Deep Learning Neural Network for Medical Image Ablation Region Segmentation
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Solution Overview
Problem
Current methods for evaluating the effectiveness of minimally invasive medical interventions, such as percutaneous ablation, face challenges in precisely determining ablation margins and predicting the risk of recurrence due to operator-dependent segmentation and poor image quality, especially in heterogeneous and low-contrast medical images.
Innovation Solution
A method using a machine learning-based neural network for automatic segmentation of ablation regions in post-operative medical images, trained on a database of segmented images from multiple operators, allowing for precise evaluation and prediction of recurrence risk without requiring an experienced operator, and incorporating pre-operative images for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation by operator is used to determine ablation region volume, then flexibility and adaptability are maintained, but measurement precision and reliability deteriorate due to operator dependency and poor image quality
Solution Approach 1:
The patent replaces manual operator-based mechanical segmentation with an automated deep learning neural network system. The neural network automatically segments the ablation region from post-operative images, eliminating operator dependency and improving measurement precision while maintaining manageable system complexity through algorithmic automation.
2Reliability
If automatic segmentation methods are used to improve consistency, then operator dependency is reduced, but measurement precision deteriorates due to low precision in heterogeneous and low-contrast images
Solution Approach 1:
The patent employs a deep learning neural network that learns optimal segmentation parameters from training data. The network adapts its internal parameters (weights and biases) through training on annotated images, enabling it to accurately segment ablation regions in heterogeneous and low-contrast images where traditional automatic methods fail, thus improving both reliability and precision simultaneously.
3Productivity
If sub-sampling matrix with fixed size is used for segmentation, then processing speed is improved, but measurement precision deteriorates due to limited coverage and fixed resolution
Solution Approach 1:
The patent uses a dynamic approach where the neural network processes the entire post-operative image at its native resolution without fixed sub-sampling. The network dynamically adapts to images of varying sizes and complexities, allowing precise measurement of ablation margins while maintaining efficient processing through optimized neural network architecture and parallel computation.
4Quantity of substance
If segmentation is performed on blurry and low-contrast images, then complete ablation region coverage is achieved, but measurement precision deteriorates due to poor image quality
Solution Approach 1:
The patent performs preliminary training of the neural network on a large dataset of annotated ablation images before deployment. This preliminary action allows the network to learn robust features and patterns that enable accurate segmentation even in blurry and low-contrast post-operative images, achieving both complete coverage and precise boundary definition despite poor image quality.
Data Source
AI summary
The invention relates to a method for evaluating in post-treatment an ablation of a portion of an anatomy of interest of an individual, the anatomy of interest comprising at least one lesion. The evaluation method comprises in particular a step of automatically determining a contour of the ablation region by means of an automatic learning method, such as a neural network, analyzing the post-treatment image of the anatomy of interest of the individual, said automatic learning method being preloaded during a so-called training phase using a database comprising a plurality of post-operative medical images of an anatomy of identical interest of a set of patients, each medical image of the database being associated with an ablation region of the anatomy of interest of said patient. The invention also relates to an electronic device comprising a processor and a computer memory storing instructions of such an evaluation method.