Systems and methods for predicting tissue characteristics for a pathology image using a statistical model

A system for predicting tissue characteristics for a pathology image using a trained statistical model trained on a plurality of annotated pathology images, wherein each image of the plurality of annotated pathology images includes at least one annotation describing tissue characteristics for one or more portions of the image, the system comprising:

• at least one computer hardware processor;
• and
• at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:
• accessing a pathology image;
• retrieving a trained statistical model from at least one storage device, wherein:
• the trained statistical model is trained on a set of training patches and a corresponding set of patch annotations derived from at least one annotated pathology image of the plurality of annotated pathology images,
• each training patch of the set of training patches includes one or more values obtained from a subset of pixels in the at least one annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the subset of pixels, and
• at least one dimension of each training patch of the set of training patches is larger than a corresponding dimension of input to the trained statistical model and additional pixels in the training patch are included when randomly resampling the training patch for data augmentation;
• defining a set of patches from the pathology image, wherein the pathology image is different from the plurality of annotated pathology images, wherein each patch of the set of patches includes a subset of pixels from the pathology image;
• processing, using the trained statistical model, the set of patches to predict one or more annotations for each patch of the set of patches;
• and
• storing the predicted one or more annotations for each patch of the set of patches on the at least one storage device.

A method for predicting tissue characteristics for a pathology image using a trained statistical model trained on a plurality of annotated pathology images, wherein each image of the plurality of annotated pathology images includes at least one annotation describing tissue characteristics for one or more portions of the image, the method comprising:

• accessing a pathology image;
• retrieving a trained statistical model from at least one storage device, wherein:
• the trained statistical model is trained on a set of training patches and a corresponding set of patch annotations derived from at least one annotated pathology image of the plurality of annotated pathology images,
• each training patch of the set of training patches includes one or more values obtained from a subset of pixels in the at least one annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the subset of pixels, and
• the trained statistical model comprises a convolutional neural network including a plurality of layers, wherein at least one layer of the plurality of layers is aligned such that (N−K)/S is an integer, wherein N represents a size of each input dimension of the at least one layer, wherein K represents a size of a convolution filter of the at least one layer, and wherein S represents a size of a stride of the at least one layer;
• defining a set of patches from the pathology image, wherein the pathology image is different from the plurality of annotated pathology images, wherein each patch of the set of patches includes a subset of pixels from the pathology image;
• processing, using the trained statistical model, the set of patches to predict one or more annotations for each patch of the set of patches;
• and
• storing the predicted one or more annotations for each patch of the set of patches on the at least one storage device.

A system for predicting tissue characteristics for a pathology image using a trained statistical model trained on a plurality of annotated pathology images, wherein each image of the plurality of annotated pathology images includes at least one annotation describing tissue characteristics for one or more portions of the image, the system comprising:

• at least one computer hardware processor;
• and
• at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:
• accessing a pathology image;
• retrieving a trained statistical model from at least one storage device, wherein:
• the trained statistical model is trained on a set of training patches and a corresponding set of patch annotations derived from at least one annotated pathology image of the plurality of annotated pathology images,
• each training patch of the set of training patches includes one or more values obtained from a subset of pixels in the at least one annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the subset of pixels, and
• the trained statistical model comprises a convolutional neural network including a plurality of layers, wherein at least one layer of the plurality of layers is aligned such that (N−K)/S is an integer, wherein N represents a size of each input dimension of the at least one layer, wherein K represents a size of a convolution filter of the at least one layer, and wherein S represents a size of a stride of the at least one layer;
• defining a set of patches from the pathology image, wherein the pathology image is different from the plurality of annotated pathology images, wherein each patch of the set of patches includes a subset of pixels from the pathology image;
• processing, using the trained statistical model, the set of patches to predict one or more annotations for each patch of the set of patches;
• and
• storing the predicted one or more annotations for each patch of the set of patches on the at least one storage device.