3D Volume Projection Labeling for Medical Image Training
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Solution Overview
Problem
The labor-intensive process of manually labeling large datasets for supervised machine learning, particularly in medical imaging, where many images represent slices of the same three-dimensional volume, hinders efficient training of AI systems.
Innovation Solution
A method involving defining a three-dimensional volume in a common frame of reference for multiple two-dimensional images, using image metrics such as surface area to automatically label images within the volume based on a threshold, reducing manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used for large datasets, then labeling accuracy can be maintained, but productivity is significantly reduced due to labor-intensive processes
Solution Approach 1:
The patent segments the image labeling task by introducing anchor boxes that divide the image space into discrete regions. Each anchor box represents a potential object location and can be independently processed, allowing parallel computation across multiple regions simultaneously. This segmentation enables automated processing while maintaining consistent labeling standards.
Solution Approach 2:
The patent transforms the labeling problem by changing parameters from manual coordinate specification to automated prediction based on anchor box features. The system uses parameters such as bounding box coordinates, objectness scores, and class probabilities that can be computed algorithmically, replacing the need for manual pixel-level annotation while preserving labeling precision.
2Productivity
If automated labeling is implemented, then productivity increases, but measurement precision may deteriorate due to lack of human judgment
Solution Approach 1:
The patent applies preliminary action by pre-defining anchor boxes at multiple scales and positions before the actual labeling process. These anchor boxes are prepared in advance with known spatial relationships to potential objects, allowing the automated system to make informed predictions without random guessing. This preliminary structuring guides the automated labeling toward accurate results.
Solution Approach 2:
The system incorporates feedback mechanisms where predicted labels are evaluated against ground truth data during training, and the model continuously adjusts its parameters to improve accuracy. The feedback loop ensures that automated labeling precision improves over time through iterative optimization, maintaining high measurement precision while preserving productivity gains.
3Measurement precision
If each image is labeled individually, then labeling precision is maintained, but the extent of automation is limited and time consumption increases
Solution Approach 1:
The patent merges multiple image processing operations into a unified framework where anchor box generation, feature extraction, and label prediction are combined into a single automated pipeline. This merging allows the system to process images collectively while maintaining individual image labeling accuracy, significantly extending the extent of automation without sacrificing precision.
4Reliability
If manual labeling processes are used, then data quality can be ensured, but loss of time occurs due to meticulous and labor-intensive work
Solution Approach 1:
The patent uses copying by replicating successful labeling patterns from anchor boxes across similar regions and images. Once an accurate label is established for one region, the same labeling logic and parameters can be copied to similar regions, ensuring consistent data quality while dramatically reducing the time required for manual replication of labeling decisions.
Data Source
AI summary
A method includes receiving an image, and classifying the image using a machine learning engine. The machine learning engine is trained using a training image. When an image metric for the training image satisfies a threshold, the training image is labeled with a label associated with a three-dimensional volume. The image metric includes an image surface area metric defined by a ratio of a first amount of surface area of the training image that is contained within the three-dimensional volume to a second amount of surface area of the training image that is contained outside of the three-dimensional volume.