Convolutional Neural Network Cell Center Analysis for Cancer Therapy Response Prediction
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Solution Overview
Problem
Current methods for assessing a cancer patient's response to therapy are subjective and prone to variability due to visual assessment by pathologists, which can lead to inconsistent treatment outcomes.
Innovation Solution
A method utilizing spatial statistical analysis of cell centers in digital images of stained tissue, where convolutional neural networks generate feature vectors for image patches to classify cell centers and compute a score indicative of treatment response, leveraging techniques like k-means clustering and generative adversarial networks to provide an objective and repeatable assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual assessment by pathologists is used to evaluate cancer patient response to therapy, then the assessment can be performed with current standard methodologies, but the assessment is prone to variability and subjectivity
Solution Approach 1:
The patent replaces the manual visual assessment mechanism with an automated computer-based system. The system automatically detects cell centers, extracts image patches, generates feature vectors using convolutional neural networks, performs spatial statistical analysis, and computes prediction scores without requiring pathologist intervention, thereby eliminating subjectivity and variability inherent in manual evaluation
Solution Approach 2:
The patent creates a digital copy of the tissue sample through high-resolution imaging and processes this digital representation through computational algorithms. The system replicates and extends the pathologist's evaluation process by automatically analyzing the same tissue characteristics that would be manually assessed, but with consistent, repeatable results across different evaluations
2Measurement precision
If computer-based methods are used to generate repeatable and objective scores, then assessment objectivity is improved, but the system complexity increases
Solution Approach 1:
The patent divides the complex tissue analysis task into discrete, manageable segments: cell center detection, image patch extraction, feature vector generation through convolutional neural networks, class assignment based on feature vectors, and spatial statistical analysis. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high measurement precision
Solution Approach 2:
The patent introduces feature vectors as an intermediary representation between the raw image data and the final prediction score. The convolutional neural network processes image patches into feature vectors that capture essential cellular characteristics, which then serve as input for spatial statistical analysis. This intermediary layer simplifies the relationship between complex image data and the final objective score, making the system more manageable
3Measurement precision
If spatial statistical analysis of cell centers is performed to predict treatment response, then treatment prediction accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing by pre-segmenting the tissue image into individual cell centers and pre-extracting image patches for each cell. The convolutional neural network generates feature vectors for these patches in advance, which are then used in the spatial statistical analysis. This preliminary action reduces the computational burden during the final prediction step and speeds up the overall processing time while maintaining high prediction accuracy
Data Source
AI summary
A method for indicating how a cancer patient will respond to a predetermined therapy relies on spatial statistical analysis of classes of cell centers in a digital image of tissue of the cancer patient. The cell centers are detected in the image of stained tissue of the cancer patient. For each cell center, an image patch that includes the cell center is extracted from the image. A feature vector is generated based on each image patch using a convolutional neural network. A class is assigned to each cell center based on the feature vector associated with each cell center. A score is computed for the image of tissue by performing spatial statistical analysis based on classes of the cell centers. The score indicates how the cancer patient will respond to the predetermined therapy. The predetermined therapy is recommended to the patient if the score is larger than a predetermined threshold.