Medical Image Segmentation via Device Metadata Correction
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Solution Overview
Problem
Medical image assessment and tumor segmentation are heavily influenced by variations in image acquisition devices, imaging protocols, display parameters, and physician preferences, leading to inconsistent measurement results across different imaging conditions.
Innovation Solution
A system that learns image correction parameters and optimal segmentation algorithm settings using metadata and training data, including phantom data and clinical studies, to provide consistent segmentation and measurement across various imaging conditions, employing machine learning methods such as genetic algorithms and neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual assessment and segmentation methods are used, then physician expertise and visual review are leveraged, but measurement results become inconsistent across different imaging devices and display parameters
Solution Approach 1:
The system automatically adjusts segmentation parameters and display settings based on the specific imaging device and image characteristics. By dynamically changing parameters such as window width, window level, and segmentation thresholds according to the input image properties, the system maintains consistent measurement results across different CT scanners and display configurations without requiring manual recalibration for each device.
2Measurement precision
If image correction parameters are optimized for each specific imaging device, then segmentation accuracy improves, but system complexity increases due to needing to handle multiple device variations
Solution Approach 1:
The system employs a universal correction framework that can handle multiple imaging devices and protocols through a single integrated approach. Rather than maintaining separate optimization routines for each device model, the system uses a unified parameter adjustment mechanism that adapts to different devices based on their metadata and image characteristics, thereby reducing overall system complexity while maintaining high segmentation accuracy across diverse imaging equipment.
3Reliability
If manual adjustment of segmentation parameters is performed for each image, then adaptation to specific imaging conditions improves, but processing time and labor increase significantly
Solution Approach 1:
The system performs automatic self-adjustment of segmentation parameters by analyzing image metadata and characteristics to determine optimal settings without requiring manual intervention. The automated parameter optimization process evaluates imaging conditions and confidently selects appropriate segmentation thresholds and display parameters, thereby maintaining high segmentation reliability while eliminating the time-consuming manual adjustment process for each image.
4Ease of operation
If display parameters are customized according to physician preferences, then individual viewing comfort improves, but measurement consistency across different physicians decreases
Solution Approach 1:
The system incorporates feedback mechanisms that monitor and adjust display parameters based on both physician preferences and objective image characteristics. By continuously evaluating the interaction between display settings and segmentation results, the system provides feedback to maintain measurement consistency while accommodating reasonable physician preferences for viewing comfort, ensuring that personalization does not compromise measurement reliability.
Data Source
AI summary
Medical imaging device- and display-invariant segmentation and measurement is provided. In various embodiments, a plurality of medical images is read from a data store. Metadata of each of the plurality of medical images is read. The metadata identifies an image acquisition device associated with each of the plurality of medical images. Based on the plurality of medical images and the metadata of each of the plurality of images, a learning system is trained to determine one or more image correction parameters. The one or more image correction parameters optimize segmentation of the plurality of medical images.