Diffusion Weighted MRI Delineation with Deformable Atlas Registration
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Solution Overview
Problem
Current methods for automated delineation of disease in Diffusion Weighted MRI (DWI) images face challenges due to anatomical variability, complex skeletal structures, imaging artifacts, and differences in patient pose, leading to poor performance in inter-patient spatial alignment.
Innovation Solution
An improved atlas-based segmentation method using transfer learning and non-linear deformable transformations to align pre-delineated parts of ground truth images with patient images, facilitated by machine learning classifiers and deep learning models for accurate skeletal and soft tissue segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual delineation of disease regions is performed, then segmentation accuracy is improved, but time consumption increases (1-2 hours per patient)
Solution Approach 1:
The system enables automatic self-segmentation of disease regions using deep learning models that process DWI images independently without requiring manual intervention. The neural network automatically identifies and delineates tumor regions, skeleton, and soft tissues, allowing the imaging system to serve itself rather than requiring radiologist expertise for each segmentation task.
Solution Approach 2:
The patent replaces the mechanical manual delineation process with an automated computational system. Instead of radiologists manually tracing and defining regions, a deep learning-based automated segmentation system performs the same function through algorithmic processing, substituting human expertise with machine intelligence.
2Loss of time
If semi-automated methodologies are used, then processing time is reduced, but inter-observer variability increases
Solution Approach 1:
The system ensures homogeneous segmentation results across different users by employing a single standardized deep learning model that processes all images uniformly. Instead of varying levels of automation or different radiologists applying subjective judgment, the neural network applies consistent algorithmic logic to every case, eliminating inter-observer variability while maintaining efficient processing speeds.
Solution Approach 2:
The patent replaces semi-automated methods that still require human interpretation with fully automated deep learning segmentation. This substitution eliminates the variability introduced by different operators' expertise and judgment, ensuring reproducible results that are independent of who performs the analysis.
3Extent of automation
If atlas-based segmentation is used, then automation is improved, but spatial alignment accuracy deteriorates due to anatomical variability and complex skeletal structures
Solution Approach 1:
The patent replaces traditional atlas-based registration methods with a deep learning-based automated segmentation approach. Instead of relying on statistical atlases and rigid registration algorithms that struggle with anatomical variability, the system uses neural networks trained to directly segment structures like skeleton and soft tissues, achieving both automation and high spatial alignment accuracy through learned patterns from training data.
Solution Approach 2:
The system changes the fundamental parameters of the segmentation approach by moving from atlas-based methods to direct deep learning segmentation. This parameter change allows the system to handle anatomical variability and complex skeletal structures more effectively by using neural networks that can adapt to individual patient anatomy through their training on diverse datasets, rather than forcing data into a standardized atlas framework.
4Productivity
If fully automatic segmentation is implemented, then productivity is improved, but segmentation accuracy may deteriorate without proper validation
Solution Approach 1:
The system incorporates feedback mechanisms through validation datasets and performance metrics that continuously monitor segmentation accuracy. The deep learning model is trained and validated against ground truth annotations, allowing the system to learn from its performance and improve its accuracy while maintaining high productivity. This feedback loop ensures that automated segmentation remains accurate without requiring manual review of each case.
Data Source
AI summary
A computer-implemented method is provided for delineating one or more parts of a body within a diffusion weighted MRI 3D patient image of a human or animal body. The method includes: providing the diffusion weighted MRI 3D patient image of the human or animal body, the patient image being formed of plural slices stacked along a direction of the body; analysing the patient image to identify different contiguous anatomical regions of the body, the regions being distributed along said direction such that each slice of the patient image is allocated to a respective region; providing an atlas of diffusion weighted MRI 3D ground truth images of plural other corresponding bodies containing the regions, each ground truth image being formed of plural slices stacked along a corresponding direction of the respective body, the different regions of the body being pre-identified for each ground truth image such that each slice of that ground truth image is allocated to a respective region, and one or more parts of the body of each ground truth image being pre-delineated; registering each ground truth image to the patient image by: translating and stretching each ground truth image in its corresponding direction to align the identified regions of that ground truth image with the corresponding identified regions of the patient image; identifying a transformation of each registered ground truth image that matches the pre-delineated parts of the body of that registered ground truth image to the corresponding parts of the body of the patient image by minimising a cost function; and segmenting the patient image by obtaining a probability image for the corresponding parts of the body of the patient image, wherein the probability image combines the pre-delineated parts of the bodies of the ground truth images transformed according to their respective non-linear deformable transformations and weighted according to their respective cost-functions.