Abstract Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art
performance in medical image segmentation tasks. A common feature in most top …

We propose a deep learning based approach for automatic brain tumor segmentation
utilizing a three-dimensional U-Net extended by residual connections. In this work, we did …

A deep convolutional neural network (CNN) achieves remarkable performance for medical
image analysis. UNet is the primary source in the performance of 3D CNN architectures for …

Tumor segmentation is an important research topic in medical image segmentation. With the
fast development of deep learning in computer vision, automated segmentation of brain …

Brain tumor segmentation from medical images is a prerequisite to provide a quantitative
and intuitive reference for clinical diagnosis and treatment. Manual segmentation depends …

Brain tumors are one of the leading causes of death in adults. They come in various shapes
and sizes from one patient to another. Sometimes, they infiltrate surrounding normal tissues …

The brain is the center of human control and communication. Hence, it is very important to
protect it and provide ideal conditions for it to function. Brain cancer remains one of the …

Recently, U-Net architecture with its strong adaptability has become prevalent in the field of
MRI brain tumor segmentation. Meanwhile, researchers have demonstrated that introducing …

The utilization of different types of brain images has been expanding, which makes manually
examining each image a labor-intensive task. This study introduces a brain tumor …

Accurate segmentation of brain tumors is an essential stage in treatment planning. Fully
convolutional neural networks, specifically the encoder-decoder architectures such as U-net …