Automated Axillary Lymph Node Segmentation with Live Feedback
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Solution Overview
Problem
Current methods for assessing axillary lymph node (ALN) status in breast cancer patients, such as Sentinel lymph node biopsy, are invasive and prone to human error, while imaging modalities like ultrasound and MRI are operator-dependent and time-consuming, necessitating a more efficient and reproducible approach.
Innovation Solution
A segmentation system and method that uses live feedback to define a search region, sample, edge filter, and construct a directed graph for image segmentation, allowing user input for final boundary adjustments, thereby reducing human error and improving reproducibility in measuring ALN dimensions from ultrasound and MRI images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual measurement by radiologists is used, then flexibility and adaptability are maintained, but measurement precision and reliability deteriorate due to human error and subjectivity
Solution Approach 1:
The system performs automatic measurements of ALN dimensions by processing medical images through computer algorithms, eliminating the need for manual radiologist measurements. The algorithm independently identifies lymph node boundaries and computes dimensional parameters, providing objective and reproducible results without human intervention in the measurement process.
Solution Approach 2:
The patent replaces the mechanical/manual measurement process performed by radiologists with an automated computer-based image processing system. The system uses digital image analysis algorithms to automatically detect, segment, and measure lymph node dimensions, substituting human manual operations with computational processes that eliminate subjectivity and improve measurement consistency.
2Measurement precision
If automated image processing is used, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The image processing system is divided into distinct functional modules: image acquisition module, preprocessing module (including normalization and filtering), segmentation module (using thresholding and edge detection), measurement module (computing dimensional parameters), and output module. This modular segmentation allows each component to be optimized independently while maintaining overall system precision.
Solution Approach 2:
The system performs preliminary preprocessing operations on medical images before measurement, including intensity normalization, noise filtering, and contrast enhancement. These preparatory steps are automatically executed to standardize input images, ensuring consistent measurement results while simplifying the subsequent segmentation and measurement processes.
3Reliability
If multiple imaging modalities are used, then measurement reliability improves through cross-validation, but loss of time increases due to processing multiple image sets
Solution Approach 1:
The system integrates multiple imaging modalities (ultrasound, MRI, CT) into a unified measurement platform that processes different image types through the same automated algorithmic framework. By merging the processing pipelines, the system can handle multiple modalities simultaneously without requiring separate manual measurement procedures for each, reducing overall processing time while maintaining the reliability benefits of multi-modality assessment.
Solution Approach 2:
The measurement system is designed with universal functionality to process various imaging modalities using a common set of algorithms and measurement protocols. The system can automatically adapt to different image types (ultrasound, MRI, CT) and perform consistent dimensional measurements across all modalities, eliminating the need for modality-specific manual processing and improving efficiency.
Data Source
AI summary
A segmentation system and method include defining (14) a search region by selecting two locations in an image of a structure to be segmented and sampling the search region to provide an unwrapped image. The unwrapped image is edge filtered (20) to determine likely boundary pixels in the image. A directed graph is constructed (22) for the unwrapped image by determining nodes of the graph by computing (24) a lowest cost between boundary pixels. A potential segmentation is generated (26) for user approval using live images. In accordance with a trace by the user, nodes are connected (32) for a final segmentation of the image of the structure to be segmented.