Interactive Neural Network for Medical Volumetric Segmentation
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Solution Overview
Problem
Current fully automated segmentation processes in medical imaging lack interaction and user feedback, leading to suboptimal solutions due to limited data and noisy labels, and are domain-specific, making them inefficient for clinical use.
Innovation Solution
An interactive segmentation process using a neural network that refines estimates based on user feedback, incorporating a click generation protocol to synthesize user inputs and encode them as distance transformations, allowing for guided correction and accurate refinement of segmentation masks in 3D volumetric imaging datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fully automated segmentation processes are used, then productivity is improved, but measurement precision deteriorates due to lack of user feedback and interaction
Solution Approach 1:
The system implements an interactive segmentation process where users provide feedback on segmentation estimates through localized user inputs (clicks, corrections, or confirmations). This feedback loop allows the neural network to iteratively refine its segmentation output, combining automated processing speed with human expertise for high precision. The feedback mechanism resolves the contradiction by enabling both rapid automated segmentation and accurate correction when needed.
2Measurement precision
If manual segmentation is performed, then measurement precision is improved, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The neural network performs self-service by automatically generating segmentation estimates without requiring complete manual annotation. The system processes volumetric imaging datasets autonomously, producing initial segmentation results that can be quickly reviewed and corrected by users. This self-service capability maintains high productivity while allowing precision through selective human intervention only where necessary.
3Measurement precision
If domain-specific segmentation models are used, then measurement precision is improved for specific tasks, but adaptability deteriorates making them inefficient for general clinical use
Solution Approach 1:
The system employs a universal neural network architecture designed to handle multiple segmentation tasks across different anatomical regions and imaging modalities. The model incorporates generalizable features and transfer learning capabilities, allowing it to adapt to various clinical scenarios without requiring complete retraining. This universality enables the system to maintain high precision across diverse applications while improving adaptability for general clinical use.
Data Source
AI summary
A computer-implemented method comprises: performing an interactive segmentation process to determine a segmentation of a target volume depicted by a volumetric imaging dataset, the interactive segmentation process including multiple iterations. Each iteration of the interactive segmentation process includes: determining, using a neural network algorithm, a respective estimate of the segmentation; and obtaining, from a user interface, one or more localized user inputs correcting or ascertaining the respective estimate of the segmentation. The neural network algorithm includes multiple inputs, wherein the multiple inputs include an estimate of the segmentation determined in a preceding iteration of the multiple iterations, an encoding of the one or more localized user inputs obtained in the preceding iteration, and the volumetric imaging dataset.