Deep Learning Medical Image Segmentation for Faster TTFields Layouts
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Solution Overview
Problem
Manual segmentation of medical images for tumor treating fields (TTFields) is laborious and time-consuming, particularly due to the large number of slices and voxels, which hinders the efficient generation of transducer layouts for TTFields application.
Innovation Solution
Utilizing deep learning techniques, specifically unsupervised trained machine learning models and neural networks, to automatically segment medical images into normal and abnormal tissues, followed by combining these segments to generate recommended transducer layouts for TTFields application, thereby reducing computational time and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation methods are used, then segmentation accuracy can be maintained, but processing time and labor requirements increase significantly
Solution Approach 1:
The patent replaces manual mechanical segmentation processes with an automated deep learning-based system. The neural network automatically processes medical images, identifies tumor regions, and generates segmentation masks without human intervention, thereby maintaining accuracy while dramatically reducing processing time and labor requirements.
Solution Approach 2:
The patent uses deep learning models trained on annotated medical images to create automated segmentation systems. The model learns from training data and reproduces segmentation results that mirror manual expert annotations, enabling automated processing that replicates the accuracy of manual methods.
2Productivity
If deep learning models are used for automatic segmentation, then processing speed increases, but computational resource requirements and system complexity increase
Solution Approach 1:
The patent divides the segmentation task into multiple components: image loading and preprocessing, deep learning-based tumor segmentation, and transducer layout generation. This modular approach allows each component to be optimized independently and facilitates easier implementation and maintenance despite the overall complexity.
Solution Approach 2:
The patent introduces intermediate representations and processing stages between input medical images and final transducer layouts. The deep learning model processes images through multiple layers, generating intermediate feature maps and segmentation masks that serve as inputs for subsequent processing steps, thereby managing complexity through structured information flow.
Data Source
AI summary
A method for reviewing a medical image including: accessing from memory a medical image of a subject, the medical image comprising voxels; generating, using a first trained machine learning model and the medical image, a first segmented medical image, the first trained machine learning model trained to generate a medical image segmenting normal tissue in a medical image; generating, using a second trained machine learning model and the medical image, a second segmented medical image, the second trained machine learning model trained to generate a medical image segmenting abnormal tissue in a medical image; combining the first segmented medical image and the second segmented medical image to obtain a segmented medical image, the segmented medical image comprising segmented normal tissue based on the first segmented medical image and segmented abnormal tissue based on the second segmented medical image.