Multi-Phase 3D Liver Lesion Segmentation From CT or MRI
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Solution Overview
Problem
Existing methods for 3D lesion segmentation in CT or MRI imaging primarily rely on single-phase or phase-specific neural networks, which are confined to 2D applications, or combine separate phase-specific models post-processing, lacking an efficient method for enhanced 3D segmentation directly from multi-phase imaging datasets.
Innovation Solution
A computer-implemented method using a trained function that encodes and aggregates feature information from multiple phases of 3D CT imaging datasets through an encoder-decoder architecture with a spatial aggregation module, generating 3D segmentation data by aligning and combining 2D slices across phases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If phase-specific neural networks are used for 3D lesion segmentation, then segmentation can be performed on specific imaging phases, but the method is confined to 2D applications and lacks efficient 3D segmentation capability
Solution Approach 1:
The patent transitions from 2D phase-specific segmentation to 3D multi-phase segmentation by processing volumetric data across multiple imaging phases simultaneously. The system analyzes lesions in three-dimensional space while incorporating temporal information from different contrast phases, enabling comprehensive 3D segmentation that leverages both spatial and temporal dimensions of the imaging data.
Solution Approach 2:
The patent creates a unified segmentation framework that can handle multiple imaging phases and 3D volumetric data within a single system. This multi-functional approach allows the same methodology to process different phases (arterial, portal venous, delayed) and generate consistent 3D segmentation results across various lesion types and imaging scenarios.
2Loss of information
If separate phase-specific models are combined in post-processing, then multi-phase information can be utilized, but the combination process is complex and less efficient
Solution Approach 1:
The patent merges multiple phase-specific processing streams into a unified 3D segmentation framework. Instead of separately processing each phase and then combining results, the system integrates arterial, portal venous, and delayed phase information within a single coherent processing pipeline that operates on 3D volumetric data, simplifying the overall process while preserving multi-phase information.
Solution Approach 2:
The patent performs preliminary alignment and registration of multi-phase 3D volumetric data before segmentation. By pre-processing the data to establish consistent spatial relationships across phases, the system eliminates the need for complex post-processing combination steps, as the segmentation is performed on already-integrated multi-phase information.
Data Source
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AI summary
Computer-implemented methods and computer systems are provided for locating a liver lesion in the input data. In an example, a computer implemented method of the current disclosure comprises receiving, by a first computer interface, input data; applying a trained function to the input data; generating, by the trained function, output data, wherein the output data is associated with the input data; providing, via a second computer interface, the output data, wherein the input data comprises a multi-phase three-dimensional, 3D, computed tomography, CT, imaging dataset of a liver lesion associated with a patient, and wherein the output data comprises a three-dimensional, 3D, segmentation data for locating the liver lesion in the input data.