CT Image Segmentation for Lung Nodule Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for automatically classifying lung nodule types in CT imaging are costly, time-consuming, and biased due to the need for comprehensive and balanced databases, especially when dealing with small tumors and varying tissue compositions.
Innovation Solution
A device and method that segment CT image data into components with different Hounsfield density values, simulate the object of interest by assigning component classes based on these values, and iteratively adjust the component ratio to minimize deviation between simulated and actual data, allowing for a low-cost, fast, and unbiased classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive and balanced database is used for training classifiers to cover wide range of different compositions, shapes, and sizes of nodules, then the classification accuracy is improved, but the cost and time to establish the database increases significantly
Solution Approach 1:
The patent creates virtual copies of nodules through computer-generated simulations rather than using real patient data. Synthetic nodule images are generated with controlled characteristics (size, shape, composition, location) to create training databases without requiring extensive manual collection and annotation of real medical images, thus reducing time and resource investment while maintaining comprehensive coverage of nodule variations
Solution Approach 2:
The patent performs preliminary classification by determining nodule center position and composition type before detailed analysis. This preliminary action allows the system to quickly categorize nodules into basic types (ground-glass, part-solid, solid) and only then proceed to more detailed feature extraction and classification, reducing overall processing time while maintaining accuracy
2Reliability
If manual classification of nodule types is performed to ensure accurate tissue composition assessment, then the reliability of malignancy assessment is improved, but the productivity decreases due to time-consuming manual work
Solution Approach 1:
The patent implements automated self-service classification where the computer system independently performs nodule detection, segmentation, composition determination, and classification without requiring manual radiologist intervention for each step. The system automatically extracts features, compares them against reference data, and generates classification results, thereby maintaining high reliability while dramatically improving productivity
Solution Approach 2:
The patent incorporates feedback mechanisms where classification results are continuously refined by comparing automated measurements against established reference ranges and previously classified cases. The system adjusts its classification thresholds and parameters based on performance feedback, ensuring high reliability while maintaining automated high-speed operation
3Ease of manufacture
If the classifier is trained on a small database to reduce establishment cost, then the cost decreases, but the classification becomes biased towards certain nodule types
Solution Approach 1:
The patent employs dynamic sampling strategies where the synthetic training database is continuously adapted and expanded based on the specific characteristics of the data being processed. The system dynamically adjusts the distribution of synthetic nodule examples to ensure balanced representation of all nodule types (ground-glass, part-solid, solid) and various compositions, preventing bias while keeping database establishment costs low through targeted generation rather than comprehensive collection
Solution Approach 2:
The patent systematically varies key parameters (size, shape, composition, density, location) when generating synthetic nodule training data to ensure diverse and balanced representation. By controlling parameter distributions during synthetic data generation, the system creates comprehensive training sets that cover all nodule types without requiring expensive manual database curation, thereby achieving both low cost and high adaptability
Data Source
AI summary
A method for modelling a composition of an object of interest comprises segmenting object of interest image data provided by computer tomography image data resulting in a plurality of image segments. A determined Hounsfield density value is then extracted from the object of interest image data for each image segment. A component ratio of at least two component classes is defined for the object of interest, the at least two component classes having different component Hounsfield density values. At least one component class is assigned to each image segment based on the corresponding determined Hounsfield density value resulting in simulated image segments comprising the component Hounsfield density values. The simulated image segments define simulated image data of the object of interest, where a ratio of the assigned component classes corresponds to the component ratio. A deviation between the simulated image data and the object of interest image data is then determined.