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Glioma Tumor Segmentation Using Deep Cascaded � 3D Convolutional Neural Networks
13th International Conference on Laboratory Medicine & Pathology
Wiem Takrouni and Ali Douik
University of Sousse, Tunisia
Posters & Accepted Abstracts: Diagn Pathol ÌìÃÀ´«Ã½
DOI:
Abstract
Glioma is a brain tumor, mainly dangerous form of cancer which starts in the gluey supportive cells (glial cells) that encircle nerve cells and help them function. Glioma tumor brain structure segmentation in non-invasive magnetic resonance images (MRI) has enticed the interest of the research community for a long time because of morphological changes in these structures. In this poster, we propose a deep cascaded 3D convolutional neural network (DC-3DCNN) based method to segment glioma lesions from multi-contrast MR images. DC-3DCNN was evaluated on a challenge dataset, where the performance of our method is also compared with different current publicly available state-of-the-art lesion segmentation methods.Biography
I’m Wiem Takrouni from Tunisia, i’m a Phd Student in computer vision and Medical imaging in the (NOCCS Laboratory, National School of Engineering of Sousse, University of Sousse, Tunisia). I have three publications paper conferences: two publications in smart vehicule with deep learning and a publication in tumor reconstruction in medical imaging.
E-mail: Suhi14@yahoo.com
E-mail: takrouni.wiem@gmail.com
