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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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,Position-Encoded Pixel-to-Prototype Contrastive Learning for Aortic Vessel Tree Segmentation, phase 2 of the challenge. Such advancements in segmentation techniques not only prove effective in competitions but also have the potential to revolutionize medical image analysis, paving the way for improved diagnostic and treatment planning in the clinical realm.
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,Misclassification Loss for Segmentation of the Aortic Vessel Tree, our method achieves a Dice score of 0.93 and a Hausdorff Distance (HD) of 3.50 mm on a 5-fold split of 56 training subjects. We participated in the SEG.A. 2023 challenge, and the proposed method ranks among the top-six approaches in the validation phase-1. The pre-trained models, source code, and implementation will be made public.
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Aortic Segmentations and Their Possible Clinical Benefits,” for catheter-based treatment options and implants in many different procedures. Segmentations of the aorta and its vascular tree might have the potential of enhancing clinical workflows and results. Manual segmentation is a very time-consuming process, therefore automated solutions are urgently needed.
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Andriy Myronenko,Dong Yang,Yufan He,Daguang Xuntegration of this knowledge with system specifications, and its semi-automated verification both in design and run time. A case study on a distributed obstacle detection system for vehicles extended with social knowledge to anticipate people’ behaviour illustrates the approach.
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