Lung Lobe Segmentation via Constellation Modification
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Solution Overview
Problem
Current image processing methods for lung lobe segmentation in CT scans face challenges with low-dose and ultra-low dose scans, which result in high image noise and low resolution, making it difficult to accurately define fissure lines due to insufficient definition of thin pulmonary fissures.
Innovation Solution
An image processing system that modifies a set of anchor points in the input image to create a modified constellation by varying coordinates and projecting them onto a hyperplane, with a constellation evaluator scoring the suitability of the modified constellation to define a segmentation, allowing for accurate segmentation in low-resolution and high-noise imagery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If low-dose or ultra-low dose CT scans are used for lung screening, then radiation exposure is reduced and cost is lowered, but image noise increases and resolution decreases making fissure segmentation difficult
Solution Approach 1:
The system performs preliminary actions by pre-defining anchor points and constellations that represent expected fissure locations and geometries before processing the low-dose CT image. These pre-established geometric models guide the subsequent segmentation process, allowing the system to identify fissures even when the image quality is degraded by noise and low resolution.
Solution Approach 2:
The patent introduces intermediary structures including anchor points, constellations, and hyperplanes as mediators between the low-quality image data and the desired segmentation output. These intermediaries serve as geometric constraints and reference frames that bridge the gap between insufficient image information and accurate fissure detection, enabling reliable segmentation despite high noise and low resolution.
2Productivity
If existing segmentation methods are applied to low-dose CT scans, then processing can be performed, but segmentation accuracy deteriorates due to high noise and low resolution
Solution Approach 1:
The patent replaces traditional image processing mechanisms that rely on direct pixel-based analysis with a geometric constraint-based approach. Instead of mechanically processing image pixels to identify fissures, the system substitutes this with a geometric model-driven method where anchor points and constellations define fissure locations independently of image quality, thereby maintaining segmentation accuracy despite high noise and low resolution.
Solution Approach 2:
The system changes the fundamental parameters of the segmentation approach by transitioning from image intensity-based parameters to geometric parameter-based segmentation. By defining fissures through spatial relationships, anchor point configurations, and hyperplane geometries rather than relying on image pixel values, the method achieves robust segmentation accuracy that is insensitive to noise and resolution limitations inherent in low-dose CT scans.
3Power
If traditional image processing algorithms are used, then computation can be performed, but computational efficiency decreases when dealing with high noise and low resolution data
Solution Approach 1:
The patent extracts the essential geometric information needed for segmentation from the complex task of processing entire low-dose CT images. By separating the segmentation problem into independent geometric components (anchor points, constellations, hyperplanes), the system extracts only the necessary computational elements, significantly reducing the computational burden while maintaining segmentation accuracy in the presence of high noise and low resolution.
Data Source
AI summary
An image processing system and related method. The system comprises an input interface (IN) configured for receiving an n[≥2]-dimensional input image with a set of anchor points defined in same, said set of anchor points forming an input constellation. A constellation modifier (CM) is configured to modify said input constellation into a modified constellation. A constellation evaluator (CE) configured to evaluate said input constellation based on said hyper-surface to produce a score. A comparator (COMP) is configured to compare said score against a quality criterion. Through an output interface (OUT) said constellation is output if the score meets said criterion. The constellation suitable to define a segmentation for said input image.