System and method for automation of surgical pathology processes using artificial intelligence

An image processing device for pathology comprising:

• a processor, having one or more artificial intelligence (AI) modules in association therewith, which receives image data from at least one of camera images of whole tissue acquired by a camera assembly and whole slide images (WSIs) of inked and segmented tissue samples;
• a mask that produces image results for tissue with holes and free of holes;
• a filter that provides filtered image results to the AI modules, which thereby detects tumors and macroarchitecture features,
• wherein the one or more AI modules comprise at least one trained neural network including at least one of a convolutional neural network (CNN), a graph neural network (GNN), or a combination thereof configured to detect tumors and macroarchitecture features from the filtered image results;
• a score generator that produces quality score outputs for tumors and macroarchitecture features;
• a report generator that provides reports with one or more parameters to a user;
• a tissue determination process that determines a size and a description of tissue automatically based upon characteristics in the images acquired by the camera assembly;
• a tissue grossing and inking process that automatically generates a grossing and inking scheme based upon a user input of desired percentage of tissue margins analyzed and the tissue size;
• and
• an ink detection and orientation process that operates on the filtered image results and delivers the filtered image results thereof to the report generator,
• wherein the macroarchitecture features are defined by at least one of holes, fat, edges, dermis and epidermis information,
• wherein the score generator is configured to compute a quality score (Quality section i) for a section i according to:
• Quality
• section
• ⁢
• i
• =
• ∑
• region
• ∈
• {
• fat
• ,
• dermis
• ,
• …
• }
• importance
• region
• *
• (
• holes
• region
• +
• ∑
• j
• ∈
• {
• 0
• ,
• 1
• ,
• 2
• ,
• …
• }
• tumor
• region
• ,
• section
• ⁢
• j
• *
• [
• λ
• *
• distance
• section
• ⁢
• i
• ,
• section
• ⁢
• j
• ]
• -
• 1
• )
• wherein:
• region is a set of tissue regions including at least one of fat, dermis, epidermis, or subcutaneous tissue,
• holesregion is a hole-related value for the region representing an amount or presence of tissue artifact within the region,
• importanceregion is an importance weighting associated with the region,
• tumorregion,section is a tumor-related value associated with the region in an adjacent section,
• distancesection I,section j is a distance between a section i and an adjacent section j;
• λ is a scaling constant that controls weighting of adjacent sections as a function of distance;
• and
• wherein the importance weighting and the scaling constant λ are each determined based on at least one of expert knowledge and training data.