Method and system for disease quantification modeling of anatomical tree structure

Abstract

A method and system can be used for disease quantification modeling of an anatomical tree structure. The method may include obtaining a centerline of an anatomical tree structure and generating a graph neural network including a plurality of nodes based on a graph. Each node corresponds to a centerline point and edges are defined by the centerline, with an input of each node being a disease related feature or an image patch for the corresponding centerline point and an output of each node being a disease quantification parameter. The method also includes obtaining labeled data of one or more nodes, the number of which is less than a total number of the nodes in the graph neural network. Further, the method includes training the graph neural network by transferring information between the one or more nodes and other nodes based on the labeled data of the one or more nodes.

What is claimed is: 
     
         1 . A computer-implemented method for disease quantification, comprising:
 receiving an image containing an anatomical tree structure;   extracting, by at least one processor, a centerline of the anatomical tree structure;   extracting, by the at least one processor, disease related features or image patches for a plurality of centerline points along the centerline; and   predicting disease quantification parameters along the centerline, by the at least one processor, by applying a graph neural network to the disease related features or image patches, wherein the graph neural network comprises a plurality of nodes each corresponding to the plurality of centerline points along the centerline.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein the graph neural network comprises a graph convolutional neural network configured as a function of the plurality of nodes and edges among the nodes. 
     
     
         3 . The computer-implemented method of  claim 2 , wherein the graph convolutional neural network is configured to perform a graph convolution operation for each node with at least one neighbor node taken into account. 
     
     
         4 . The computer-implemented method of  claim 2 , wherein each edge is undirected or directed, for propagating information between the nodes linked by the edge. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein the graph neural network comprises a plurality of graph units each corresponding to a node, wherein each graph unit is any one of a Gated Recurrent unit (GRU), a Long Short Term Memory (LSTM) unit, a convolutional LSTM (CLSTM) unit, or a convolutional GRU (CGRU). 
     
     
         6 . The computer-implemented method of  claim 5 , wherein a node has at least one child node, wherein the graph unit corresponding to the node comprises one forget gate for each child node. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein the plurality of centerline points include at least a first point at an inlet of the anatomical tree structure. 
     
     
         8 . The computer-implemented method of  claim 1 , wherein the disease related features comprise at least one of a structural feature, an intensity feature, or a derived feature. 
     
     
         9 . The computer-implemented method of  claim 1 , wherein the graph neural network is trained using labeled data of one or more nodes, a number of which is less than a total number of the plurality of nodes in the graph neural network, wherein gradients or errors of the one or more nodes are transferred to the other nodes of the plurality of nodes. 
     
     
         10 . The computer-implemented method of  claim 1 , wherein the anatomical tree structure is a vessel or an airway. 
     
     
         11 . A system for disease quantification, comprising:
 an interface configured to receive an image containing an anatomical tree structure;   at least one processor configured to:
 receive an image containing an anatomical tree structure; 
 extract a centerline of the anatomical tree structure; 
 extract disease related features or image patches for a plurality of centerline points along the centerline; and 
 predict disease quantification parameters along the centerline, by the at least one processor, by applying a graph neural network to the disease related features or image patches, wherein the graph neural network comprises a plurality of nodes each corresponding to the plurality of centerline points along the centerline. 
   
     
     
         12 . The system of  claim 11 , wherein the graph neural network comprises a graph convolutional neural network configured as a function of the plurality of nodes and edges among the nodes. 
     
     
         13 . The system of  claim 12 , wherein the graph convolutional neural network is configured to perform a graph convolution operation for each node with at least one neighbor node taken into account. 
     
     
         14 . The system of  claim 11 , wherein the graph neural network comprises a plurality of graph units each corresponding to a node, wherein each graph unit is any one of a Gated Recurrent unit (GRU), a Long Short Term Memory (LSTM) unit, a convolutional LSTM (CLSTM) unit, or a convolutional GRU (CGRU). 
     
     
         15 . The system of  claim 14 , wherein the node has at least one child node, wherein the graph unit corresponding to the node comprises one forget gate for each child node. 
     
     
         16 . The system of  claim 11 , wherein the plurality of centerline points include at least a first point at an inlet of the anatomical tree structure. 
     
     
         17 . The system of  claim 11 , wherein the disease related features comprise at least one of a structural feature, an intensity feature, or a derived feature. 
     
     
         18 . The system of  claim 11 , wherein the anatomical tree structure is a vessel or an airway. 
     
     
         19 . A non-transitory computer readable medium, storing instructions that, when executed by a processor, perform a method for disease quantification, the method comprising:
 receiving an image containing an anatomical tree structure;   extracting a centerline of the anatomical tree structure;   extracting disease related features or image patches for a plurality of centerline points along the centerline; and   predicting disease quantification parameters along the centerline by applying a graph neural network to the disease related features or image patches, wherein the graph neural network comprises a plurality of nodes each corresponding to the plurality of centerline points along the centerline.   
     
     
         20 . The non-transitory computer readable medium of  claim 19 , wherein the anatomical tree structure is a vessel or an airway.