Image Segmentation Using ML Shape Descriptors for MBS Initialization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image segmentation methods, such as Model-Based Segmentation (MBS) and machine learning (ML) approaches, face challenges in accurately segmenting anatomical structures that fall outside their respective shape constraints, leading to spurious or non-smooth results.
Innovation Solution
A method that combines ML segmentation with MBS by using a machine learning model to predict shape descriptors for initializing an MBS model, thereby aligning the MBS model with the actual anatomical shape, allowing for a more accurate fit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used for image segmentation, then segmentation accuracy is improved, but computational resources and processing time are increased
Solution Approach 1:
The patent divides the image segmentation task into multiple stages: a coarse segmentation stage using a deep learning model to identify potential objects, and a refinement stage using graph cut algorithms to precisely segment each object. This segmentation of the processing pipeline allows the system to achieve high accuracy while reducing overall computational burden by avoiding the need to process every pixel through the most computationally intensive methods.
Solution Approach 2:
The patent applies deep learning models selectively only to regions that require high accuracy (such as medical images or images with specific characteristics), while using simpler, faster algorithms for regions where high precision is not critical. This partial application of computationally intensive methods only where necessary resolves the contradiction between accuracy and computational resource consumption.
2Measurement precision
If deep learning models are used for image segmentation, then segmentation accuracy is improved, but processing time is increased
Solution Approach 1:
The processing pipeline is segmented into two distinct stages: a fast coarse segmentation stage that quickly identifies object regions, and a more time-consuming refinement stage that applies graph cut algorithms only to the identified regions. This temporal segmentation of processing stages reduces overall processing time by avoiding the application of slow deep learning models to entire images when faster methods suffice.
Solution Approach 2:
The system performs preliminary coarse segmentation using deep learning models to identify potential objects and their locations before applying more time-consuming refinement algorithms. This preliminary action filters out regions that do not require high-precision segmentation, thereby reducing the total processing time by avoiding unnecessary computation on the entire image.
3Productivity
If traditional algorithms like graph cut are used, then processing speed is maintained, but segmentation accuracy decreases
Solution Approach 1:
The patent segments the segmentation task into a coarse stage using deep learning models that provide fast but imprecise results, followed by a refinement stage using graph cut algorithms that provide high precision. By combining these two approaches in sequence, the system achieves both speed (from the fast coarse segmentation) and accuracy (from the precise refinement), resolving the trade-off between processing speed and segmentation accuracy.
Data Source
Figure 1
Figure 2a~2d
Figure 3
AI summary
In a method of segmenting a feature in an image, an image product related to the image is provided (102) to a model trained using a machine learning process. An indication of a shape descriptor for the feature in the image is received (104) from the model, based on the image product. The indicated shape descriptor is then used (106) in a model based segmentation, MBS, to initialize the MBS and segment the feature.