Medical Image Segmentation Using Parameter-Space Spatial Prompts
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Solution Overview
Problem
Current deep-learning-based image segmentation models face challenges in adapting to clinical workflows, with high latency, workflow complexity, and poor reproducibility due to user-defined spatial prompts, leading to suboptimal treatment decisions and increased workload.
Innovation Solution
Transform medical image data into parameter space to generate spatial prompts based on parameter space selection criteria, allowing for rapid, repeatable, and reproducible segmentation using promptable embedding-based models like vision transformers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or semi-automated segmentation methods are used, then segmentation accuracy can be high, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system performs preliminary processing by transforming medical image data into parameter space and generating spatial prompts before the main segmentation task. This preliminary action prepares the data in an optimized format that enables faster automated segmentation while maintaining accuracy, resolving the contradiction between high accuracy and time efficiency
Solution Approach 2:
The patent introduces parameter space as an intermediary representation between the original medical images and the final segmentation output. By transforming images into parameter space and using this as a mediator for generating spatial prompts, the system achieves both high segmentation accuracy and reduced processing time, avoiding the need for labor-intensive manual methods
2Productivity
If fully automated deep learning models are used, then segmentation speed is fast, but accuracy varies significantly depending on training data quality and model architecture
Solution Approach 1:
The patent transforms the input data from image space to parameter space, changing the representation parameters of the medical images. This parameter transformation enables the automated model to achieve both high speed and high accuracy by representing the data in a form that better captures the essential features for segmentation while maintaining computational efficiency
3Adaptability or versatility
If user-defined spatial prompts are used for segmentation, then adaptability to specific cases is improved, but workflow complexity increases and reproducibility decreases
Solution Approach 1:
The system performs self-service by automatically generating spatial prompts from the medical image data itself through parameter space transformation. Instead of requiring external user input, the system autonomously creates the prompts needed for segmentation, thereby maintaining case-specific adaptability while eliminating workflow complexity and improving reproducibility
4Ease of manufacture
If conventional CNN-based models are used, then implementation is straightforward, but performance and segmentation latency are problematic for small lesions
Solution Approach 1:
The patent transitions from the conventional image space to parameter space, adding a new dimensional perspective to the data representation. This dimensional change enables the model to better detect small lesions and improve segmentation reliability while maintaining ease of implementation through the systematic transformation process
Data Source
AI summary
Systems and methods are provided for performing segmentation of medical image data based on generating a spatial prompt for a promptable embedding-based segmentation model, such as, for example, an interactive vision transformer based segmentation model. At least a subset of a medical image dataset is transformed into a parameter space representation, where the dataset is processed to select a set of voxels satisfying parameter space selection criteria associated with one or more target substances (e.g. a target tissue, fluid or material). The resulting selected set of voxels is back-projected into image space, and employed to generate a region selection dataset for use as a spatial prompt for the promptable embedding-based segmentation model. The region selection dataset is provided as a spatial prompt to the promptable embedding-based segmentation model, and the promptable embedding-based segmentation model is employed to process the medical image dataset to determine a segmentation.