Tree Structured RNN for Anatomical Tree Analysis
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Solution Overview
Problem
Current machine learning-based algorithms for anatomical tree structure analysis are labor-intensive, time-consuming, and prone to subjective results due to asynchronous analysis of branches, leading to reduced accuracy and efficiency, especially in bifurcation and overlapped regions.
Innovation Solution
A tree structured recurrent neural network (RNN) model is developed that embeds spatial relationships among nodes, considering global dependencies across the entire anatomical tree structure, allowing simultaneous analysis of all sampling positions to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If machine learning-based algorithms analyze branches asynchronously, then the analysis process can be simplified, but the analysis accuracy and efficiency are reduced due to inconsistent results in bifurcation and overlapped regions
Solution Approach 1:
The patent merges the analysis of multiple branches into a unified synchronous processing framework. The tree structured RNN integrates information from all branches simultaneously, combining their features at bifurcation regions to produce consistent analysis results, thereby resolving the accuracy- efficiency contradiction.
Solution Approach 2:
The tree structured recurrent neural network serves as an intermediary that coordinates information flow between different branches. It mediates the analysis process by propagating information synchronously across the tree structure, ensuring consistency in bifurcation and overlapped regions while maintaining systematic processing.
2Loss of energy
If machine learning-based algorithms analyze branches asynchronously, then the computational load per branch is reduced, but the overall analysis efficiency is reduced due to inconsistent results requiring reanalysis
Solution Approach 1:
The patent implements continuous synchronous processing across all branches simultaneously. The tree structured RNN maintains continuous information propagation throughout the entire tree structure, eliminating the need for reanalysis due to inconsistencies, thereby improving overall productivity while managing computational energy efficiently.
Solution Approach 2:
The patent segments the analysis task into parallel processing units at different tree levels, where each node processes its local information independently but contributes to the global synchronous analysis. This segmentation allows efficient parallel computation while maintaining overall consistency through the tree structure.
3Device complexity
If local features of single centerline points are used for analysis, then the model complexity is reduced, but the analysis accuracy is limited due to inability to capture global dependencies
Solution Approach 1:
The patent transitions from analyzing single centerline points in one dimension to analyzing the entire tree structure in multiple dimensions. The tree structured RNN captures spatial relationships and global dependencies by propagating information across the hierarchical tree structure, adding dimensional context without excessive complexity increase.
Solution Approach 2:
The tree structured recurrent neural network serves multiple functions simultaneously: it processes local features at each node, captures global dependencies across the entire tree, and produces consistent results for all branches. This multi-functionality resolves the contradiction between model complexity and analysis accuracy.
Data Source
AI summary
The present disclosure is directed to a computer-implemented method and system for anatomical tree structure analysis. The method includes receiving model inputs for a set of positions in an anatomical tree structure. The method further includes applying, by a processor, a learning network to the model inputs. The learning network comprises a set of encoders and a neural network modeling the anatomical tree structure, wherein each encoder provides features extracted from the model input at a corresponding position. The neural network has a plurality of nodes constructed according to the anatomical tree structure and each node is configured to process the extracted features from one or more of the encoders. The method additionally includes providing an output of the learning network as an analysis result of the anatomical tree structure analysis.