Massive training artificial neural network (MTANN) for detecting abnormalities in medical images

An artificial neural network including network parameters that govern how the artificial neural network operates, the artificial neural network being trained by the steps of:

• receiving at least a training image including plural training image pixels;
• receiving at least a likelihood distribution map as a teacher image, the teacher image including plural teacher image pixels each having a pixel value indicating a likelihood that a respective training image pixel is part of a target structure;
• moving a local window across plural sub-regions of the training image to obtain respective sub-region pixel sets;
• inputting the sub-region pixel sets to the artificial neural network so that the artificial neural network provides output pixel values;
• comparing the output pixel values to corresponding teacher image pixel values to determine an error;
• and
• training the network parameters of the artificial neural network to reduce the error.

A computer program product storing program instructions for execution on a computer system, which when executed by the computer system, cause the computer system to train an artificial neural network having network parameters that govern how the artificial neural network operates by performing the steps of:

• receiving at least a training image including plural training image pixels;
• receiving at least a likelihood distribution map as a teacher image, the teacher image including plural teacher image pixels each having a pixel value indicating a likelihood that a respective training image pixel is part of a target structure;
• moving a local window across plural sub-regions of the training image to obtain respective sub-region pixel sets;
• inputting the sub-region pixel sets to the artificial neural network so that the artificial neural network provides output pixel values;
• comparing the output pixel values to corresponding teacher image pixel values to determine an error;
• and
• training the network parameters of the artificial neural network to reduce the error.

A method of detecting a target structure in an image by using an artificial neural network, the method comprising:

• scanning a local window across sub-regions of the image by moving the local window for each sub-region, so as to obtain respective sub-region pixel sets;
• inputting the sub-region pixel sets to the artificial neural network so that the artificial neural network provides, corresponding to the sub-regions, respective output pixel values that represent likelihoods that respective image pixels are part of a target structure, the output pixel values collectively constituting a likelihood distribution map;
• and
• scoring the likelihood distribution map to detect the target structure.

An artificial neural network system, comprising:

• an artificial neural network configured to detect a target structure in an image;
• a scanning mechanism configured to scan a local window across sub-regions of the image by moving the local window for each sub-region, so as to obtain respective sub-region pixel sets:
• an inputting mechanism configured to input the sub-region pixel sets to the artificial neural network so that the artificial neural network provides, corresponding to the sub-regions, respective output pixel values that represent likelihoods that respective image pixels are part of the target structure, the output pixel values collectively constituting a likelihood distribution map;
• and
• a scoring mechanism configured to score the likelihood distribution map to detect the target structure.

A computer program product storing program instructions for execution on a computer system, which when executed by the computer system, cause the computer system to detect a target structure in an image by using an artificial neural network by performing the steps of:

• scanning a local window across sub-regions of the image by moving the local window for each sub-region, so as to obtain respective sub-region pixel sets;
• inputting the sub-region pixel sets to the artificial neural network so that the artificial neural network provides corresponding to the sub-regions respective output pixel values that represent likelihoods that respective image pixels are part of a target structure, the output pixel values collectively constituting a likelihood distribution map;
• and
• scoring the likelihood distribution map to detect the target structure.