Prostate Boundary Segmentation Using Demographic Models and Narrow Band Processing
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Solution Overview
Problem
The segmentation of medical images, particularly ultrasound images of the prostate, is challenging due to poor image quality and the presence of artifacts, making real-time boundary identification and accurate volume determination impractical.
Innovation Solution
A system and method that uses predetermined models based on demographic information and stored boundary data to provide an initial estimate of the prostate boundary, followed by a narrow band region processing with active contours to capture the actual boundary, minimizing an energy function using a level set framework for automated segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation techniques are used to identify prostate boundaries in ultrasound images, then measurement precision can be improved, but the time required for segmentation increases significantly and real-time imaging becomes impractical
Solution Approach 1:
The system performs preliminary segmentation using a predetermined model based on demographic information before the actual imaging procedure. This initial boundary estimate is generated in advance, allowing the detailed segmentation to be completed much faster during the actual procedure, thus resolving the time-precision contradiction.
Solution Approach 2:
The segmentation process is divided into multiple stages: first generating an initial boundary estimate using a predetermined model, then refining this estimate by processing only a narrow band region around the initial boundary. This multi-stage segmentation approach reduces the overall computation time while maintaining precision.
2Measurement precision
If the entire image is processed for segmentation to ensure accurate boundary identification, then measurement precision is improved, but the complexity of the processing system and computational resources increase
Solution Approach 1:
Instead of processing the entire image with high computational resources, the system applies intensive processing only to a narrow band region surrounding the initial boundary estimate. This local processing approach maintains boundary identification precision while significantly reducing overall system complexity and computational requirements.
3Measurement precision
If manual boundary identification is performed to achieve accurate segmentation, then measurement precision is improved, but the ease of operation decreases and requires skilled technicians
Solution Approach 1:
The system performs automated segmentation using predetermined models and algorithms, eliminating the need for manual intervention by skilled technicians. The computer automatically generates initial boundary estimates and refines them through narrow band processing, making the operation simple while maintaining high precision.
Solution Approach 2:
The manual mechanical process of boundary identification by technicians is replaced with an automated computer-based system using image processing algorithms and level set methods. This substitution maintains measurement precision while dramatically improving ease of operation.
4Measurement precision
If images are segmented after the imaging procedure to ensure accuracy, then measurement precision is improved, but productivity decreases and requires repositioning of imaging devices
Solution Approach 1:
The system generates initial boundary estimates and performs preliminary segmentation during the imaging procedure itself rather than after. This allows for real-time guidance and eliminates the need for repositioning imaging devices, thereby improving productivity while maintaining precision through subsequent refinement.
Data Source
AI summary
An improved system and method (i.e. utility) for segmentation of medical images is provided. The utility fits an estimated boundary on a structure of interest in an automated selection and fitting process. The estimated boundary may be a model boundary that is generated actual boundaries of like structures. In one arrangement, the boundaries may be selected based on the age and/or ethnicity of a patient. In further arrangements, narrow band processing is performed to estimate the actual boundary of the structure of interest.