Medical Image Segmentation Input Validation and Output Refinement
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Solution Overview
Problem
Conventional medical image segmentation techniques often produce inaccurate contours due to the lack of input and output data usability checks, especially when dealing with imaging artifacts, multiple structures in a single image, and partially covered structures.
Innovation Solution
A holistic, automated segmentation process that includes input data preparation and checking, optimization within the segmentation process, and additional output checking and optimization, using a method that receives an input dataset of medical images, a structure list, and a segmentation protocol, performs input checks, segments structures using appropriate models, and processes segmentations for quality and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional segmentation techniques are used without input and output data checks, then the segmentation process is faster and simpler, but the accuracy of contours is reduced due to imaging artifacts and incomplete structure coverage
Solution Approach 1:
The patent applies preliminary action by performing input data checks before segmentation and output checks after segmentation. The input check validates image quality, artifacts, and structure coverage before processing, while the output check verifies segmentation accuracy. This preliminary validation approach improves segmentation accuracy without significantly increasing overall process complexity.
Solution Approach 2:
The patent implements feedback mechanisms through iterative refinement processes. The output check provides feedback on segmentation quality, and when issues are detected (such as artifacts or incomplete coverage), the system refines the segmentation by adjusting parameters or re-processing specific regions. This feedback loop continuously improves measurement precision while maintaining manageable process complexity.
2Productivity
If multiple structures are segmented in a single image without validation, then processing efficiency is maintained, but errors increase due to overlapping structures and partial coverage
Solution Approach 1:
The patent performs preliminary validation checks on input images to assess the presence of multiple structures, artifacts, and coverage completeness before segmentation. This preliminary assessment allows the system to prepare appropriate processing strategies for each image, ensuring reliable segmentation of multiple structures while maintaining processing efficiency through automated workflow management.
Solution Approach 2:
The output check mechanism provides feedback on the quality of segmented structures, identifying errors such as overlapping contours or incomplete boundaries. When reliability issues are detected, the system automatically refines the segmentation by adjusting parameters or re-processing affected regions, thereby improving segmentation reliability without requiring manual intervention for each case.
3Ease of manufacture
If input images with artifacts and poor quality are processed, then no data preprocessing is needed, but segmentation accuracy deteriorates due to imaging artifacts and insufficient image quality
Solution Approach 1:
The patent implements a comprehensive input check that automatically assesses image quality metrics, detects artifacts, and evaluates structure coverage before segmentation processing. This preliminary validation identifies images that require preprocessing or parameter adjustment, ensuring that segmentation is performed only on suitable images or with appropriate quality controls, thereby maintaining both ease of processing and high measurement precision.
Solution Approach 2:
The output check mechanism provides feedback on segmentation quality, and when images with artifacts or poor quality are processed, the system identifies accuracy issues and automatically refines the segmentation by adjusting parameters or re-processing specific regions. This feedback-driven approach ensures contour accuracy is maintained even when processing challenging images, without requiring manual preprocessing intervention.
Data Source
AI summary
Methods and systems are provided for segmenting structures in medical images. In one embodiment, a method includes receiving an input dataset including a set of medical images, a structure list specifying a set of structures to be segmented, and a segmentation protocol, performing an input check on the input dataset, determining whether each medical image of the set of medical images has passed the input check and removing any medical images from the set of medical images that do not pass the input check to form a final set of medical images, segmenting each structure from the structure list using one or more segmentation models and the final set of medical images, receiving a set of segmentations output from the one or more segmentation models, processing the set of segmentations to generate a final set of segmentations, and displaying and/or saving in memory the final set of segmentations.