Patent ID: 8341100
Filing Date: 2012-12-25
Classification: G06K,G06N

Abstract:
1. A method for automatically identifying epithelial portions of a tissue sample, the method comprising the steps of: staining the tissue sample with one or more dyes; applying a color transformation computer process to separate color images of the dyed tissue sample into one or more color channels; and applying a convolutional neural network computer process to the color channels to obtain a decision for each position in the tissue sample, as to whether it is inside or outside an epithelial layer; wherein the color channels produced by the color transformation computer process applying step are dye channels; wherein L1, L2, L3, and L4 are positive integers and wherein the convolutional neural network computer process applying step is performed by: convolving each of the color channels with a set of L1 trained kernels designated for that channel, adding the results that correspond to the same feature map, adding trained constants to each of the L1 results, and passing the results through a non-linear transfer function, to produce a layer of L1 feature maps; averaging together groups of adjacent values in each of the L1 feature maps from the first layer to produce averaged adjacent values of the L1 feature maps, multiplying the results by trained constants, adding trained constants to each of the L1 results, and passing the results through a non-linear transfer function, to produce a second layer of L1 feature maps; convolving those L1 feature maps with a set of L2 trained kernels designated for those feature maps, adding the results that correspond to the same feature map, adding trained constants to each of the L2 results, and passing the results through a non-linear transfer function, to produce a third layer of L2 feature maps; averaging together groups of adjacent values in each of those L2 feature maps to produce averaged adjacent values of the L2 feature maps, multiplying the results by trained constants, adds trained constants to each of the L2 results, and passing the results through a non-linear transfer function, to produce a fourth layer of L2 feature maps; convolving those L2 feature maps of the fourth layer with a set of L3 trained kernels designated for those feature maps, adding the results that correspond to the same feature map, adding trained constants to each of the L3 results, and passing the results through a non-linear transfer function, to produce a fifth layer of L3 feature maps; convolving the L3 feature maps of the fifth layer with a set of L4 trained kernels designated for those feature maps, adding the results that correspond to the same feature map, adding trained constants to each of the L4 results, and passing the results through a non-linear transfer function, to produce a sixth layer of L4 feature maps; multiplying each of the L4 feature maps of the sixth layer with a first trained weight and adds the results together to produce a first output map; multiplying each of the L4 feature maps of the sixth layer with a second trained weight and adding the results together to produce a second output map; comparing the first and second output maps against one another to determine which locations are in the epithelial layers and which are not wherein convolving steps are each performed with a 5 by 5 kernel; wherein L1 is 9, L2 is 16, L3 is 32, and L4 is 64.