Fluorescence lifetime imaging using deep learning

A method for fluorescence lifetime imaging (FLI), the method comprising:

• receiving, by a first convolutional layer of a deep neural network (DNN), FLI input data;
• receiving, by each intermediate layer of a plurality of intermediate layers, a respective intermediate input corresponding to an output of a respective prior layer;
• providing, by each intermediate layer, a respective intermediate output related to the received respective intermediate input;
• and
• providing, by an output layer, estimated FLI output data corresponding to the received FLI input data, the output layer is a fully convolutional (FC) down-sample layer comprising:
• a first two-dimensional (2D) convolutional block;
• a first intermediate block coupled to an output of the first 2D convolutional block;
• a second 2D convolutional block coupled to an output of the first intermediate block;
• a second intermediate block coupled to an output of the second 2D convolutional block;
• a third 2D convolutional block coupled to an output of the second intermediate block;
• and
• a rectified linear unit block coupled to an output of the third 2D convolutional block;
• wherein the first intermediate block and the second intermediate block each comprise a batch normalization function and a rectified linear unit wherein the DNN is trained using synthetic data.

A fluorescence lifetime imaging (FLI) deep learning system, the system comprising:

• a processor;
• a memory;
• input/output circuitry;
• and
• a deep neural network (DNN), the DNN comprising:
• a first convolutional layer configured to receive FLI input data;
• a plurality of intermediate layers, each intermediate layer configured to receive a respective intermediate input corresponding to an output of a respective prior layer, each intermediate layer further configured to provide a respective intermediate output related to the received respective intermediate input;
• and
• an output layer configured to provide estimated FLI output data corresponding to the received FLI input data, the output layer is a fully convolutional (FC) down-sample layer comprising:
• a first two-dimensional (2D) convolutional block;
• a first intermediate block coupled to an output of the first 2D convolutional block;
• a second 2D convolutional block coupled to an output of the first intermediate block;
• a second intermediate block coupled to an output of the second 2D convolutional block;
• a third 2D convolutional block coupled to an output of the second intermediate block;
• and
• a rectified linear unit block coupled to an output of the third 2D convolutional block;
• wherein the first intermediate block and the second intermediate block each comprise a batch normalization function and a rectified linear unit;
• wherein the DNN is trained using synthetic data.