MR images (MRIs) accurate segmentation of brain lesions is important for improving cancer diagnosis, surgical planning, and prediction of outcome. However, manual and accurate segmentation of brain lesions from 3D MRIs is highly expensive, time-consuming, and prone to user biases. We present an efficient yet conceptually simple brain segmentation network (referred as Brain SegNet), which is a 3D residual framework for automatic voxel-wise segmentation of brain lesion. Our model is able to directly predict dense voxel segmentation of brain tumor or ischemic stroke regions in 3D brain MRIs. The proposed 3D segmentation network can run at about 0.5s per MRIs – about 50 times faster than previous approaches Med Image Anal 43: 98-111, 2018, Med Image Anal 36:61-78, 2017. Our model is evaluated on the BRATS 2015 benchmark for brain tumor segmentation, where it obtains state-of-the-art results, by surpassing recently published results reported in Med Image Anal 43: 98-111, 2018, Med Image Anal 36:61-78, 2017. We further applied the proposed Brain SegNet for ischemic stroke lesion outcome prediction, with impressive results achieved on the Ischemic Stroke Lesion Segmentation (ISLES) 2017 database.
January 23, 2015
Potential Therapeutic Application of Zinc Oxide Nanoflowers in Cerebral Ischemia Rat Model Through Neuritogenic and Neuroprotective Properties.
February 13, 2020
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ASCO 2019The 2019 ASCO Annual Meeting, taking place May 31-June 4 in Chicago, will bring together more than 32,000 oncology professionals from across the globe. The theme of this year’s conference is Caring for Every Patient, Learning From Every Patient.