標(biāo)題: Titlebook: Cerebral Aneurysm Detection and Analysis; First Challenge, CAD Anja Hennemuth,Leonid Goubergrits,Jan-Martin Kuhni Conference proceedings 20 [打印本頁] 作者: Lampoon 時(shí)間: 2025-3-21 19:43
書目名稱Cerebral Aneurysm Detection and Analysis影響因子(影響力)
書目名稱Cerebral Aneurysm Detection and Analysis影響因子(影響力)學(xué)科排名
書目名稱Cerebral Aneurysm Detection and Analysis網(wǎng)絡(luò)公開度
書目名稱Cerebral Aneurysm Detection and Analysis網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Cerebral Aneurysm Detection and Analysis被引頻次
書目名稱Cerebral Aneurysm Detection and Analysis被引頻次學(xué)科排名
書目名稱Cerebral Aneurysm Detection and Analysis年度引用
書目名稱Cerebral Aneurysm Detection and Analysis年度引用學(xué)科排名
書目名稱Cerebral Aneurysm Detection and Analysis讀者反饋
書目名稱Cerebral Aneurysm Detection and Analysis讀者反饋學(xué)科排名
作者: intrigue 時(shí)間: 2025-3-21 23:20 作者: 猛烈責(zé)罵 時(shí)間: 2025-3-22 04:09
Deep Learning-Based 3D U-Net Cerebral Aneurysm Detection a death rate of roughly 40%, it is highly desirable to detect aneurysms early and decide about the appropriate rupture prevention strategy. Rotational X-ray angiography is a non-invasive imaging modality and enables diagnostics to detect cerebral aneurysms at an early stage..We propose a variation 作者: GAVEL 時(shí)間: 2025-3-22 05:34 作者: JECT 時(shí)間: 2025-3-22 09:00 作者: allergy 時(shí)間: 2025-3-22 14:22
3D Attention U-Net with Pretraining: A Solution to CADA-Aneurysm Segmentation Challengeatening. 3D images can provide abundant information for characterizing the aneurysm. But the traditional manual segmentation of aneurysms takes lots of time and effort. Therefore, accurate and rapid automatic algorithm for 3D segmentation of aneurysm is needed. U-Net is a widely used deep learning n作者: allergy 時(shí)間: 2025-3-22 19:15 作者: convert 時(shí)間: 2025-3-23 00:28
CADA Challenge: Rupture Risk Assessment Using Computational Fluid Dynamicsional methods. In this work we performed computational fluid dynamics (CFD) on a subset of aneurysm cases provided by the challenge committee. A large number of aneurysm cases were available, CFD analysis using the lattice Boltzmann method (LBM) were performed on 18 of them. These 18 aneurysms were 作者: collagen 時(shí)間: 2025-3-23 04:05 作者: cauda-equina 時(shí)間: 2025-3-23 06:35
Intracranial Aneurysm Rupture Risk Estimation Utilizing Vessel-Graphs and Machine Learningt methods combine demographic, clinical, morphological, and computational fluid dynamics parameters..We propose a method combining morphological radiomics features, gray-level radiomics features, and a novel aneurysm site location encoding via directed graphs on the vessel tree. Some of the gray-lev作者: 有發(fā)明天才 時(shí)間: 2025-3-23 11:43 作者: malapropism 時(shí)間: 2025-3-23 16:49 作者: 砍伐 時(shí)間: 2025-3-23 21:36 作者: 赦免 時(shí)間: 2025-3-24 01:00 作者: 大包裹 時(shí)間: 2025-3-24 03:49
https://doi.org/10.1007/978-3-658-41661-4hese objectives motivated us to initiate the Cerebral Aneurysm Detection and Analysis (CADA) challenge. It is based on datasets of 3D rotational angiographies, the “gold standard” for clinical management of cerebral aneurysms. Datasets stem from patients with unruptured and ruptured aneurysms.作者: 按時(shí)間順序 時(shí)間: 2025-3-24 08:52 作者: 軌道 時(shí)間: 2025-3-24 11:28
Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)essment was evaluated using the F2 score. 158 participants from 22 countries registered for the CADAchallenge. The detection solutions presented by the community are mostly accurate (F2 score 0.92) with a small number of missed aneurysms with diameters of 3.5?mm. In addition, the delineation of thes作者: 的是兄弟 時(shí)間: 2025-3-24 15:55
CADA: Clinical Background and Motivationhese objectives motivated us to initiate the Cerebral Aneurysm Detection and Analysis (CADA) challenge. It is based on datasets of 3D rotational angiographies, the “gold standard” for clinical management of cerebral aneurysms. Datasets stem from patients with unruptured and ruptured aneurysms.作者: 經(jīng)典 時(shí)間: 2025-3-24 19:36 作者: Contracture 時(shí)間: 2025-3-25 01:49 作者: 填料 時(shí)間: 2025-3-25 04:31 作者: innovation 時(shí)間: 2025-3-25 08:14 作者: 事物的方面 時(shí)間: 2025-3-25 14:58
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.作者: 袋鼠 時(shí)間: 2025-3-25 19:43 作者: Flat-Feet 時(shí)間: 2025-3-25 21:51
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.作者: machination 時(shí)間: 2025-3-26 02:29
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.作者: 認(rèn)為 時(shí)間: 2025-3-26 06:43
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.作者: Rodent 時(shí)間: 2025-3-26 10:59
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.作者: A保存的 時(shí)間: 2025-3-26 12:58 作者: 軌道 時(shí)間: 2025-3-26 20:48
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作者: Condense 時(shí)間: 2025-3-26 21:47
https://doi.org/10.1007/978-3-658-42014-7 that we configure the 3D U-Net with a large patch size, which can obtain the large context. Our method ranked second on the MICCAI 2020 CADA testing dataset with an average Jaccard of 0.7593. Our code and trained models are publicly available at ..作者: 沖突 時(shí)間: 2025-3-27 02:10
Organisationale Machtbeziehungen im Wandelosed. We applied a variety of methods to extract features of cerebral aneurysm images and 3D modeling, and used XGBoost and fully connected neural network for classification and analysis respectively. The method achieved an F2-score of 0.862 on the test set of CADA 2020.作者: 武器 時(shí)間: 2025-3-27 08:19 作者: Mobile 時(shí)間: 2025-3-27 11:08
Cerebral Aneurysm Rupture Risk Estimation Using XGBoost and Fully Connected Neural Networkosed. We applied a variety of methods to extract features of cerebral aneurysm images and 3D modeling, and used XGBoost and fully connected neural network for classification and analysis respectively. The method achieved an F2-score of 0.862 on the test set of CADA 2020.作者: Ergots 時(shí)間: 2025-3-27 17:17
Heidi M?ller,Thomas Giernalczykaccuracy across various models fed with the aneurysm site encoding. A K-nearest neighbors method shows the best results during our model selection with an F2-score of 0.7 and an accuracy of 0.73 on the relatively small private test set with 22 individuals and 30 aneurysms.作者: 偉大 時(shí)間: 2025-3-27 19:38 作者: 英寸 時(shí)間: 2025-3-27 23:34 作者: Mawkish 時(shí)間: 2025-3-28 04:24
https://doi.org/10.1007/978-3-030-72862-53D imaging; artificial intelligence; computer graphics; computer systems; computer vision; deep learning; 作者: Junction 時(shí)間: 2025-3-28 06:34 作者: 六邊形 時(shí)間: 2025-3-28 12:12 作者: NAV 時(shí)間: 2025-3-28 14:59
https://doi.org/10.1007/978-3-658-42011-6n 3D U-net as the backbone and heavy data augmentation with a carefully chosen loss function. We were able to generalize well using our solution (despite training on a small dataset) that is demonstrated through accurate detection and segmentation on the test data.作者: heartburn 時(shí)間: 2025-3-28 20:50
A,-Net: Automatic Detection and Segmentation of Aneurysmn 3D U-net as the backbone and heavy data augmentation with a carefully chosen loss function. We were able to generalize well using our solution (despite training on a small dataset) that is demonstrated through accurate detection and segmentation on the test data.作者: periodontitis 時(shí)間: 2025-3-28 23:52 作者: ingrate 時(shí)間: 2025-3-29 03:49 作者: ANTI 時(shí)間: 2025-3-29 10:08
https://doi.org/10.1007/978-3-658-41661-4 microsurgical clipping or as endovascular intervention including filling of the aneurysm with coils, implantation of flow diverter in the parent vessel or filling the aneurysm with liquid embolic agents. Additionally, imaging is used in long-term follow-up of treated and untreated aneurysms. Imagin
