Geometric-topological deep transfer learning for precise vessel segmentation in 3D medical volumes

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Pathological conditions in neural vasculature and myocardial systems pose significant public health challenges requiring precise vascular delineation.
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Conventional voxel-wise categorization methods struggle with the complex topological variability of vascular structures, limiting their performance.
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The study introduces FlowAxis, a novel approach for vascular segmentation that utilizes optimal transport theory to enhance domain adaptation.
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FlowAxis redefines vessel representation by embedding geometric principles in a continuous framework, improving accuracy in medical imaging.
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The proposed methodology integrates advanced mathematical techniques to ensure robust transfer learning and accurate medial representation of vessels.

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