Most of the current state-of-the-art methods for tumor segmentation are based on machine
learning models trained manually on segmented images. This type of training data is …

Manual segmentation of brain tumor is often time consuming and the performance of the
segmentation varies based on the operators experience. This leads to the requisition of a …

The introduction of quantitative image analysis has given rise to fields such as radiomics
which have been used to predict clinical sequelae. One growing area of interest for analysis …

We propose a novel, multi-task, fully convolutional network (FCN) architecture for automatic
segmentation of brain tumor. This network extracts multi-level contextual information by …

Gliomas are the most frequent primary brain tumors, which have a high mortality. Surgery is
the most commonly used treatment. Magnetic resonance imaging (MRI) is especially useful …

We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified
nnU-Net baseline configuration already achieves a respectable result. By incorporating …

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

In this work we approach the brain tumor segmentation problem with a cascade of two CNNs
inspired in the V-Net architecture [13], reformulating residual connections and making use of …

Thanks to the powerful representation learning ability, convolutional neural network has
been an effective tool for the brain tumor segmentation task. In this work, we design multiple …

Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of
focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an …