US 2020/0349449 A1 · 3-D CONVOLUTIONAL AUTOENCODER FOR LOW-DOSE CT VIA TRANSFER LEARNING FROM A 2-D TRAINED NETWORK

Abstract

A 3-D convolutional autoencoder for low-dose CT via transfer learning from a 2-D trained network is described, A machine learning method for low dose computed tomography (LDCT) image correction is provided. The method includes training, by a training circuitry, a neural network (NN) based, at least in part, on two-dimensional (2-D) training data. The 2-D training data includes a plurality of 2-D training image pairs. Each 2-D image pair includes one training input image and one corresponding target output image. The training includes adjusting at least one of a plurality of 2-D weights based, at least in part, on an objective function. The method further includes refining, by the training circuitry, the NN based, at least in part, on three-dimensional (3-D) training data. The 3-D training data includes a plurality of 3-D training image pairs. Each 3-D training image pair includes a plurality of adjacent 2-D training input images and at least one corresponding target output image. The refining includes adjusting at least one of a plurality of 3-D weights based, at least in part, on the plurality of 2-D weights and based, at least in part, on the objective function. The plurality of 2-D weights includes the at least one adjusted 2-D weight.

Claims20

Dependent claims

A method for low dose computed tomography (LDCT) image correction, the method comprising:

training, by a training circuitry, a convolutional neural network (CNN) based, at least in part, on two-dimensional (2-D) training data, the 2-D training data comprising a plurality of 2-D training image pairs, each 2-D image pair comprising one training input image and one corresponding training output image, the training comprising adjusting at least one of a plurality of 2-D filter parameters based, at least in part, on an objective function;
and;
refining, by the training circuitry, the CNN based, at least in part, on three-dimensional (3-D) training data, the 3-D training data comprising a plurality of 3-D training image pairs, each 3-D training image pair comprising a plurality of adjacent 2-D training input images and at least one corresponding training output image, the refining comprising adjusting at least one of a plurality of 3-D filter parameters based, at least in part, on the plurality of 2-D filter parameters and based, at least in part, on the objective function, the plurality of 2-D filter parameters comprising the at least one adjusted 2-D filter parameter.

A convolutional neural network (CNN) comprising:

a contracting path-based convolutional auto encoder (CPCA) comprising:
a plurality of main convolutional layers coupled in series,
a plurality of deconvolutional layers coupled in series, and
a plurality of contracting paths, each contracting path coupled between an output of a respective convolutional layer and a respective selected deconvolutional layer,
wherein the CPCA is trained based, at least in part, on two-dimensional (2-D) training data, the 2-D training data comprising a plurality of 2-D training image pairs, each 2-D image pair comprising one training input image and one corresponding training output image, the training comprising adjusting at least one of a plurality of 2-D filter parameters based, at least in part, on an objective function and the CPCA is refined based, at least in part, on three-dimensional (3-D) training data, the 3-D training data comprising a plurality of 3-D training image pairs, each 3-D training image pair comprising a plurality of adjacent 2-D training input images and at least one corresponding training output image, the refining comprising adjusting at least one of a plurality of 3-D filter parameters based, at least in part, on the plurality of 2-D filter parameters and based, at least in part, on the objective function, the plurality of 2-D filter parameters comprising the at least one adjusted 2-D filter parameter.

2 more elements

Dependencies are inferred from the claim text.

3-d convolutional autoencoder for low-dose ct via transfer learning from a 2-d trained network

Life of this patent

3-d convolutional autoencoder for low-dose ct via transfer learning from a 2-d trained network

Life of this patent

Abstract

Claims20