Mitral Valve Shape Estimation via Probability Maps
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Solution Overview
Problem
Current medical image processing technologies face challenges in accurately estimating the shape of specific anatomical regions, such as the mitral valve, in three-dimensional medical images, which is crucial for precise diagnostics and treatment planning.
Innovation Solution
A medical image processing apparatus and method that acquires three-dimensional medical images, utilizes machine learning models like 3D U-Net and DenseNet to calculate the existence probability and shape of the mitral valve by processing voxel information and probability maps, enabling accurate shape estimation and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used to estimate the shape of anatomical regions, then the processing is simpler and faster, but the estimation accuracy is insufficient for precise diagnostics
Solution Approach 1:
The patent segments the shape estimation task into two distinct processing stages: (1) existence probability calculation that determines where the anatomical region is located, and (2) shape estimation that determines the precise boundaries. This segmentation allows each stage to be optimized independently, with the first stage providing robust localization and the second stage delivering precise shape delineation, thereby resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The patent introduces an intermediary probability map that serves as a bridge between the input medical image and the final shape estimation. This probability map encodes the likelihood of anatomical region presence at each spatial location, acting as an intermediate representation that guides the subsequent shape estimation process and improves overall accuracy without requiring the final stage to process raw image data directly.
2Measurement precision
If machine learning models are applied to calculate existence probability and shape, then the estimation accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent divides the computational workload into two sequential machine learning stages: existence probability calculation and shape estimation. By segmenting the task, each model can be optimized for its specific function with appropriate complexity, avoiding the need for a single overly complex model that would process all aspects of shape estimation from raw images, thus reducing overall processing time while maintaining high accuracy.
Solution Approach 2:
The patent performs preliminary action by calculating the existence probability map before conducting the final shape estimation. This preliminary calculation identifies and localizes the anatomical region of interest, allowing the subsequent shape estimation model to focus computational resources only on relevant regions rather than processing the entire image, thereby significantly reducing processing time while maintaining precision.
3Reliability
If multiple processing stages are implemented for shape estimation, then the estimation robustness improves, but the system complexity and difficulty of implementation increase
Solution Approach 1:
The patent segments the complex shape estimation problem into two manageable processing stages with clearly defined functions. The first stage (existence probability calculation) handles localization and presence detection, while the second stage (shape estimation) handles boundary delineation. This segmentation improves robustness by ensuring each stage can be independently validated and optimized, while the modular architecture actually simplifies implementation compared to a monolithic system.
Solution Approach 2:
The patent implements feedback mechanisms where the output of the existence probability calculation stage feeds into the shape estimation stage. This feedback loop allows the system to use the probability information to guide and constrain the shape estimation process, improving robustness by ensuring that shape estimates are only generated in regions where the anatomical structure is confidently detected, while maintaining a clear sequential processing flow that simplifies implementation.
Data Source
AI summary
A medical image processing apparatus of an embodiment includes processing circuitry. The processing circuitry acquires an input image including a region of interest of a subject. The processing circuitry acquires information on an existence probability of the region of interest on the basis of the input image. The processing circuitry calculates an estimated value of a shape of the region of interest on the basis of the input image and the information on the existence probability.