arXiv:2401.01160v3 Announce Type: replace-cross Abstract: We investigate a framework for train-free MRI segmentation based on Topological Data Analysis. The pipeline proceeds in three steps, first identifying the whole object to segment via automatic thresholding, then detecting a distinctive subset whose topology is known in advance, and finally deducing the various components of the segmentation. A key ingredient is the extraction of approximate representative cycles from persistence diagrams, which provides an interpretable link between persistent features and anatomical components. To clar
This research is emerging as AI in medical imaging seeks more robust, explainable, and less data-intensive methods, moving beyond traditional deep learning reliance on vast labeled datasets.
A train-free, topology-based segmentation method for MRI could significantly reduce the computational cost and data dependency for medical AI, accelerate clinical deployment, and enhance interpretability.
The reliance on large, annotated training datasets for MRI segmentation could diminish, opening avenues for AI deployment in data-scarce medical contexts and improving diagnostic reliability.
- · Medical imaging AI startups
- · Healthcare providers in data-poor regions
- · Patients needing MRI diagnostics
- · Medical device manufacturers
- · Companies reliant on large, manual data annotation for medical AI
- · Traditional deep learning-only medical imaging AI approaches
Reduced need for extensive, curated datasets for medical image analysis.
Faster and more globally equitable diffusion of advanced AI diagnostics, particularly in areas with limited data infrastructure.
A broader shift in AI development towards topological and explainable AI methods, impacting various scientific and engineering fields.
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Read at arXiv cs.LG