Virtual Vasculature Models for Balanced Deep Learning Training
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Solution Overview
Problem
Characterizing the morphology of vasculature in digital pathology is challenging due to the time-consuming and labor-intensive process of acquiring natural training samples, which can lead to biased classification models, especially when dealing with imbalanced data sets in deep learning applications.
Innovation Solution
The method generates virtual multi-dimensional data using pre-trained convolutional neural networks, bypassing image segmentation and hand-engineered feature extraction, and creates 3D parametric models of vasculature to characterize morphology, allowing for the use of synthetic data to train classification models, which are then applied to clinical images for vascular feature classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If natural training samples are acquired for deep learning, then classification models can be trained, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent creates synthetic copies of vascular structures through 3D parametric modeling and virtual histology sectioning. These synthetic images replicate the appearance and morphological features of real tissue sections, providing unlimited training data without requiring additional physical sample acquisition. The synthetic data preserves the statistical properties and visual characteristics needed for effective deep learning training.
Solution Approach 2:
The patent performs preliminary 3D reconstruction and virtual sectioning to generate all training images computationally before the classification task. By pre-generating synthetic training data through parametric modeling and virtual histology, the system eliminates the need for time-consuming physical sample preparation and imaging during the training phase, enabling rapid model development.
2Measurement precision
If natural training samples are used, then real vascular morphology can be captured, but data sets become imbalanced leading to biased classification models
Solution Approach 1:
The patent applies local quality by generating synthetic samples with specific, controlled morphological characteristics for different vascular classes. The parametric models allow precise control over local features such as vessel diameter, branching patterns, and lumen structure, enabling balanced representation of rare and common vascular types in the training set, thereby preventing class imbalance bias.
Solution Approach 2:
The patent utilizes parameter changes in the 3D parametric models to systematically vary vascular morphology parameters and generate diverse synthetic samples. By adjusting parameters such as vessel radius, curvature, branching angles, and lumen presence, the system creates balanced datasets covering the full range of vascular morphologies, ensuring reliable classification across all classes.
3Productivity
If image segmentation and hand-engineered feature extraction are used, then traditional analysis can be performed, but pre-trained convolutional neural networks can bypass this pipeline more efficiently
Solution Approach 1:
The patent replaces the mechanical pipeline of image segmentation and hand-engineered feature extraction with a neural network-based approach. Pre-trained convolutional neural networks automatically learn relevant features from the synthetic images, substituting manual feature engineering with automated deep learning feature extraction, thereby simplifying the pipeline and improving efficiency.
Solution Approach 2:
The patent employs pre-trained convolutional neural networks that have universal feature extraction capabilities across different image types. These networks can process both synthetic and real histology images, performing morphology characterization without requiring task-specific feature engineering, thus providing a universal solution that simplifies the analysis pipeline.
Data Source
AI summary
The present approach relates to the use of trained artificial neural networks, such as convolutional neural networks, to classify vascular structures, such as using a hierarchical classification scheme. In certain approaches, the artificial neural network is trained using training data that is all or partly derived from synthetic vascular representations.