Identifying microorganisms using three-dimensional quantitative phase imaging

A system comprising:

• a phase-contrast microscope that is configured to generate one or more three-dimensional quantitative phase image of one or more microscopic entities,
• wherein each of the 3D quantitative phase images comprises a respective 3D representation of the one or more microscopic entities as a 3D refractive index tomogram represented as a 3D matrix of numerical values, where each component of the 3D matrix corresponds to a respective 3D spatial position and defines a refractive index at the corresponding 3D spatial position;
• and
• a microscopic entity classification system that is configured to perform operations comprising:
• processing the one or more 3D quantitative phase images using a neural network, wherein:
• the neural network is a convolutional neural network comprising one or more 3D convolutional layers, wherein each of the 3D convolutional layers performs operations comprising:
• receiving a layer input that comprises one or more 3D matrices of features derived from the one or more 3D quantitative phase images input to the neural network;
• and
• processing the layer input to generate a layer output, comprising convolving one or more 3D convolutional filters with the one or more 3D matrices included in the layer input;
• and
• the neural network is configured to process the one or more three-dimensional quantitative phase images in accordance with trained parameter values of the neural network to generate a neural network output characterizing the one or more microscopic entities;
• and
• classifying the one or more microscopic entities using the neural network output.