Archive of SID Multidisciplinary Cancer Investigation Original Article © 2020. Multidisciplinary Cancer Investigation DOI:10.30699/acadpub.mci.4.1.5 Submitted: 9 November 2019 Revised: 12 December 2019 Accepted: 23 December 2019 e-Published: 1 January 2020 Keywords: Brain Neoplasms Nerve Net Magnetic Resonance Imaging Introduction: Brain tumors such as glioma are among the most aggressive lesions, which result in a very short life expectancy in patients. Image segmentation is highly essential in medical image analysis with applications, particularly in clinical practices to treat brain tumors. Accurate segmentation of magnetic resonance data is crucial for diagnostic purposes, planning surgical treatments, and also follow-up evaluation. Man- ual segmentation of a large volume of MRI data is a time-consuming endeavor, and this necessitates employing automatic segmentation techniques that are both accurate and reliable. However, the vast spatial and structural diversity of brain tissue poses serious challenges for this procedure. The current study proposed an automatic segmentation method based on convolutional neural networks (CNN), where weights of a pre-trained network were used as initial weights of neurons to prevent possible overftting in the training phase. Methods: As tumors were diverse in their shape, size, location, and overlapping with other tissue, it was decided to exploit a fexible and extremely effcient architecture tailored to glioblastoma. To remove some of the overlapping diffculties, morphological operators as a pre-processing step were utilized to strip the skull. Results: The proposed CNN had a hierarchical architecture to exploit local and global contextual features to handle both high- and low-grade glioblastoma. To address bias- ing stem from the imbalance of tumor labels, dropout was employed and a stochastic pooling layer was proposed. Conclusions: Experimental results reported on a dataset of 400 brain MR images sug- gested that the proposed method outperformed the currently published state-of-the-art approach in terms of various image quality assessment metrics and achieved magnitude fold speed-up. A Hierarchical Convolutional Neural Network Architecture for Brain Tumor Segmentation in 3D Brain Magnetic Resonance Imaging Ayoub Adineh-vand 1 , Gholamreza Karimi 1, * , Mozafar Khazaei 2 1 Electrical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran 2 Fertility and Infertility Research Center, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran *Corresponding author: Gholamreza Karimi, Electrical Engineering, Faculty of Engineering, Razi University, Postal Code: 67149, Kermanshah, Iran. Tel: +988334343218, Fax: +988334343211, E-mail: ghkarimi@razi.ac.ir Image segmentation aims at partitioning images into homogeneous regions by employing spatial- spectral attributes of the image. A segmentation algorithm assigns a unique label to each pixel and defnes segmented regions based on all pixels serving certain criteria. This task has an important INTRODUCTION January 2020, Volume 4, Issue 1 www.SID.ir