Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation
arXiv:2603.04024v2 Announce Type: replace-cross Abstract: Ambiguous 3D medical image segmentation often involves boundaries where different expert delineations are non-identical yet clinically plausible. Modeling such inter-observer variability requires a careful balance between diversity and anatomical fidelity: deterministic models preserve coherent volumetric structures but collapse expert disagreement into a single mask, while stochastic generative models can produce diverse samples but may introduce disconnected components or slice-to-slice inconsistency when generating full 3D masks from
The proliferation of advanced AI in medical imaging necessitates more robust methods for uncertainty quantification to bridge the gap between AI outputs and clinical consensus.
Improving the accuracy and reliability of medical image segmentation through better uncertainty quantification addresses a critical hurdle for AI adoption in clinical settings, reducing diagnostic errors and improving patient outcomes.
The ability to model inter-observer variability in 3D medical image segmentation will lead to more trustworthy AI tools, potentially accelerating regulatory approval and clinical integration of AI diagnostics.
- · Medical AI developers
- · Hospitals and clinics
- · Medical imaging equipment manufacturers
- · Patients
- · Developers of less robust, deterministic medical AI
More accurate and reliable AI-driven medical diagnoses become feasible, reducing cognitive load on clinicians and improving throughput.
This improved reliability leads to broader adoption of AI in critical medical applications, potentially standardizing diagnostic approaches across institutions.
Reduced diagnostic errors and earlier, more precise interventions could significantly lower healthcare costs and improve public health outcomes globally.
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Read at arXiv cs.AI