Medical Image Label Sharing to Reduce Segmentation Channels
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Solution Overview
Problem
Conventional segmentation models for biomedical imaging require a large number of output channels and parameters, leading to inefficiencies such as catastrophic forgetting and high inference costs, especially when adapting to new tasks.
Innovation Solution
A label sharing model is introduced, where labels across multiple tasks are grouped and shared, reducing the number of channels required and enabling incremental learning without altering the model architecture, using similarity metrics to determine optimal label combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional segmentation models use separate output channels for each label in multiple tasks, then each task can be processed independently, but the number of channels and parameters increases significantly leading to catastrophic forgetting and high inference costs
Solution Approach 1:
The patent merges labels from multiple segmentation tasks into shared label groups, where common anatomical structures across different tasks are grouped together. This consolidation reduces the total number of output channels needed in the segmentation model, directly addressing the contradiction between handling multiple tasks and maintaining model complexity at manageable levels.
Solution Approach 2:
The patent creates a universal segmentation model architecture where a single set of output channels serves multiple tasks simultaneously. By designing the model to share label groups across different anatomical segmentation tasks, the same model structure and parameters can be universally applied to multiple tasks without requiring task-specific channel expansions, thereby preventing catastrophic forgetting.
2Adaptability or versatility
If the segmentation model architecture is altered to accommodate new tasks, then the model can adapt to new anatomies, but the model architecture becomes less stable and requires retraining
Solution Approach 1:
The patent performs preliminary grouping of labels into shared label groups before model training. By pre-organizing labels from multiple tasks into cohesive groups based on anatomical relationships, the model architecture is configured in advance to handle multiple tasks. This preliminary structuring allows the model to adapt to new tasks without requiring architectural modifications, maintaining stability while enabling incremental learning.
3Manufacturing precision
If more output channels are used to cover all labels across multiple tasks, then all regions of interest can be segmented accurately, but computational overhead and inference costs increase
Solution Approach 1:
The patent combines labels from different tasks into shared label groups, reducing the total number of output channels required. This merging maintains segmentation accuracy for all regions of interest across multiple tasks while significantly reducing the computational overhead associated with processing a large number of separate channels, thereby lowering inference costs.
Data Source
AI summary
Various systems and methods are presented regarding segmentation of medical images whereby a segmentation process comprises of channels configured to share labels. Accordingly, rather than each label in a series of images all requiring an individual channel in a segmentation model, the segmentation model can be configured such that a single channel (e.g., having a single group of labels) is shared by images having multiple labels. By sharing a channel, and label group, across multiple labels, the efficiency of the segmentation model is improved as fewer channels are required to segment a series of images. Matching between regions of interest across the series of images can be performed to determine a number of shared channels required for the segmentation model. The series of images can be further applied to the segmentation model to generate a set of labeled segmented images.