium configured to store instructions that configure the processing circuitry to:
• obtain input data, the input data comprising a hologram, a depth map of the 3D particle field, and a maximum phase projection of the 3D particle field;
• and
• apply a U-net convolutional neural network (CNN) to the input data to generate output data, wherein:
• the U-net CNN comprises a set of one or more encoder blocks and a set of one or more decoder blocks,
• for each respective encoder block of the set of encoder blocks, the respective encoder block has a residual connection between a first layer of the respective encoder block and a second layer of the respective encoder block that skips over a convolution layer of the respective encoder block between the first layer of the respective encoder block and the second layer of the respective encoder block,
• for each respective decoder block of the set of decoder blocks, the respective decoder block has a residual connection between a first layer of the respective decoder block and a second layer of the respective decoder block that skips over a convolution layer of the respective decoder block between the first layer of the respective decoder block and the second layer of the respective decoder block,
• output of a last-occurring encoder block of the set of encoder blocks is input to a first-occurring decoder block of the set of decoder blocks, and
• the output data includes a first output image and a second output image, the first output image comprising a channel in which pixel intensity corresponds to relative depth of particles in the 3D particle field, and the second output image indicating locations of centroids of the particles in the 3D particle field.