Titlebook: Cerebral Aneurysm Detection and Analysis; First Challenge, CAD Anja Hennemuth,Leonid Goubergrits,Jan-Martin Kuhni Conference proceedings 20

填料

innovation

事物的方面

https://doi.org/10.1007/978-3-658-42014-7U-Net, pretrained 3D U-Net and pretrained 3D Attention U-Net are 0.881, 0.884, 0.890 and 0.907, respectively. The experimental results show that the use of attention gate and Models Genesis can significantly improve the performance of U-Net model in segmenting aneurysms. This work achieved rank one in CADA 2020- Aneurysm Segmentation Challenge.

袋鼠

Flat-Feet

Heidi M?ller,Thomas Giernalczyksm. The proposed network was trained on the . challenge set of 109 aneurysms. The proposed method achieves an accuracy of 0.64 and an F2-score of 0.73 on the private . challenge test set of 30 aneurysms.

machination

Deep Learning-Based 3D U-Net Cerebral Aneurysm Detectiont solutions, with a drastically reduced false-positive rate per patient. The described solution is almost entirely accurate on structures larger than 5?mm in diameter but shows difficulties with smaller aneurysms. We show an F2-score of 0.84 and a false-positive rate of 0.41 on a private test set.

認(rèn)為

3D Attention U-Net with Pretraining: A Solution to CADA-Aneurysm Segmentation ChallengeU-Net, pretrained 3D U-Net and pretrained 3D Attention U-Net are 0.881, 0.884, 0.890 and 0.907, respectively. The experimental results show that the use of attention gate and Models Genesis can significantly improve the performance of U-Net model in segmenting aneurysms. This work achieved rank one in CADA 2020- Aneurysm Segmentation Challenge.

Rodent

CADA Challenge: Rupture Risk Assessment Using Computational Fluid Dynamicsults of the DNS may serve as inputs for data driven methods to identify qualitative maps of hemodynamic quantities in aneurysms. In this article we report the results of CFD and discuss hypotheses associating the flow characteristics with the rupture risk of aneurysms.

A保存的

軌道

0302-9743 Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in October 2020. The conference was planned to take place in Lima, Peru, and took place virtually due to the COVID-19 pandemic. .The 9 regular papers presented in this volume, together with an overview and one in