FCN-Driven Deformable Model for Echocardiography Segmentation
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Solution Overview
Problem
Deep learning approaches fail to produce accurate and reliable segmentation in echocardiography image sequences due to speckle noise, ill-defined boundaries, and overfitting, particularly in ultrasound images of the heart, where the motion of the heart and dynamics of the mitral valve complicate anatomical structure segmentation.
Innovation Solution
Combining deep learning with a shape-guided deformable model using level sets, where an end-to-end trained fully convolutional network acts as a prior to drive the level sets-based deformable model, accounting for the characteristics of the desired cardiac structure through an energy formulation that minimizes errors and handles noise and motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If deep learning approaches are used for segmentation, then automation is improved, but measurement precision deteriorates due to speckle noise and ill-defined boundaries
Solution Approach 1:
The method segments the echocardiography image processing into two distinct stages: first, a fully convolutional network generates preliminary segmentations automatically; second, a deformable model with shape constraints refines these segmentations. This two-stage segmentation approach maintains automation while improving precision by separating automated feature extraction from precision-boundary refinement.
Solution Approach 2:
The deformable model acts as an intermediary between the automated deep learning network and the final precise segmentation. It takes the preliminary segmentation output and mediates the refinement process by incorporating shape constraints and boundary information, thereby improving measurement precision without eliminating automation.
2Productivity
If deep learning approaches are used for segmentation, then productivity is improved, but reliability deteriorates due to overfitting and noise sensitivity
Solution Approach 1:
The deformable model with shape constraints serves as a preparatory cushioning layer that prevents the deep learning network from producing unreliable segmentations. By incorporating prior knowledge of cardiac anatomy and boundary characteristics into the deformable model, the system cushions against overfitting and noise sensitivity before final segmentation results are generated, thereby improving reliability while maintaining productivity.
3Measurement precision
If a deformable model with shape constraints is applied, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The fully convolutional network performs preliminary segmentation action first, generating initial boundary estimates that guide the subsequent deformable model. This preliminary action reduces the search space for the deformable model, allowing it to focus computational resources on refining boundaries with shape constraints rather than exploring all possible segmentations, thereby improving boundary accuracy without proportionally increasing system complexity.
4Measurement precision
If a two-stage approach combining deep learning and deformable models is used, then segmentation accuracy is improved, but loss of time increases
Solution Approach 1:
The deformable model applies shape constraints and boundary refinement only to regions where the preliminary segmentation from the deep learning network shows uncertainty or potential errors. Rather than processing the entire image with the computationally intensive deformable model, the system applies partial action only where needed, thereby improving segmentation accuracy without proportionally increasing processing time.
Data Source
AI summary
Image segmentation based on the combination of a deep learning network and a shape-guided deformable model is provided. In various embodiments, a time sequence of images is received. The sequence of images is provided to a convolutional network to obtain a sequence of preliminary segmentations. The sequence of preliminary segmentations labels a region of interest in each of the images of the sequence. A reference and auxiliary mask are generated from the sequence of preliminary segmentations. The reference mask corresponds to the region of interest. The auxiliary mask corresponds to areas outside the region of interest. A final segmentation corresponding to the region of interest is generated for each of the sequence of images by applying a deformable model to the composite mask with reference to the auxiliary mask.