標題: Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; First International Alessandro Crimi,Bjoern Menze,Heinz Hand [打印本頁] 作者: Fixate 時間: 2025-3-21 16:39
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: 大廳 時間: 2025-3-21 20:35
Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRIs and Low Grade Gliomas we trained two different architectures, one for each grade. Using the proposed method it was possible to obtain promising results in the 2015 Multimodal Brain Tumor Segmentation (BraTS) data set, as well as the second position in the on-site challenge.作者: badinage 時間: 2025-3-22 01:31 作者: Cytology 時間: 2025-3-22 04:36 作者: fledged 時間: 2025-3-22 10:44
Conference proceedings 2016ce on Conferenceon Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015...The 25papers presented in this volume were carefully reviewed and selected from 28submissions. They are grouped around the following topics: brain lesion imageanalysis; brain tumor image segmentation; ischemic stroke lesion imagesegmentation..作者: Paraplegia 時間: 2025-3-22 15:11
Macroevolution in Human Prehistory segmentation and exclude voxels labeled as CSF, ventricles and hemorrhagic lesion and then automatically detect the lesion load. Preliminary results demonstrate that our method is coherent with expert opinion in the identification of lesions. We outline the challenges posed in automatic analysis for TBI.作者: PLAYS 時間: 2025-3-22 19:56
https://doi.org/10.1057/9780230604315s and Low Grade Gliomas we trained two different architectures, one for each grade. Using the proposed method it was possible to obtain promising results in the 2015 Multimodal Brain Tumor Segmentation (BraTS) data set, as well as the second position in the on-site challenge.作者: 傀儡 時間: 2025-3-23 00:40
Wolfgang Sch?nfeld,Stjepan Mutakhat parameter learning leads to comparable or even improved performance. In addition, we also performed experiments to study the impact of the composition of training data on the final segmentation performance. We found that models trained on mixed data sets achieve reasonable performance compared to models trained on stratified data.作者: FER 時間: 2025-3-23 02:21
Rituparna Bose,Alexander J. Bartholomewal features, which have the benefit of no computational overhead and easy extraction from the MR images. On MR images of 18 patients with multiple sclerosis the proposed method achieved the median Dice similarity of 0.73, sensitivity of 0.90 and positive predictive value of 0.61, which indicate accurate segmentation of white-matter lesions.作者: GAVEL 時間: 2025-3-23 08:54
Principle Of Social Subsistenceases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.作者: AFFIX 時間: 2025-3-23 13:36 作者: 提煉 時間: 2025-3-23 15:43 作者: VOK 時間: 2025-3-23 19:29 作者: 間接 時間: 2025-3-23 22:10 作者: 歡笑 時間: 2025-3-24 02:54
Macroevolution in Human Prehistoryts have revealed high similarity between the segmentation performed with this method and that performed manually by an expert operator, as well as a low misclassification of lesions. Moreover, the time required for segmentation is drastically reduced.作者: Assemble 時間: 2025-3-24 06:48
Principle Of Social Subsistence among them a second place. The outcome underlines the robustness of our features for segmentation in brain MR, while simultaneously stressing the necessity for highly specialized solution to achieve state-of-the-art performance.作者: Tdd526 時間: 2025-3-24 11:54
Holly M. Mattoes,Charles H. Nightingale, without the use of the . in the training images. Experiments on public benchmark data of patients suffering from low- and high-grade gliomas show that the method performs well compared to current state-of-the-art methods, while not being tied to any specific imaging protocol.作者: 報復(fù) 時間: 2025-3-24 16:54
Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probset in multiple sclerosis indicate that the method’s lesion segmentation accuracy compares well to that of the current state-of-the-art in the field, while additionally providing robust whole-brain segmentations.作者: babble 時間: 2025-3-24 22:15 作者: chalice 時間: 2025-3-24 23:44
A Semi-automatic Method for Segmentation of Multiple Sclerosis Lesions on Dual-Echo Magnetic Resonants have revealed high similarity between the segmentation performed with this method and that performed manually by an expert operator, as well as a low misclassification of lesions. Moreover, the time required for segmentation is drastically reduced.作者: BRINK 時間: 2025-3-25 03:48
Image Features for Brain Lesion Segmentation Using Random Forests among them a second place. The outcome underlines the robustness of our features for segmentation in brain MR, while simultaneously stressing the necessity for highly specialized solution to achieve state-of-the-art performance.作者: 飛來飛去真休 時間: 2025-3-25 08:26
Brain Tumor Segmentation Using a Generative Model with an RBM Prior on Tumor Shape, without the use of the . in the training images. Experiments on public benchmark data of patients suffering from low- and high-grade gliomas show that the method performs well compared to current state-of-the-art methods, while not being tied to any specific imaging protocol.作者: Adornment 時間: 2025-3-25 13:41
0302-9743 in Lesion (BrainLes), Brain Tumor Segmentation (BRATS) andIschemic Stroke Lesion Segmentation (ISLES), held in Munich, Germany, onOctober 5, 2015, in conjunction with the International Conference on Conferenceon Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015...The 25papers p作者: 兵團 時間: 2025-3-25 16:54
Macroevolution in Human Prehistoryere misregistration. In this paper, it is proposed to quantitatively assess the impact of large stroke lesions onto the registration process. To reduce this impact, a new registration algorithm, that localizes the lesion via Bayesian estimation, is proposed.作者: 察覺 時間: 2025-3-25 22:20
Bayesian Stroke Lesion Estimation for Automatic Registration of DTI Imagesere misregistration. In this paper, it is proposed to quantitatively assess the impact of large stroke lesions onto the registration process. To reduce this impact, a new registration algorithm, that localizes the lesion via Bayesian estimation, is proposed.作者: 古文字學 時間: 2025-3-26 01:18 作者: SPALL 時間: 2025-3-26 06:35
Stroke Lesion Segmentation Using a Probabilistic Atlas of Cerebral Vascular Territories by a spatial atlas of stroke lesion occurrence by making use of information about the vascular territories. As the territories of the major arterial trees often coincide with the location and extensions of large stroke lesions, we use 3D maps of the vascular territories to form patient-specific atl作者: 安慰 時間: 2025-3-26 11:42 作者: IRS 時間: 2025-3-26 13:15
A Quantitative Approach to Characterize MR Contrasts with Histologyinterest and mapped on the target non-specific modalities through co-registration. These non-overlapping ROIs were considered ground truth for later classification. Voxels were evenly split in training and testing sets for a logistic regression model. The statistical significance of resulting accura作者: GRIPE 時間: 2025-3-26 16:57
Multi-modal Brain Tumor Segmentation Using Stacked Denoising Autoencoderss respectively. Two different networks were trained one with high grade glioma (HGG) data and other with a combination of high grade and low grade gliomas (LGG). Each network was trained with 35 patients for pre-training and 21 patients for fine tuning. The predictions from the two networks were com作者: Diuretic 時間: 2025-3-26 23:02
A Convolutional Neural Network Approach to Brain Tumor Segmentationges (BRATS’13, BRATS’15) and Ischemic Stroke Lesion Segmentation challenge (ISLES’15) reveal that our approach is among the most accurate in the literature, while also being computationally very efficient.作者: 漂亮 時間: 2025-3-27 03:05 作者: flamboyant 時間: 2025-3-27 07:51 作者: 巧辦法 時間: 2025-3-27 13:01
https://doi.org/10.1007/978-1-4419-0682-3interest and mapped on the target non-specific modalities through co-registration. These non-overlapping ROIs were considered ground truth for later classification. Voxels were evenly split in training and testing sets for a logistic regression model. The statistical significance of resulting accura作者: mendacity 時間: 2025-3-27 14:21
Macrolides and Interstitial Lung Diseasess respectively. Two different networks were trained one with high grade glioma (HGG) data and other with a combination of high grade and low grade gliomas (LGG). Each network was trained with 35 patients for pre-training and 21 patients for fine tuning. The predictions from the two networks were com作者: 使厭惡 時間: 2025-3-27 18:25 作者: 符合你規(guī)定 時間: 2025-3-28 01:28 作者: DRILL 時間: 2025-3-28 02:16 作者: Insubordinate 時間: 2025-3-28 07:54 作者: EXULT 時間: 2025-3-28 10:39
Stroke Lesion Segmentation Using a Probabilistic Atlas of Cerebral Vascular Territorieson with functional areas and for determining the optimal strategy for patient treatment. Manual labeling of each lesion turns out to be time-intensive and costly, making an automated method desirable. Standard approaches for brain parcellation make use of spatial atlases that represent prior informa作者: 網(wǎng)絡(luò)添麻煩 時間: 2025-3-28 16:28
Fiber Tracking in Traumatic Brain Injury: Comparison of 9 Tractography Algorithmso this disruption. Even so, traumatic injury often disrupts brain morphology as well, complicating the analysis of brain integrity and connectivity, which are typically evaluated with tractography methods optimized for analyzing normal healthy brains. To understand which fiber tracking methods show 作者: BOOR 時間: 2025-3-28 19:39
Combining Unsupervised and Supervised Methods for Lesion Segmentations. We present a novel automated lesion segmentation method consisting of an unsupervised mixture model based extraction of candidate lesion voxels, which are subsequently classified by a random decision forest (RDF) using simple visual features like multi-sequence MR intensities sourced from connect作者: alcoholism 時間: 2025-3-29 01:37 作者: 惡心 時間: 2025-3-29 06:00
A Nonparametric Growth Model for Brain Tumor Segmentation in Longitudinal MR Sequencesor multimodal brain tumor segmentation, we make use of a nonparametric growth model that is implemented as a conditional random field (CRF) including directed links with infinite weight in order to incorporate growth and inclusion constraints, reflecting our prior belief on tumor occurrence in the d作者: 刪除 時間: 2025-3-29 08:51 作者: 賞錢 時間: 2025-3-29 13:33
Bayesian Stroke Lesion Estimation for Automatic Registration of DTI Imagesker for stroke recovery. This measure is highly sensitive to applied pre-processing steps; in particular, the presence of a lesion may result into severe misregistration. In this paper, it is proposed to quantitatively assess the impact of large stroke lesions onto the registration process. To reduc作者: 規(guī)范就好 時間: 2025-3-29 17:47 作者: handle 時間: 2025-3-29 22:48
Image Features for Brain Lesion Segmentation Using Random Forestsin MR scans. This paper describes a set of hand-selected, voxel-based image features highly suitable for the tissue discrimination task. Embedded in a random decision forest framework, the proposed method was applied to sub-acute ischemic stroke (ISLES 2015 - SISS), acute ischemic stroke (ISLES 2015作者: 氣候 時間: 2025-3-29 23:55
Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRIs for follow-up evaluation. In this paper, we propose to segment brain tumors using a Deep Convolutional Neural Network. Neural Networks are known to suffer from overfitting. To address it, we use Dropout, Leaky Rectifier Linear Units and small convolutional kernels. To segment the High Grade Glioma作者: endarterectomy 時間: 2025-3-30 05:13
GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modela hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification sc作者: 食道 時間: 2025-3-30 10:58
Parameter Learning for CRF-Based Tissue Segmentation of Brain Tumorspairwise CRF are estimated via a stochastic subgradient descent of a max-margin learning problem. We compared the performance of our brain tumor segmentation method using parameter learning to a version using hand-tuned parameters. Preliminary results on a subset of the BRATS2015 training set show t作者: Monolithic 時間: 2025-3-30 16:27 作者: NEX 時間: 2025-3-30 18:15
Multi-modal Brain Tumor Segmentation Using Stacked Denoising Autoencoders segmentation is time consuming, an automated method can be useful, especially in large clinical studies. Since Gliomas have variable shape and texture, automated segmentation is a challenging task and a number of techniques based on machine learning algorithms have been proposed. In the recent past作者: 巡回 時間: 2025-3-30 23:55
A Convolutional Neural Network Approach to Brain Tumor Segmentationlesion. We propose a Convolutional Neural Network (CNN) approach which is amongst the top performing methods while also being extremely computationally efficient, a balance that existing methods have struggled to achieve. Our CNN is trained directly on the image modalities and thus learns a feature 作者: 直覺沒有 時間: 2025-3-31 03:40 作者: 緯線 時間: 2025-3-31 08:42 作者: 忘川河 時間: 2025-3-31 10:28 作者: 金盤是高原 時間: 2025-3-31 13:21
