Non-Tumor Region Segmentation for Accurate Tumor Image Analysis
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Solution Overview
Problem
Existing automated tumor segmentation methods struggle with accurately distinguishing non-tumor regions, such as lymphoid aggregates, from tumor cells due to their similar morphology and staining characteristics, leading to errors in tumor detection and analysis.
Innovation Solution
A computer-implemented method using a modified U-Net model with a contracting and expansive path, trained on pre-labeled images, segments non-tumor regions like lymphoid aggregates and masks them out before performing image analysis, improving the accuracy of tumor cell classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing automated tumor segmentation methods are used, then tumor detection can be performed, but accuracy deteriorates due to inability to distinguish non-tumor regions from tumor cells
Solution Approach 1:
The patent applies segmentation by dividing the image into multiple patches and classifying them into different regions (tumor, non-tumor, lymphoid aggregates). The U-Net model segments the image into distinct classes, allowing accurate differentiation between tumor cells and non-tumor regions including lymphoid aggregates, thereby resolving the contradiction between detection capability and classification accuracy.
Solution Approach 2:
The patent introduces an intermediary approach by using a two-stage classification system: first classifying patches into broad regions (tumor vs non-tumor), then performing secondary classification within tumor regions. This intermediary step of regional classification before cell-level analysis improves both detection accuracy and reliability by filtering out non-tumor regions early in the process.
2Measurement precision
If manual analysis is performed, then accuracy can be maintained, but productivity deteriorates due to time-consuming analysis
Solution Approach 1:
The patent implements self-service automation where the U-Net model autonomously performs both regional classification and tumor cell detection without manual intervention. The system self-classifies patches into tumor and non-tumor regions, then automatically identifies tumor cells within those regions, maintaining high accuracy while dramatically increasing productivity by eliminating manual analysis time.
Solution Approach 2:
The patent applies preliminary action by first performing regional classification to identify potential tumor areas before conducting detailed tumor cell detection. This preliminary regional segmentation step guides subsequent analysis, allowing the system to efficiently process only relevant areas while maintaining high accuracy, thus improving both productivity and measurement precision.
3Quantity of substance
If non-tumor regions are not masked out, then analysis coverage is maximized, but measurement precision deteriorates due to over-detection and miss-classification
Solution Approach 1:
The patent applies the extraction principle by removing non-tumor regions (including lymphoid aggregates) from the analysis through masking. The U-Net model identifies and extracts these non-tumor regions, creating masks that exclude them from subsequent tumor cell detection. This extraction improves measurement precision by preventing over-detection and miss-classification of non-tumor regions while maintaining comprehensive analysis coverage through systematic region identification.
Data Source
AI summary
The present disclosure relates machine learning techniques for segmenting non-tumor regions in specimen images to support tumor detection and analysis. Particularly, aspects of the present disclosure are directed to accessing one or more images that comprise a non-target region (e.g., a non-tumor region) and a target region (e.g., a tumor region), predicting, by a two-dimensional segmentation model, segmentation maps for the non-target region based on discriminative features encoded from the one or more images, a segmentation mask for the one or more images based on the segmentation maps, applying the segmentation mask to the one or more images to generate non-target region masked images that exclude the non-target region from the one or more images, and classifying, by an image analysis model, a biological material or structure within the target region based on a set of features extracted from the non-target region masked images.