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.
Automatic colon segmentation on T1-FS MR images
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A two-stage deep learning framework segments ten GI organs from coronal MR enterography images, achieving mean DSC of 88.99% and outperforming baselines.
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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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A Two-Stage Deep Learning Framework for Segmentation of Ten Gastrointestinal Organs from Coronal MR Enterography
A two-stage deep learning framework segments ten GI organs from coronal MR enterography images, achieving mean DSC of 88.99% and outperforming baselines.