MRI Anatomy Labeling Workflow with Machine-Learned Localizer Masks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional MRI workflows for labeling subject anatomy are time-consuming and operator-dependent, requiring high-resolution images and manual segmentation, which can lead to errors and inefficiencies.
Innovation Solution
Utilize low-resolution localizer images and machine learning modules to label anatomical points, which are then applied to high-resolution images, reducing the need for high-resolution imaging and segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of anatomical structures is performed on high-resolution MR images, then labeling accuracy can be maintained, but the process becomes extremely time-consuming and operator-dependent
Solution Approach 1:
The patent introduces low-resolution localizer images as an intermediary step. These localizer images are processed through machine learning models to generate preliminary anatomical labels, which then serve as guidance for the final high-resolution image labeling. This intermediary approach reduces the time required while maintaining accuracy through a two-stage process.
Solution Approach 2:
The patent performs preliminary labeling on low-resolution localizer images before processing high-resolution images. The machine learning model generates initial anatomical structure labels on the faster-to-acquire localizer images, which are then transferred and refined for the high-resolution images, eliminating the need for complete manual labeling from scratch.
2Measurement precision
If image segmentation techniques are used to divide and label each anatomical part individually, then comprehensive anatomical coverage is achieved, but the workflow becomes more complex and time-consuming
Solution Approach 1:
The patent merges the labeling of multiple anatomical structures into a unified machine learning model processing step. Instead of manually segmenting and labeling each anatomical part separately, the system uses a trained neural network to simultaneously identify and label multiple anatomical structures in one automated process, reducing workflow complexity while maintaining comprehensive coverage.
3Measurement precision
If high-resolution MRI scans are obtained for primary assessment and labeling, then detailed anatomical information is available, but the scanning time and cost increase significantly
Solution Approach 1:
The patent applies local quality by using low-resolution localizer images for initial anatomical assessment and labeling, reserving high-resolution imaging only for specific regions or final detailed analysis when needed. This approach maintains adequate anatomical detail for labeling purposes while significantly reducing overall scanning time and resource requirements.
Solution Approach 2:
The patent uses partial action by obtaining only the necessary localizer images for labeling purposes rather than complete high-resolution datasets. The machine learning model is trained to extract sufficient anatomical information from these partial (low-resolution) images, eliminating the need for excessive high-resolution scanning in many cases.
Data Source
AI summary
Systems and methods for workflow management for labeling the subject anatomy are provided. The method comprises obtaining at least one localizer image of a subject anatomy using a low-resolution medical imaging device. The method further comprises labeling at least one anatomical point within the at least one localizer image. The method further comprises extracting using a machine learning module a mask of the at least one localizer image comprising the at least one anatomical point label. The method further comprises using the mask to label at least one anatomical point on a high-resolution image of the subject anatomy based on the at least one anatomical point within the localizer image.