2021/08/15 by Daniel E. Cahall, Ghulam Rasool, Cahall, Daniel E. +7 · 11 citations
Computer Science · Engineering · Mathematics · Medicine · Neuroscience · #Advanced Neural Network Applications #Artificial intelligence #Artificial neural network #Brain Tumor Detection and Classification #Brain tumor #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Image segmentation #Machine Learning (cs.LG) #Magnetic resonance imaging #Mathematics #Medical Image Segmentation Techniques #Medicine #Net (polyhedron) #Pathology #Pattern recognition (psychology) #Radiology #Segmentation #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2108.06772
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/08/15 · openalex publication_date 2021/08/15 · arxiv updated 2021/08/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Magnetic resonance imaging (MRI) is routinely used for brain tumor diagnosis, treatment planning, and post-treatment surveillance. Recently, various models based on deep neural networks have been proposed for the pixel-level segmentation of tumors in brain MRIs. However, the structural variations, spatial dissimilarities, and intensity inhomogeneity in MRIs make segmentation a challenging task. We propose a new end-to-end brain tumor segmentation architecture based on U-Net that integrates Inception modules and dilated convolutions into its contracting and expanding paths. This allows us to extract local structural as well as global contextual information. We performed segmentation of glioma sub-regions, including tumor core, enhancing tumor, and whole tumor using Brain Tumor Segmentation (BraTS) 2018 dataset. Our proposed model performed significantly better than the state-of-the-art U-Net-based model (p<0.05) for tumor core and whole tumor segmentation.