Time-Consistent 2D Image Segmentation for Anatomical Structures
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Solution Overview
Problem
Existing techniques for segmenting two-dimensional medical images are inaccurate due to the lack of time-consistent segmentation of contours, as each image is segmented separately without considering temporal variations, leading to incomplete information about the anatomical structure's movement.
Innovation Solution
A method and apparatus that acquire a time sequence of two-dimensional images and apply a segmentation model simultaneously in time and space, allowing for the segmentation of the entire sequence using a plurality of segments, which can adapt shape and topology to accurately represent anatomical structure movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing slice-by-slice segmentation techniques are used, then the segmentation process is simple and computationally efficient, but the segmentation accuracy is poor due to lack of temporal consistency
Solution Approach 1:
The segmentation model is divided into a plurality of segments that can independently deform and adapt to anatomical structures across multiple time points. Each segment can be adjusted individually while maintaining spatial and temporal relationships, allowing accurate tracking of moving structures without requiring complex global optimization at each time step.
Solution Approach 2:
The segmentation approach extends from traditional 2D slice-by-slice processing to 4D segmentation by incorporating the time dimension. The segmentation model operates simultaneously across multiple time points and spatial dimensions, enabling temporal consistency while maintaining segmentation accuracy for moving anatomical structures.
2Loss of information
If temporal smoothing is applied after segmentation, then some temporal consistency is achieved, but incomplete information about temporal variation is still captured during segmentation
Solution Approach 1:
The segmentation model incorporates temporal information from multiple time points during the segmentation process itself, rather than applying temporal smoothing as a post-processing step. By pre-defining the segmentation model across the time sequence and allowing simultaneous deformation across all time points, the method captures complete temporal variation information inherently during segmentation.
Solution Approach 2:
The segmentation approach uses feedback from multiple time points to constrain and guide the segmentation at each individual time point. The model adjusts segments based on temporal relationships and anatomical consistency across the entire time sequence, ensuring reliable segmentation that respects both local image features and global temporal patterns.
Data Source
AI summary
There is provided a method and apparatus for segmenting two-dimensional images of an anatomical structure. A time sequence of two-dimensional images of the anatomical structure is acquired (202) and a segmentation model for the anatomical structure is acquired (204). The segmentation model comprises a plurality of segments. The acquired segmentation model is applied to the entire time sequence of two-dimensional images of the anatomical structure simultaneously in time and space to segment the time sequence of two-dimensional images by way of the plurality of segments (206).