Tissue Segmentation via Tile Decomposition and Aggregation
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Solution Overview
Problem
Current medical image analysis techniques, particularly for large images like whole-slide histopathology, face challenges in processing and interpreting due to their size, and lack transparency in their decision-making processes, making it difficult to accurately segment and classify tissues without prior knowledge of the image content.
Innovation Solution
The approach involves decomposing large medical images into smaller overlapping tiles, using a classifier like CNN to generate tile-level classifications, and aggregating these to produce pixel-level segmentation masks and subtype classifications, enhancing accuracy through data augmentation and ensemble modeling, while also providing visual cues for clinical interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large medical images are processed directly by CNNs, then classification accuracy may be improved, but computational complexity and memory requirements increase significantly
Solution Approach 1:
The patent divides large medical images into smaller overlapping tiles that can be processed individually by CNNs. Each tile is classified separately, and the results are aggregated to produce the final classification. This segmentation approach reduces the computational burden on individual CNN processing steps while maintaining the ability to capture fine-grained features that would be lost in direct processing of the entire large image.
2Measurement precision
If traditional segmentation techniques are used, then tissue regions can be identified, but the methods require a priori knowledge of tissue types and are computationally demanding
Solution Approach 1:
The patent employs self-supervised learning where the model learns to segment tissues by predicting the content of occluded regions in the image. The system uses data augmentation techniques including random occlusions and transformations to train the model without requiring manual annotations or prior knowledge of tissue types. The model serves itself by learning from the image data alone, eliminating the need for complex preprocessing or expert knowledge input.
3Productivity
If black-box CNN models are used for classification, then diagnostic speed is improved, but clinical acceptance decreases due to lack of interpretability
Solution Approach 1:
The patent introduces an intermediary segmentation step that produces visual maps of tissue regions before final classification. These segmentation maps serve as interpretable intermediaries that show clinicians which tissue regions are being analyzed and how they are classified. The system generates probability maps and visualizations that bridge the gap between the black-box CNN processing and clinical interpretation, allowing clinicians to understand the rationale behind classifications while maintaining fast automated processing.
Data Source
AI summary
Accurate tissue segmentation is performed without a priori knowledge of tissue type or other extrinsic information not found within the subject image, and may be combined with classification analysis so that diseased tissue is not only delineated within an image but also characterized in terms of disease type. In various embodiments, a source image is decomposed into smaller overlapping subimages such as square or rectangular tiles. A predictor such as a convolutional neural network produces tile-level classifications that are aggregated to produce a tissue segmentation and, in some embodiments, to classify the source image or a subregion thereof.