Titlebook: Left Atrial and Scar Quantification and Segmentation; First Challenge, LAS Xiahai Zhuang,Lei Li,Fuping Wu Conference proceedings 2023 The E

不友善

書目名稱Left Atrial and Scar Quantification and Segmentation影響因子(影響力)




書目名稱Left Atrial and Scar Quantification and Segmentation影響因子(影響力)學科排名




書目名稱Left Atrial and Scar Quantification and Segmentation網(wǎng)絡公開度




書目名稱Left Atrial and Scar Quantification and Segmentation網(wǎng)絡公開度學科排名




書目名稱Left Atrial and Scar Quantification and Segmentation被引頻次




書目名稱Left Atrial and Scar Quantification and Segmentation被引頻次學科排名




書目名稱Left Atrial and Scar Quantification and Segmentation年度引用




書目名稱Left Atrial and Scar Quantification and Segmentation年度引用學科排名




書目名稱Left Atrial and Scar Quantification and Segmentation讀者反饋




書目名稱Left Atrial and Scar Quantification and Segmentation讀者反饋學科排名

船員

978-3-031-31777-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

嬰兒

Left Atrial and Scar Quantification and Segmentation978-3-031-31778-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

劇毒

https://doi.org/10.1007/978-3-031-31778-1image processing; pattern recognition; image segmentation; artificial intelligence; computer vision; medi

無孔

脫落

0302-9743 with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, in Singapore, in September 2022..The 15 papers presented in this volume were carefully reviewed and selected form numerous submissions.?The aim of the challenge is not only benchmarkin

Veneer

,LASSNet: A Four Steps Deep Neural Network for?Left Atrial Segmentation and?Scar Quantification,erent parameters and applied data triaging. 200. late gadolinium enhancement magnetic resonance images were provided by the LAScarQS 2022 Challenge. Finally, we compared our performances for scar quantification to the literature and demonstrated improved performances.

expansive

,Sequential Segmentation of?the?Left Atrium and?Atrial Scars Using a?Multi-scale Weight Sharing Netwage Dice score of 0.938 and 0.558 for the LA cavity and scars segmentation of task 1, and a Dice score of 0.846 for LA cavity segmentation of task 2. The pre-trained models, source code, and implementation details are available at

浮雕

Multi-depth Boundary-Aware Left Atrial Scar Segmentation Network,ated from the LA branch to the scar branch. Thus, LA scar segmentation can be performed condition on the LA boundaries regions. In our experiments, 40 labeled images were used to train the proposed network, and the remaining 20 labeled images were used for evaluation. The network achieved an average Dice score of 0.608 for LA scar segmentation.

不公開

,Two Stage of?Histogram Matching Augmentation for?Domain Generalization: Application to?Left Atrial The method is evaluated on LAScarQS 2022 data-set, acquiring average Dice of 0.87790 for LA segmentation. Besides, the two-stage network is about four times faster against a single-stage network in the test phase.