Medical Image Segmentation Training With Weak Pixel Annotations
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Solution Overview
Problem
The high cost and time-consuming process of manually annotating medical images for supervised learning, and the limited performance of unsupervised algorithms in image segmentation tasks, particularly in histopathological images, necessitate a more efficient training method for decision systems.
Innovation Solution
A method involving iterative steps to train a decision system using pseudo ground-truth tensors, derived from probability predictions and quantitative image annotations, reducing the need for detailed pixel-level labeling by employing sub-image grouping and cost function optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning with pixel-level annotations is used for training, then segmentation performance is improved, but annotation cost and time consumption increase significantly
Solution Approach 1:
The image is divided into multiple sub-images, which are then grouped into super-pixels or regions. This segmentation allows the model to learn from region-level annotations rather than requiring pixel-level annotations, significantly reducing annotation time while maintaining segmentation performance.
Solution Approach 2:
The patent uses pseudo-ground truth labels generated from weak annotations (e.g., bounding boxes or region labels) to create training data. These pseudo-labels are then used to train the segmentation model, eliminating the need for expensive pixel-level expert annotations while achieving comparable performance.
2Loss of time
If unsupervised algorithms are used for training, then annotation cost is reduced, but segmentation performance deteriorates
Solution Approach 1:
The patent performs preliminary clustering or region grouping on the input images before training the segmentation model. This preliminary structuring of data allows the model to learn more effective features and achieve better segmentation performance even with reduced annotation requirements.
Solution Approach 2:
The patent introduces an intermediate representation (such as super-pixels, regions, or clustered groups) that bridges the gap between unsupervised learning and supervised segmentation. This intermediate structure enables the model to leverage weak annotations more effectively, achieving good performance without requiring extensive pixel-level labeling.
3Measurement precision
If detailed pixel-level annotations are required, then model accuracy is improved, but ease of manufacture of training data deteriorates
Solution Approach 1:
The patent segments images into regions or super-pixels and uses these coarser annotations for training. This approach maintains model accuracy by preserving important structural information while making training data preparation much easier, as experts only need to annotate regions rather than individual pixels.
Solution Approach 2:
The patent changes the annotation parameter from pixel-level precision to region-level or bounding-box level annotations. This parameter change significantly reduces the effort required to prepare training data while maintaining sufficient model accuracy through the use of pseudo-ground truth generation and iterative refinement techniques.
Data Source
AI summary
A method implemented by computer means for training a decision system for segmenting medical images from a training set of annotated medical images, the segments belonging to at least one class, each annotation of the medical images including quantitative information about a number of pixels of the image that belongs to each of the classes, the method using weakly-supervised algorithm based on a percentage of the pixels of the image belonging to a concerned class.