Deep Neural Network Out-painting for Tumor Border Zone Assessment
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Solution Overview
Problem
Current diagnostic methods face challenges in accurately combining information from different imaging modalities, such as ultrasound and histopathology, to effectively assess tumor borders and predict recurrence, leading to potential missed malignant cells and increased risk of secondary cancer.
Innovation Solution
A method using a deep neural network to fuse pre-operative ultrasound images with post-operative histopathological images, generating synthesized histopathology images to out-paint the tumor border zone, enabling precise analysis and prediction of residual malignant cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physicians remove more surrounding tissue to prevent recurrence, then the risk of secondary cancer decreases, but the quality of life of the patient deteriorates
Solution Approach 1:
The patent replaces manual surgical decision-making with an automated deep learning system that fuses ultrasound and histopathology images to predict residual malignant cells. This substitution allows for more precise, objective assessment of tumor borders, enabling physicians to make better-informed decisions about resection margins without relying solely on visual inspection or experience-based judgment.
Solution Approach 2:
The patent introduces a deep learning model as an intermediary between imaging data and surgical decision-making. This intermediary processes and integrates information from multiple imaging modalities (ultrasound and histopathology) to generate predictive maps of residual malignant cells, serving as a bridge that translates complex medical data into actionable surgical guidance.
2Object-affected harmful factors
If physicians remove less surrounding tissue to preserve quality of life, then the quality of life improves, but the risk of secondary cancer increases
Solution Approach 1:
The automated deep learning system provides objective, data-driven assessment that reduces reliance on subjective visual inspection. By replacing manual assessment with algorithmic analysis of fused imaging data, the system enables more accurate identification of residual malignant cells, allowing for conservative resection margins that preserve healthy tissue while still detecting cancerous cells that might be missed by human observers.
Solution Approach 2:
The patent generates synthesized histopathology images from ultrasound images using the deep learning model. This copying approach allows for virtual visualization of histopathological features without requiring actual tissue sampling or staining, enabling assessment of residual malignant cells in real-time during surgery and reducing the need for extensive tissue removal for diagnostic purposes.
3Measurement precision
If multiple imaging modalities are combined to improve detection accuracy, then the detection precision of malignant cells improves, but the device complexity increases
Solution Approach 1:
The patent merges ultrasound imaging and histopathology imaging into a unified deep learning framework. By combining these complementary modalities within a single neural network architecture, the system leverages the real-time capabilities of ultrasound with the cellular-level detail of histopathology, achieving superior detection precision while managing complexity through integrated processing rather than separate analysis systems.
Solution Approach 2:
The deep learning model serves as an intermediary that handles the complexity of multi-modal image fusion. Rather than requiring physicians to manually integrate information from separate ultrasound and histopathology systems, the neural network automatically processes and synthesizes both modalities, managing the computational complexity while delivering simplified, actionable output in the form of predictive maps.
4Measurement precision
If deep learning models are used to fuse imaging modalities, then the detection accuracy of residual malignant cells improves, but the computational requirements and processing time increase
Solution Approach 1:
The deep learning model is pre-trained on large datasets of paired ultrasound and histopathology images before deployment. This preliminary training phase allows the model to learn the complex relationships between imaging modalities offline, so that during actual surgical use, the inference process can proceed rapidly without requiring extensive computation in real-time, thus reducing processing time while maintaining high detection accuracy.
Data Source
AI summary
In one embodiment, a method, comprising: obtaining a correlation between the pre-operative ultrasound images and the post-operative histopathological images based on application of a fused image to a neural network; receiving post-operative ultrasound images; using the correlation to translate the post-operative ultrasound images to synthesized histopathology images; fusing the post-operative ultrasound images with the synthesized histopathology images; out-painting the synthesized histopathology images to a border zone remaining after the surgical procedure, the out-painting performed on a neural network; and displaying the out-painted, synthesized image with the post-operative ultrasound images.