Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 5th International Wo Alessandro Crimi,Spyridon Bakas Conferen

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Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluationatment, accurate delineation of brain tumors is of great importance. However, slice-by-slice manual segmentation on T1c MRI could be time-consuming (especially for multiple metastases) and subjective. In our work, we compared several deep convolutional networks architectures and training procedures

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3D U-Net Based Brain Tumor Segmentation and Survival Days Predictioneatment of brain tumors requires an accurate and reliable segmentation of brain tumors as a prerequisite. However, such work conventionally requires brain surgeons significant amount of time. Computer vision techniques could provide surgeons a relief from the tedious marking procedure. In this paper

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TuNet: End-to-End Hierarchical Brain Tumor Segmentation Using Cascaded Networksa high mortality rate, glioma treatment is also very expensive. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of the treatment. In the present work, we propose a novel end-t