Deep Learned Graph Cut Segmentation for Lung Nodules
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Solution Overview
Problem
Current automated lung nodule segmentation from 3D chest computed tomography (CT) images faces challenges due to varying nodule appearances, noise, reconstruction artifacts, and overlapping pulmonary structures, lacking effective methods to suppress non-nodule structures and extract relevant features for characterization.
Innovation Solution
A hybrid segmentation algorithm, Deep Learned Graph Cut (DLGC), incorporates deep learned features into a model-based energy minimization framework using graph cuts, combining radial gradient data terms with deep learned probability maps to enhance segmentation accuracy and robustness, particularly in handling noise and imaging artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional segmentation methods are used, then the segmentation process is simpler, but segmentation accuracy deteriorates due to noise and imaging artifacts
Solution Approach 1:
The patent combines deep learning-based probability map generation with graph-cut energy minimization segmentation into a unified framework. The deep learned probability map provides robustness to noise and artifacts, while the graph-cut method ensures accurate boundary delineation. This merging of two different approaches (deep learning and traditional energy minimization) resolves the contradiction by achieving high segmentation accuracy without requiring overly complex algorithms.
Solution Approach 2:
The patent performs preliminary generation of a deep learned probability map before executing the graph-cut segmentation. This pre-computed probability map serves as an energy term in the graph-cut optimization, guiding the segmentation process toward accurate boundaries while being robust to noise. The preliminary action of creating the probability map allows the subsequent segmentation to focus on precise boundary detection rather than dealing with noise and artifacts from scratch.
2Reliability
If deep learning methods are used for segmentation, then robustness to noise and artifacts improves, but computational time increases
Solution Approach 1:
Instead of using full deep learning segmentation which would be computationally expensive, the patent applies deep learning partially to generate only the probability map that serves as an energy term. This partial application of deep learning provides robustness to noise and artifacts while significantly reducing computational time compared to end-to-end deep learning segmentation. The graph-cut method then efficiently completes the segmentation using this pre-computed guidance.
3Ease of operation
If automated segmentation is implemented, then operator variability is reduced, but difficulty in handling varying nodule appearances increases
Solution Approach 1:
The patent uses deep learning to automatically adapt segmentation parameters based on the specific characteristics of each nodule appearance. The deep neural network learns to adjust energy terms in the graph-cut formulation according to varying nodule types (solid, part-solid, ground-glass), sizes, and locations. This automatic parameter adaptation enables the automated system to handle diverse nodule appearances effectively while maintaining consistency and reducing operator variability.
Data Source
AI summary
Embodiments described herein provide a hybrid technique which incorporates learned pulmonary nodule features in a model based energy minimization segmentation using graph cuts. Features are extracted from training samples using a convolutional neural network, and the segmentation cost function is augmented via the deep learned energy. The system and method improves segmentation performance and more robust initialization.