Interactive Medical Image Segmentation With Image-Specific Adaptation
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Solution Overview
Problem
Conventional CNNs for medical image segmentation struggle with generalization to unseen object classes, lack of image-specific adaptation, and high memory and computational demands, especially for 3D volumes, limiting their effectiveness in clinical settings.
Innovation Solution
A neural network training method that incorporates user interactions to adaptively segment anatomical structures by combining user corrections with distance metrics, allowing for efficient segmentation of medical images with high accuracy and robustness, even in the absence of extensive training datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional CNNs are used for automated medical image segmentation, then segmentation speed is improved, but segmentation accuracy and robustness deteriorate due to poor generalization to unseen object classes and lack of image-specific adaptation
Solution Approach 1:
The system dynamically adapts the CNN model to each test image through image-specific fine-tuning. The model parameters are not fixed but are adjusted based on the specific characteristics of each input image, enabling the system to maintain high segmentation accuracy while preserving automated processing speed. This dynamic adaptation resolves the contradiction by allowing the model to be both fast (automated) and accurate (adapted to specific images).
Solution Approach 2:
The invention changes the parameters of the CNN model during the fine-tuning phase for each test image. By adjusting weights and biases based on image-specific characteristics, the model achieves better generalization to unseen object classes and pathologies. This parameter adaptation enables the system to maintain high segmentation accuracy without sacrificing the speed benefits of automated processing.
2Measurement precision
If user interactions are incorporated for image-specific adaptation, then segmentation accuracy is improved, but user time and operational complexity increase
Solution Approach 1:
Instead of requiring users to provide extensive annotations for entire images, the system only requires minimal user interactions (such as a few clicks or scribbles) to identify regions of interest. The CNN then performs the bulk of the segmentation work automatically, achieving high accuracy with minimal user time investment. This partial action approach resolves the contradiction by obtaining sufficient user input to guide adaptation without requiring exhaustive user annotation.
Solution Approach 2:
The system enables the CNN model to perform self-service adaptation by automatically fine-tuning its parameters based on minimal user guidance. The model itself carries out the complex work of adapting to image-specific characteristics without requiring extensive user intervention, thus improving segmentation accuracy while minimizing user time requirements.
3Productivity
If CNNs work on entire 3D volumes for fast inference, then segmentation speed is improved, but memory requirements increase significantly
Solution Approach 1:
The invention divides the 3D volume into multiple 2D slices that can be processed independently and in parallel. This segmentation approach reduces the memory burden of processing entire 3D volumes while maintaining fast inference through efficient parallel processing of slices. The system achieves both speed and memory efficiency by breaking down the large 3D processing task into smaller, manageable 2D components.
Data Source
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AI summary
The invention relates to a method for training a neural network, NN, for interactive medical image segmentation. A training phase comprises receiving a medical image dataset from a database of medical image datasets as input to the NN, with the medical image dataset comprising a medical image (302) and an annotated segmentation; performing, by hidden layers of the NN with interconnections, a first segmentation (304) of anatomic structures comprised in the medical image (302); outputting the first segmentation (304); receiving, at a user interface, a correction (306) on the first segmentation (304); determining, based on the first segmentation (304) and the correction (306), a second segmentation (312); determining a distance metric (310) between the second segmentation (312) and the annotated segmentation or the first segmentation (304); combining the distance metric (310), second segmentation (312) and medical image (302) into a further medical image dataset (314); and iteratively repeating the steps.