Deformable Model Segmentation for Complex Anatomical Structures
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Solution Overview
Problem
Current medical image processing techniques face challenges in accurately segmenting complex anatomical structures like the apex of the heart or the tip of the liver due to their difficulty in detection within medical images.
Innovation Solution
The use of a deformable model adjusted by known anatomical features and external information, such as stable anatomical structures and distances, to improve segmentation reliability and accuracy, incorporating spring energy and external energy to adapt the model shape.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deformable models are used for segmentation, then segmentation efficiency is improved, but segmentation reliability deteriorates for complex anatomical structures
Solution Approach 1:
The patent combines model-based segmentation with image-based segmentation by integrating a deformable model with image gradient information. The deformable model provides initial segmentation efficiency while image gradients provide additional constraints to improve reliability for complex structures like the apex of the left ventricle.
Solution Approach 2:
The patent changes the parameters of the deformable model by incorporating image gradient information as additional constraints. This modifies the energy function of the deformable model to include both model smoothness terms and image gradient alignment terms, improving segmentation reliability without sacrificing efficiency.
2Productivity
If deformable models are used for segmentation, then segmentation speed is improved, but measurement precision deteriorates for difficult-to-detect features
Solution Approach 1:
The patent merges deformable model-based segmentation with gradient-based segmentation. The deformable model provides fast initial segmentation while gradient information provides precise feature location, achieving both speed and precision for difficult-to-detect features.
Solution Approach 2:
The patent uses gradient information as feedback to adjust the deformable model parameters. The gradient-based force acts as a feedback mechanism that continuously refines the segmentation position, improving measurement precision while maintaining the speed advantage of model-based methods.
3Reliability
If additional anatomical information is incorporated, then segmentation reliability is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by using gradient information specifically at the boundaries of anatomical structures where it is most needed. Rather than processing the entire image uniformly, the method focuses computational effort on edge regions, improving reliability without proportionally increasing overall complexity.
Solution Approach 2:
The patent performs preliminary action by using the deformable model to generate an initial segmentation before applying gradient-based refinement. This preliminary segmentation provides a good starting point that reduces the computational burden of subsequent refinement steps, managing complexity while improving reliability.
Data Source
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AI summary
An image processing apparatus (16) is disclosed for segmenting a region of interest (15) in a multi-dimensional image data of an object (12). The image processing apparatus comprises an interface for receiving an image data of the object including the region of interest to be segmented. A selection unit selects a deformable model 30 of an anatomical structure corresponding to the object in the image data. A processing unit segments the region of interest by adapting the deformable model on the basis of the image data (xt) and additional information of the object.