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How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalize to rare congenital heart diseases for surgical planning?

, , , , , , , , , and . Medical Imaging: Image-Guided Procedures, volume 11315 of SPIE Proceedings, page 113151K. SPIE, (2020)

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Improving Workflow Fault Tolerance through Provenance-based Recovery, , , , and . (2011)Comparison of Evaluation Metrics for Landmark Detection in CMR Images., , , , , , , , and . Bildverarbeitung für die Medizin, page 198-203. Springer, (2022)Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation., , , , , , and . CoRR, (2021)Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks., , , , , , , , , and 2 other author(s). CoRR, (2021)Self-supervised Motion Descriptor for Cardiac Phase Detection in 4D CMR Based on Discrete Vector Field Estimations., , , , , , , and . STACOM@MICCAI, volume 13593 of Lecture Notes in Computer Science, page 65-78. Springer, (2022)Heatmap-based 2D Landmark Detection with a Varying Number of Landmarks., , , , , , , and . Bildverarbeitung für die Medizin, page 22-27. Springer, (2021)How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?, , , , , , , , , and . CoRR, (2020)Mutually Improved Endoscopic Image Synthesis and Landmark Detection in Unpaired Image-to-Image Translation., , , , , , and . IEEE J. Biomed. Health Informatics, 26 (1): 127-138 (2022)How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalize to rare congenital heart diseases for surgical planning?, , , , , , , , , and . Medical Imaging: Image-Guided Procedures, volume 11315 of SPIE Proceedings, page 113151K. SPIE, (2020)