User-Guided Bone Segmentation via Landmark Morphing
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Solution Overview
Problem
Current bone segmentation methods in medical imaging are inefficient and costly, relying heavily on large datasets and manual annotation, which is time-consuming and prone to errors, especially in orthopedic surgeries where precise bone analysis is crucial.
Innovation Solution
A user-guided semi-automatic approach that involves selecting landmarks on a bone model to transform and segment bones in medical images, combining initial user input with automatic refinement using graph-based algorithms to improve accuracy and speed, reducing the need for extensive training data and minimizing manual outlining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If learning-based approaches are used for bone segmentation, then automation level increases, but the need for large annotated training datasets increases time and cost
Solution Approach 1:
The patent applies preliminary action by pre-defining a set of anatomical landmarks on a template bone model before actual segmentation. These landmarks serve as prior knowledge that guides the automated segmentation process, eliminating the need to create annotated training datasets from scratch for each new segmentation task.
Solution Approach 2:
The patent uses a template bone model as a copy or representation of the actual bone structure. This template contains pre-established landmark positions that can be reused across different patients and imaging scenarios, replacing the need for extensive patient-specific annotated training data.
2Measurement precision
If manual segmentation is used to depict bone boundaries slice-by-slice, then segmentation accuracy can be controlled, but the process becomes very tedious and time-consuming
Solution Approach 1:
The patent makes the segmentation system universal by using a standardized template bone model with defined landmarks that can be applied across different patients, bone types, and imaging modalities. This universal template approach maintains accuracy while dramatically improving productivity compared to manual slice-by-slice segmentation.
Solution Approach 2:
The patent transforms the segmentation problem from a complex boundary-detection task into a simpler parameter-matching task. By representing bone geometry through landmark coordinates and transform parameters rather than detailed boundaries, the system achieves accurate segmentation much more efficiently.
3Extent of automation
If template-based methods with deformable model registration are used, then automation level increases, but the complexity of matching landmarks to model points increases
Solution Approach 1:
The patent segments the complex landmark matching problem into two simpler sub-problems: (1) identifying a small set of key anatomical landmarks on the patient's bone, and (2) matching these landmarks to corresponding points on the template model. This segmentation of the matching task reduces overall complexity compared to attempting to match all boundary points simultaneously.
Solution Approach 2:
The patent introduces transform parameters as an intermediary between landmark positions and model deformation. Rather than directly deforming the model to pass through all landmarks, the system uses transform parameters (rotation, translation, scaling) as intermediaries that simplify the matching process while maintaining accuracy.
Data Source
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AI summary
A semi-automatic approach is used for user-guided bone segmentation (132) in medical imaging. The user indicates (126) a plurality of landmarks on an image of the bone. A processor morphs (128) the same landmarks on a model of the bone to the user input landmarks on the image, resulting in a transform. This transform is then applied (130) to the model to roughly segment the bone. The user may edit (134) the resulting fit, and the processor then refines (136) the edited fit, such as fitting the bone model to the scan data and avoiding any overlap with other bones. This user-guided segmentation may avoid the need for many samples to train a classifier for a fully automated approach while also avoiding the tedious outlining in a manual approach.