Titlebook: Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic V; First Challenge, SEG Antonio Pepe,Gia

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,Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to tbility of aortic dissection or the difficulty with segmenting and annotating the small branches. This work presents a contribution by the MedGIFT team to the . challenge organized during the MICCAI 2023 conference. We propose a fully automated algorithm based on deep encoder-decoder architecture. Th

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,Misclassification Loss for Segmentation of the Aortic Vessel Tree, we propose a Misclassification Loss (MC loss) function, which can effectively suppress false positives and rescue the false negatives. A differentiable eXclusive OR (XOR) operation is implemented to identify these false predictions, which are then minimized through a cross-entropy loss. The propose

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,Deep Learning-Based Segmentation and Mesh Reconstruction of the Aortic Vessel Tree from CTA Images,rtic diseases. Identifying changes in the AVT structure requires high-quality reconstructions that can enable the accurate comparison of the AVT geometry between follow-up scans. However, manual delineation of the whole AVT is a very time-consuming and labor-intensive procedure that can stall the cl

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,RASNet: U-Net-Based Robust Aortic Segmentation Network for Multicenter Datasets,e deployed manually, which is time-consuming and requires an experienced radiologist/physician. Automatic segmentation methods developed in recent years have performed well on single-centered datasets. However, their performance degraded on multi-centered datasets due to the various specifications o

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,A Mini Guide on Mesh Generation of Blood Vessels for CFD Applications,hort guide on meshing the inlets and outlets is provided. Namely, the open-source tool . is used to generate unstructured triangle and quadrilateral meshes, given a polyline description of a two-dimensional inlet or outlet. On the other hand, generating a three-dimensional mesh from the input surfac

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0302-9743 had to upload their algorithms to Grand Challenge in the form of Docker containers. Three tasks were created for SEG.A. 2023..978-3-031-53240-5978-3-031-53241-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

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,M3F: Multi-Field-of-View Feature Fusion Network for Aortic Vessel Tree Segmentation in CT Angiograp ATB) achieves the 1st place on the second phase of the 2023 MICCAI Seg.A challenge leaderboard. Such remarkable performance highlights M3F’s potential for both clinical applications and further research in aortic vessel segmentation.