nnU-Net auto-segmentation plus human correction reduces colon MRI volume analysis time by over 80% with ICC 0.96 agreement to manual methods and better inter-observer repeatability.
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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SwinUNETR model with 32x32x32 patch sampling achieves DSC of 0.868 for LVCP segmentation in MS, outperforming UXNET with 99% lower computation.
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Accelerating MRI Colon Volume Measurements and Reducing Inter-Observer Variation through Automatic Segmentation and Human-in-the-Loop Correction
nnU-Net auto-segmentation plus human correction reduces colon MRI volume analysis time by over 80% with ICC 0.96 agreement to manual methods and better inter-observer repeatability.
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Efficient Transformer-Based Localized Patch Sampling for Choroid Plexus Segmentation in Multiple Sclerosis
SwinUNETR model with 32x32x32 patch sampling achieves DSC of 0.868 for LVCP segmentation in MS, outperforming UXNET with 99% lower computation.