Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 6th International Wo Alessandro Crimi,Spyridon Bakas Conferen

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書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries被引頻次




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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries978-3-030-72087-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

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腐爛

Lois T. Hunt,Margaret O. Dayhoffccurate treatment planning is key. Magnetic resonance imaging (MRI) is a widely used imaging technique for the assessment of these tumours but the large amount of data generated by them prevents rapid manual segmentation, the task of dividing visual input into tumorous and non-tumorous regions. Henc

Dysplasia

handle

https://doi.org/10.1007/978-3-642-60986-2 methods by implementing the U-Net model and trialing various modifications to the training and inference strategies. The trials were performed and tested on the Multimodal Brain Tumor Segmentation dataset that provides MR images of brain tumors along with manual segmentations for hundreds of subjec

蒸發(fā)

Polymer-Metal Complexes in Living Systems, MRI images. Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time-consuming, and subjective, this task is at the same time very challenging to automatic segmentation methods. Thanks to the powerful learning ability, convolutional

Obvious

https://doi.org/10.1007/978-1-4899-2809-2et uses the single and cascaded HNF-Nets to segment different brain tumor sub-regions and combines the predictions together as the final segmentation. We trained and evaluated our model on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. The results on the test set show that t

意外

Radiation-Activated Polymerizationn tumour segmentation may be more compatible with current MRI acquisition protocols than 3D methods because clinical MRI is most commonly a slice-based modality. A 2D Dense-UNet segmentation model was trained on the BraTS 2020 dataset. Mean Dice values achieved on the test dataset were: 0.859 (WT),