Deep Neural Networks (DNNs) have recently shown outstanding performance on image
classification and segmentation tasks. This paper presents our work on applying DNNs to …

In this paper, we present a fully automatic brain tumor segmentation method based on Deep
Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low …

In this work, we propose a multi-modal Convolutional Neural Network (CNN) approach for
brain tumor segmentation. We investigate how to combine different modalities efficiently in …

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis,
treatment planning, and treatment outcome evaluation. Build upon successful deep learning …

Brain tumor segmentation is one of the most challenging problems in medical image
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …

This paper presents our work on applying 3D Convolutional Networks for brain tumor
segmentation for the BRATS challenge. We are currently experimenting with different 3D …

Quantitative analysis of the brain tumors provides valuable information for understanding the
tumor characteristics and treatment planning better. The accurate segmentation of lesions …

Brain lesion segmentation is one of the hardest tasks to be solved in computer vision with an
emphasis on the medical field. We present a convolutional neural network that produces a …

In this study, brain tumor substructures are segmented using 2D fully convolutional neural
networks. A number of modifications such as double convolution layers, inception modules …

Accurate automatic algorithms for the segmentation of brain tumours have the potential of
improving disease diagnosis, treatment planning, as well as enabling large-scale studies of …