Deep Learning Segmentation Editing for Smooth Medical Image Boundaries
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Solution Overview
Problem
Current automatic image segmentation methods in medical imaging are inaccurate due to poor input image quality, discrepancies in training and testing distributions, and improper acquisition protocols, and existing user input methods require precise corrections that do not provide smooth transitions.
Innovation Solution
A system and method using deep learning to train a model on user inputs and initial segmentations, allowing for iterative editing of medical image segmentations with minimal user input, leveraging a large dataset to improve segmentation accuracy and smoothness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional automatic segmentation methods are used, then the process is fully automated, but the segmentation accuracy is limited due to poor input image quality and distribution discrepancies
Solution Approach 1:
The patent introduces an intermediary editing interface between automatic segmentation and final output. A machine learning model generates initial segmentation, which is then refined through a user-friendly editing tool that provides smooth transitions and automatic boundary adjustment, finally outputting the refined segmentation. This intermediary step resolves the contradiction by maintaining automation while improving accuracy through controlled user interaction and advanced interpolation algorithms.
Solution Approach 2:
The system implements feedback mechanisms where user edits to the segmentation are automatically processed and reflected in subsequent rendering. The machine learning model's initial segmentation provides feedback to guide user editing, and user corrections feed back into the system to refine the final output. This bidirectional feedback loop enables both automation and high accuracy by continuously improving the segmentation based on user input and system analysis.
2Ease of operation
If manual editing tools are provided for user correction, then user control is enabled, but the user input must be very accurate and smooth transitions are not provided
Solution Approach 1:
The patent replaces manual mechanical editing operations with an intelligent system that uses machine learning models and automatic boundary adjustment algorithms. Instead of requiring users to manually draw precise boundaries, the system substitutes automated computational methods that analyze image features and generate smooth transitions automatically. This substitution maintains ease of operation while dramatically improving boundary smoothness and precision.
Solution Approach 2:
The system dynamically adjusts boundary parameters such as smoothness, thickness, and curvature based on local image characteristics and user input. When users provide rough edits, the system automatically modifies boundary parameters to achieve smooth transitions. This parameter adjustment occurs in real-time during the editing process, allowing users to operate easily while the system ensures manufacturing precision through automatic parameter optimization.
3Measurement precision
If more user input is required for accurate segmentation, then segmentation quality improves, but the cost and time of the editing process increases
Solution Approach 1:
The patent implements partial action by allowing users to provide minimal, rough input rather than requiring complete precise boundaries. The machine learning model generates an initial segmentation that is already quite accurate, and users only need to make minor corrections where needed. The system then automatically fills in the remaining details and ensures smooth transitions. This partial user input approach achieves high segmentation quality while minimizing editing time and effort.
Solution Approach 2:
The system performs preliminary segmentation using machine learning models before user editing occurs. This preliminary action produces a high-quality initial segmentation that requires minimal user correction. By doing the heavy computational work beforehand, the system reduces the time users need to spend on editing while maintaining high segmentation quality. The preliminary segmentation serves as a strong foundation that accelerates the overall process.
Data Source
AI summary
Systems and methods for generating an updated segmentation of an initial segmentation are provided. An initial segmentation of an anatomical object from an input medical image is received. User input modifying the initial segmentation is received. An updated segmentation of the anatomical object in the input medical image is generated using a machine learning based network based on at least one of the initial segmentation and the user input. The updated segmentation is output.