Lesion Detection CNN for CT and MRI Analysis
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Solution Overview
Problem
Current computer-aided detection (CADe) systems for identifying lesions in lung and liver CT scans are inefficient due to reliance on multi-stage approaches requiring user input, leading to inaccuracies and increased diagnostic time.
Innovation Solution
A machine learning system utilizing a fully convolutional neural network (CNN) for automated lesion detection, segmentation, and longitudinal identification, which receives labeled image sets for training and can segment lesions without iterative clinician input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-stage CADe approaches are used for lesion detection, then detection accuracy can be improved through multiple filtering stages, but the system complexity increases and requires iterative user input
Solution Approach 1:
The patent merges multiple detection stages into a single unified CNN architecture that performs detection, classification, and segmentation simultaneously. The network integrates multiple filtering functions that were previously separate iterative stages into one cohesive model, eliminating the need for repeated user input while maintaining detection accuracy.
Solution Approach 2:
The CNN model serves multiple functions simultaneously: it detects lesions, classifies them as benign or malignant, and segments them into 3D structures. This multi-functional approach replaces multiple specialized tools that would traditionally be used in sequence, reducing overall system complexity while improving efficiency.
2Measurement precision
If multi-stage approaches with iterative user input are used, then detection specificity can be improved by allowing clinician review, but diagnostic time increases
Solution Approach 1:
The system performs self-service by automatically generating detection results, classifications, and segmentations without requiring iterative clinician input. The CNN model independently completes the full detection pipeline in a single pass, providing immediate results while maintaining high specificity through its trained discrimination capabilities.
Solution Approach 2:
The network performs preliminary detection and classification actions automatically during the single forward pass through the CNN. By pre-processing and filtering candidates in advance within the network architecture itself, the system eliminates the need for subsequent iterative review cycles, reducing diagnostic time while maintaining accuracy.
3Productivity
If automated segmentation is implemented, then productivity increases by reducing manual measurement time, but measurement precision may decrease without clinical verification
Solution Approach 1:
The patent replaces manual mechanical segmentation processes with an automated CNN-based segmentation system. The network learns to accurately delineate lesion boundaries through training on annotated data, performing the segmentation function that previously required manual radiologist intervention. This substitution dramatically increases productivity while maintaining precision through the network's learned spatial reasoning capabilities.
Data Source
AI summary
Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.