Medical Image Segmentation With Probabilistic User Feedback
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Solution Overview
Problem
Existing medical image segmentation methods, both manual and automatic, face challenges in achieving accurate and realistic segmentations, particularly in complex medical procedures like radiotherapy planning, due to the limitations of current deep learning models and the need for interactive user input.
Innovation Solution
A method combining machine learning with manual adjustments using a neural network to approximate the conditional probability distribution of segmentations, allowing for iterative optimization and user input to refine initial segmentations, ensuring they align with medical practitioner expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic segmentation using deep neural networks is used, then productivity is improved, but manufacturing precision deteriorates due to model limitations and averaging effects
Solution Approach 1:
The patent implements feedback by allowing medical practitioners to manually adjust the automatically generated segmentation and provide corrections. The system then uses maximum likelihood estimation to update the segmentation based on this feedback, creating an iterative improvement loop that combines automated efficiency with human expertise for higher precision
Solution Approach 2:
The system performs preliminary automatic segmentation using deep neural networks to generate an initial segmentation result. This preliminary action provides a good starting point that captures most of the segmentation, reducing the manual work required while maintaining high accuracy through subsequent refinement
2Manufacturing precision
If classical machine learning methods like conditional random fields are used, then manufacturing precision is improved through probability distributions, but device complexity increases and productivity decreases
Solution Approach 1:
The patent merges the advantages of deep learning (ability to learn complicated relationships) with probabilistic methods (capability to generate probability distributions over segmentations). By combining a U-net deep neural network with probabilistic segmentation, the system achieves both high precision and reliability without the limitations of using either approach alone
3Productivity
If deep learning methods are used, then productivity is improved, but manufacturing precision deteriorates due to inability to incorporate user feedback interactively
Solution Approach 1:
The patent introduces dynamics by enabling interactive segmentation where the system can adapt to user input in real-time. The model transitions from a static automatic segmentation to a dynamic process where medical practitioners can adjust segmentations and the system updates results interactively, allowing precision to be optimized for each specific clinical case
Data Source
AI summary
A machine learning system may be used for determining if a segmentation of a medical image is a reasonable segmentation in the sense that it is a segmentation that could be made by a human user and does not contain any impossible combinations of pixel values. The method is enhanced by user input to avoid the impossible combinations.