Tertiary Lymphoid Structure Detection via Neural Network Tissue Masks
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Solution Overview
Problem
Current methods for identifying tertiary lymphoid structures (TLS) and tumor infiltrating lymphocytes (TIL) in tissue images are subjective and inefficient, leading to reproducibility issues and high costs due to manual analysis.
Innovation Solution
A method using computer hardware processors to obtain TLS and TIL masks through trained neural network models, allowing for the identification of TLS boundaries and characteristics in tissue images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis methods are used to identify TLS and TIL in tissue images, then pathologists can provide expert judgment and contextual understanding, but the process is subjective, inefficient, and leads to reproducibility issues
Solution Approach 1:
The patent replaces the mechanical manual analysis system with an automated computational system using trained neural network models. These models process tissue images to generate TLS masks and TIL masks, objectively identifying structures without human subjectivity while maintaining high reproducibility across different cases and pathologists.
Solution Approach 2:
The patent creates digital copies of the tissue images and processes them through computational models to generate mask representations. These masks serve as reproducible digital annotations that can be consistently generated and analyzed, replacing variable human annotations with standardized computational outputs.
2Measurement precision
If manual analysis methods are used to identify TLS and TIL in tissue images, then detailed expert assessment can be performed, but the process is time-consuming and costly
Solution Approach 1:
The patent performs preliminary automated processing by generating TLS masks and TIL masks using trained neural network models before final analysis. This preliminary computational assessment quickly identifies candidate regions and structures, reducing the time pathologists need to spend on initial detection while maintaining accuracy through subsequent validation.
Solution Approach 2:
The patent substitutes time-consuming manual detection with rapid computational processing. The trained models can analyze images and generate masks in minutes rather than hours, dramatically reducing analysis time while maintaining or improving identification accuracy through consistent application of learned patterns.
3Productivity
If automated methods are used to identify TLS and TIL in tissue images, then efficiency and reproducibility improve, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex analysis task into distinct components: generating TLS masks, generating TIL masks, identifying boundaries, and extracting characteristics. This segmentation allows each component to be handled by specialized computational modules, making the overall complex system more manageable and interpretable while maintaining high efficiency.
Solution Approach 2:
The patent develops trained neural network models that serve multiple functions: they can identify both TLS and TIL structures, generate corresponding masks, and provide the basis for boundary identification. This multi-functionality reduces the need for separate specialized tools, managing system complexity through versatile computational assets.
4Reliability
If manual analysis methods are used to identify TLS and TIL in tissue images, then flexibility in interpretation can be maintained, but the cost of analysis increases
Solution Approach 1:
The patent replaces expensive manual expert analysis with cost-effective computational processing. Once the neural network models are trained, they can be deployed at low marginal cost to analyze multiple images consistently, eliminating the need to pay pathologist time for each individual case while maintaining high reliability through reproducible results.
Solution Approach 2:
The patent uses computational resources that can be rapidly deployed and discarded for each analysis task. The trained models serve as reusable but lightweight computational assets that don't require expensive infrastructure, enabling cost-effective scalable analysis while maintaining consistency across diverse cases.
Data Source
AI summary
Described herein are techniques for identifying at least one tertiary lymphoid structure (TLS) in an image of tissue. In some embodiments, the techniques include: obtaining a TLS mask indicating, for each particular pixel of multiple pixels of the image, a value indicative of a likelihood that the particular pixel is part of the at least one TLS; processing at least a portion of the image using a trained neural network model to obtain a tumor infiltrating lymphocyte (TIL) mask indicating, for each particular pixel of pixels of at least the portion of the image, a value indicative of a likelihood that the particular pixel is part of a TIL; identifying boundaries of the at least one TLS using the TLS mask and the TIL mask; and identifying a characteristic of the at least one TLS using the boundaries of the at least one TLS.