Reference-Aligned 3D Shape And Appearance Modeling
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Solution Overview
Problem
The challenge in generating a statistical shape and appearance model (SSAM) is the computational intensity of handling large volumes of 3-dimensional medical images, alignment of images from different reference points, and combining shape and intensity variations into a model, which is hindered by data costs, acquisition time, and privacy restrictions.
Innovation Solution
An image-based approach that re-orientates training masks and backgrounds to align with a reference mask, computes displacement fields, and reduces dimensionality using principal component analysis to generate a statistical shape and appearance model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large numbers of clinical imaging data sets are acquired to construct computational models, then model accuracy and reliability are improved, but data acquisition cost and time increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-processing and re-orienting training masks to align with a reference mask before model construction. This preparation step organizes the data in advance, enabling faster subsequent processing and reducing the overall data acquisition and preparation time while maintaining model reliability
Solution Approach 2:
The patent extracts only the essential geometric and intensity information from clinical imaging datasets to construct computational models. By taking out only the necessary features (shape and appearance data) rather than processing complete datasets, the method reduces data acquisition and processing time while maintaining sufficient model reliability
2Reliability
If large numbers of clinical imaging data sets are acquired to construct computational models, then model accuracy and reliability are improved, but data acquisition cost increases
Solution Approach 1:
The patent extracts only the essential geometric and intensity information from clinical imaging datasets. By extracting only the necessary shape and appearance features rather than processing complete high-cost datasets, the method reduces data acquisition costs while maintaining sufficient model reliability for in-silico trials
3Adaptability or versatility
If images from different reference points are used to construct models, then data versatility and adaptability are improved, but alignment and registration complexity increase
Solution Approach 1:
The patent applies preliminary action by re-orienting training masks to align with a reference mask before model construction. This pre-alignment step handles the complexity of registering images from different reference points in advance, enabling the model to accept diverse input data while keeping the main processing pipeline simple
Solution Approach 2:
The patent uses a reference mask as an intermediary to align training masks from different reference points. This intermediary reference frame mediates between diverse input images, enabling data versatility while simplifying the alignment process through a standardized reference system
4Productivity
If dimensional reduction is applied to training data, then processing speed and efficiency are improved, but information loss may occur
Solution Approach 1:
The patent extracts and processes only the essential geometric and intensity features from volumetric imaging data. By taking out only the relevant shape and appearance information needed for in-silico trials, the method achieves efficient processing speed while minimizing information loss by excluding unnecessary data
Data Source
AI summary
An image-based approach for statistical shape and appearance modeling includes re-orienting (rotating/translating) training masks to align the training masks with a reference mask to provide corresponding re-orientation parameters, where the training masks represent 3-dimensional shapes of a population of objects and the reference mask represents a 3-dimensional shape of a reference object, deforming the re-oriented training masks based on the reference mask to provide displacement fields indicative of differences between a 3-dimensional shape of the reference mask and 3-dimensional shapes of the re-oriented training masks, re-orienting training backgrounds based on the re-orientation parameters, where the training backgrounds represent volumetric intensity data of the 3-dimensional images of the objects, deforming the re-oriented training backgrounds based on the displacement fields, combining the deformed training backgrounds and the displacement fields, and reducing a dimensionality of the combined deformed training backgrounds and displacement fields to provide a statistical shape and appearance model.