標題: Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; Third International Alessandro Crimi,Spyridon Bakas,Mauricio [打印本頁] 作者: 面臨 時間: 2025-3-21 17:10
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries影響因子(影響力)
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries影響因子(影響力)學科排名
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries網(wǎng)絡(luò)公開度
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries網(wǎng)絡(luò)公開度學科排名
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries被引頻次
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries被引頻次學科排名
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries年度引用
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries年度引用學科排名
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries讀者反饋
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries讀者反饋學科排名
作者: 進取心 時間: 2025-3-21 20:32
0302-9743 this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; and ischemic stroke lesion image segmentation..978-3-319-75237-2978-3-319-75238-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 搜集 時間: 2025-3-22 02:28
Mechanisms and Ways of Macrophage Deliveryre we propose a new set of evaluation techniques that offer new insights into the behavior of segmentation algorithms. We illustrate these techniques with a case study comparing two popular multiple sclerosis?(MS) lesion segmentation algorithms: OASIS and LesionTOADS.作者: commodity 時間: 2025-3-22 06:53
Kylie B. R. Belchamber,Louise E. Donnelly who underwent brain gliomas resection. By using landmark-based mean target registration errors (TRE) for evaluation, our technique has achieved a result of 2.32?±?0.68?mm from the initial 5.13?±?2.78?mm.作者: Brocas-Area 時間: 2025-3-22 12:23
Regulation of Macrophage Productional time of patients. The proposed deep learning frameworks was evaluated on BraTS 17 validation set and achieved competing results for tumor segmentation While Dice scores of 0.88, 0.75 0.71 were achieved for whole tumor, enhancing tumor and tumor core, respectively, an accuracy of 0.55 was obtained for survival prediction.作者: 禍害隱伏 時間: 2025-3-22 14:35 作者: diskitis 時間: 2025-3-22 18:32
Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentationre we propose a new set of evaluation techniques that offer new insights into the behavior of segmentation algorithms. We illustrate these techniques with a case study comparing two popular multiple sclerosis?(MS) lesion segmentation algorithms: OASIS and LesionTOADS.作者: 痛得哭了 時間: 2025-3-22 23:28
MARCEL (Inter-Modality Affine Registration with CorrELation Ratio): An Application for Brain Shift C who underwent brain gliomas resection. By using landmark-based mean target registration errors (TRE) for evaluation, our technique has achieved a result of 2.32?±?0.68?mm from the initial 5.13?±?2.78?mm.作者: 高談闊論 時間: 2025-3-23 04:12 作者: Thymus 時間: 2025-3-23 07:56 作者: CAB 時間: 2025-3-23 10:03
Leticia Reyes,Bryce Wolfe,Thaddeus Golosg rank?#1 in the ISBI 2015 longitudinal multiple sclerosis lesion segmentation challenge, we show that a setup which combines these techniques can outperform the state of the art in automated lesion segmentation.作者: d-limonene 時間: 2025-3-23 16:44
Leticia Reyes,Bryce Wolfe,Thaddeus Golosodalities. The addition of JIF synthesized images improved the Dice-Sorensen coefficient (relative to manually drawn gold standards) of lesion segmentations over the standard model segmentations by . (mean ± standard deviation) at optimal threshold over all subjects and 10 separate training/testing folds.作者: Mettle 時間: 2025-3-23 21:03
Polarizing Macrophages In Vitro,epresentation to classification. Experiment results show that, with 10-fold cross-validation, the proposed method achieves the accuracy of 94.83% and 95.69% by using T1 contrast-enhanced and T2 weighted magnetic resonance images, respectively.作者: 不可比擬 時間: 2025-3-24 01:28 作者: 使成波狀 時間: 2025-3-24 05:19
Xia Zhang,Justin P. Edwards,David M. Mossernt Post-Concussive Symptoms in a small dataset but achieve only a .74 AUC in identifying mTBI subjects with milder symptoms. Finally, we perform subject-specific simulations which characterize which injuries are detected and which are missed.作者: Synchronism 時間: 2025-3-24 09:53
Mario R. Escobar,Herman FriedmanFlair sequences are used to populate our model, and a variational approach is implemented to find a solution. The performance of our model is demonstrated on two datasets, and compared to manual delineations by expert raters.作者: conjunctivitis 時間: 2025-3-24 11:16 作者: 泰然自若 時間: 2025-3-24 16:37
Automated Segmentation of Multiple Sclerosis Lesions Using Multi-dimensional Gated Recurrent Unitsg rank?#1 in the ISBI 2015 longitudinal multiple sclerosis lesion segmentation challenge, we show that a setup which combines these techniques can outperform the state of the art in automated lesion segmentation.作者: 仇恨 時間: 2025-3-24 20:52
Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentationodalities. The addition of JIF synthesized images improved the Dice-Sorensen coefficient (relative to manually drawn gold standards) of lesion segmentations over the standard model segmentations by . (mean ± standard deviation) at optimal threshold over all subjects and 10 separate training/testing folds.作者: 水汽 時間: 2025-3-24 23:10
Overall Survival Time Prediction for High Grade Gliomas Based on Sparse Representation Frameworkepresentation to classification. Experiment results show that, with 10-fold cross-validation, the proposed method achieves the accuracy of 94.83% and 95.69% by using T1 contrast-enhanced and T2 weighted magnetic resonance images, respectively.作者: NOCT 時間: 2025-3-25 04:59 作者: 直覺沒有 時間: 2025-3-25 10:35
Pairwise, Ordinal Outlier Detection of?Traumatic Brain Injuriesnt Post-Concussive Symptoms in a small dataset but achieve only a .74 AUC in identifying mTBI subjects with milder symptoms. Finally, we perform subject-specific simulations which characterize which injuries are detected and which are missed.作者: Lipoprotein(A) 時間: 2025-3-25 13:10 作者: PTCA635 時間: 2025-3-25 19:38 作者: anticipate 時間: 2025-3-25 20:53
0302-9743 national Multimodal Brain Tumor Segmentation, BraTS, and White Matter Hyperintensities, WMH, segmentation challenges, which were held jointly at the Medical Image computing for Computer Assisted Intervention Conference, MICCAI, in Quebec City, Canada, in September 2017. ..The 40 papers presented in 作者: Duodenitis 時間: 2025-3-26 02:10 作者: Enthralling 時間: 2025-3-26 04:50 作者: Prophylaxis 時間: 2025-3-26 11:18
https://doi.org/10.1007/978-3-319-54090-0embeds spatial information at multiple scales with deep supervision. (3) We show that the joint use of holistic CNNs and generalised Wasserstein Dice score achieves segmentations that are more semantically meaningful for brain tumour segmentation.作者: 新鮮 時間: 2025-3-26 13:49 作者: maintenance 時間: 2025-3-26 19:38 作者: propose 時間: 2025-3-26 22:40 作者: aqueduct 時間: 2025-3-27 02:16 作者: BOLT 時間: 2025-3-27 06:19 作者: 門閂 時間: 2025-3-27 09:55 作者: 違反 時間: 2025-3-27 15:47 作者: 催眠藥 時間: 2025-3-27 21:45
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain InjuriesThird International 作者: MEEK 時間: 2025-3-28 00:31 作者: 包裹 時間: 2025-3-28 03:35
Lesion Detection, Segmentation and Prediction in Multiple Sclerosis Clinical Trials Multiple Sclerosis (MS), assisting in the estimation of lesion volume, a common clinical measure of disease activity and stage. In the context of clinical trials, however, a wider number of metrics are required to determine the “burden of disease” and activity in order to measure treatment efficacy作者: 鐵砧 時間: 2025-3-28 07:45
Automated Segmentation of Multiple Sclerosis Lesions Using Multi-dimensional Gated Recurrent Unitse pathologic structures is not trivial, since location, shape and size can be arbitrary. Furthermore, the inherent class imbalance of about 1 lesion voxel to 10?000 healthy voxels further exacerbates the correct segmentation. We introduce a new MD-GRU setup, using established techniques from the dee作者: 彈藥 時間: 2025-3-28 11:45
Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentationoint intensity fusion (JIF). JIF synthesizes an image from a library of deformably registered and intensity normalized atlases. Each location in the synthesized image is a weighted average of the registered atlases; atlas weights vary spatially. The weights are determined using the joint label fusio作者: Nibble 時間: 2025-3-28 15:18 作者: 誤傳 時間: 2025-3-28 19:23
Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation Using?Holistic Convolutioused as a loss function for training convolutional neural networks (CNN). Although CNNs trained using mean-class Dice score achieve state-of-the-art results on multi-class segmentation, this loss function does neither take advantage of inter-class relationships nor multi-scale information. We argue 作者: 構(gòu)想 時間: 2025-3-28 23:09
Overall Survival Time Prediction for High Grade Gliomas Based on Sparse Representation Framework imaging-based survival prediction generally relies on some features guided by clinical experiences, which limits the full utilization of biomedical image. In this paper, we propose a sparse representation-based radiomics framework to predict overall survival (OS) time of HGG. Firstly, we develop a 作者: Hypomania 時間: 2025-3-29 04:57 作者: 導師 時間: 2025-3-29 10:16
Pairwise, Ordinal Outlier Detection of?Traumatic Brain Injuriesa model is challenging to construct. Instead, we utilize region-specific pairwise (person-to-person) comparisons. Each person-region is characterized by a distribution of Fractional Anisotropy and comparisons are made via Median, Mean, Bhattacharya and Kullback-Liebler distances. Additionally, we ex作者: 曲解 時間: 2025-3-29 14:47 作者: LATHE 時間: 2025-3-29 15:53 作者: nullify 時間: 2025-3-29 23:45
Deep Learning Based Multimodal Brain Tumor Diagnosisto address the challenges of multimodal brain tumor segmentation. The proposed multi-view deep learning framework (MvNet) uses three multi-branch fully-convolutional residual networks (Mb-FCRN) to segment multimodal brain images from different view-point, i.e. slices along x, y, z axis. The three su作者: hurricane 時間: 2025-3-30 01:53
Multimodal Brain Tumor Segmentation Using Ensemble of Forest Methodngle forest, we proposed two stage ensemble method for Multimodal Brain Tumor Segmentation problem. Identification of Tumor region and its sub-regions poses challenge in terms of variations in intensity, location etc. from patient to patient. We identify the initial region of interest (ROI) by linea作者: demote 時間: 2025-3-30 07:53 作者: 熔巖 時間: 2025-3-30 10:43 作者: 無禮回復 時間: 2025-3-30 16:13
Mechanisms and Ways of Macrophage Deliveryzed quantitative measure of segmentation accuracy in many applications, it offers a very limited picture of segmentation quality in complex segmentation tasks where the number of target objects is not known ., such as the segmentation of white matter lesions or lung nodules. While Dice overlap can s作者: BUCK 時間: 2025-3-30 16:54 作者: Paraplegia 時間: 2025-3-30 20:55 作者: 不斷的變動 時間: 2025-3-31 01:26
Leticia Reyes,Bryce Wolfe,Thaddeus Golosoint intensity fusion (JIF). JIF synthesizes an image from a library of deformably registered and intensity normalized atlases. Each location in the synthesized image is a weighted average of the registered atlases; atlas weights vary spatially. The weights are determined using the joint label fusio
