Partial Point Cloud Matching for Large-Deformation Medical Image Registration
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Solution Overview
Problem
Conventional medical image registration techniques face challenges in handling content inconsistency, varying spatial discrepancies, and suboptimal image quality, while being computationally expensive and susceptible to contrast differences, limiting their ability to handle large deformations effectively.
Innovation Solution
An unsupervised multitask ensemble of machine learning-based networks for partial point cloud matching, including a point cloud completion network, a point certainty estimation network, and a point registration network, trained with self-supervised and adversarial learning, to align multiple anatomical objects across medical images, while being invariant to image contrast and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional medical image registration techniques are used, then computational efficiency is improved, but the ability to handle content inconsistency and tissue differentiation is lost
Solution Approach 1:
The patent segments the registration task into multiple independent sub-tasks handled by separate networks: point cloud completion network for handling missing data, point certainty estimation network for quality assessment, and point registration network for alignment. This segmentation allows each network to specialize in specific challenges, improving overall adaptability while maintaining computational efficiency through parallel processing.
Solution Approach 2:
The patent creates a universal registration system where a shared encoder processes input from multiple sources (medical images, point clouds, certificates) and feeds into multiple specialized networks. This multi-functional architecture enables the system to handle content inconsistency, tissue differentiation, and various image qualities simultaneously, resolving the contradiction between efficiency and versatility.
2Adaptability or versatility
If conventional approaches addressing content mismatch are used, then content inconsistency is improved, but computational cost increases and spatially varying motion handling is limited
Solution Approach 1:
The patent divides the computationally intensive content mismatch handling into separate specialized networks (point cloud completion network and point certainty estimation network) that process information independently before feeding into the registration network. This segmentation reduces the computational burden on any single network while maintaining comprehensive content mismatch handling capabilities.
Solution Approach 2:
The patent performs preliminary actions by pre-processing medical images into point cloud representations and pre-estimating quality certificates before the actual registration occurs. This preliminary processing simplifies the main registration task and reduces real-time computational requirements while maintaining high adaptability to content inconsistencies.
3Power
If conventional registration techniques are used, then computational load is reduced, but accuracy under varying image quality and contrast is compromised
Solution Approach 1:
The patent introduces a point cloud completion network and point certainty estimation network as intermediary components that process and enhance the input data before registration. These intermediary networks compensate for varying image quality and contrast by generating complementary information and quality assessments, thereby maintaining high registration accuracy without significantly increasing computational load.
Solution Approach 2:
The patent creates synthetic point cloud representations and quality certificates as copies of the original medical image data. These synthetic representations serve as enhanced inputs for registration, allowing the system to achieve high accuracy under varying image qualities while maintaining manageable computational loads through efficient data transformation.
4Speed
If conventional techniques are used, then processing speed is maintained, but ability to resolve large deformation is limited
Solution Approach 1:
The patent employs dynamic processing where the point cloud completion network and point certainty estimation network adaptively process input data of varying complexity. The system dynamically adjusts the level of processing based on the deformation magnitude and image quality, maintaining fast processing speeds for simple cases while handling large deformations when necessary, thus resolving the contradiction between speed and adaptability.
Data Source
AI summary
Systems and methods for registering a plurality of medical images are provided. A plurality of partial point clouds of anatomical objects is received. Each of the plurality of partial point clouds is extracted from a respective one of a plurality of medical images. A set of missing points is generated for each of the plurality of partial point clouds. A measure of certainty is generated for each point in 1) the plurality of partial point clouds and 2) the sets of missing points. One or more transformations aligning the plurality of partial point clouds is determined based on the measures of certainty using a machine learning based point registration network for registering the plurality of medical images. The one or more transformations are output.