Overcoming Data Scarcity for Coronary Vessel Segmentation Through Self-supervised Pre-training
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[1] Tim Leiner,et al. Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography , 2019, GLMI@MICCAI.
[2] Li Yang,et al. Coronary artery CTA image segmentation and three-dimensional visualization Based on U-Net , 2020 .
[3] Xin Wang,et al. Learning tree-structured representation for 3D coronary artery segmentation , 2019, Comput. Medical Imaging Graph..
[4] Bostjan Likar,et al. Beyond Frangi: an improved multiscale vesselness filter , 2015, Medical Imaging.
[5] Guang Yang,et al. Recent advances in artificial intelligence for cardiac imaging , 2021, Comput. Medical Imaging Graph..
[6] Thomas Brox,et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.
[7] Nikos Paragios,et al. Globally Optimal Active Contours, Sequential Monte Carlo and On-Line Learning for Vessel Segmentation , 2006, ECCV.
[8] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[9] Nima Tajbakhsh,et al. UNet++: A Nested U-Net Architecture for Medical Image Segmentation , 2018, DLMIA/ML-CDS@MICCAI.
[10] L. Joskowicz,et al. Inter-observer variability of manual contour delineation of structures in CT , 2018, European Radiology.
[11] Kayhan Batmanghelich,et al. Self-Supervised Vessel Enhancement Using Flow-Based Consistencies , 2021, MICCAI.
[12] Martin Styner,et al. Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms , 2009, Medical Image Anal..