Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data
remains challenging owing to the complex structure of brain tumors, blurred boundaries, and …

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 …

The process of manually generating precise segmentations of brain tumors from magnetic
resonance images (MRI) is time-consuming and error-prone. We present a new algorithm …

The proposed method for fully-automatic brain tumor segmentation builds upon the
combined information from image appearance and image context. We employ a variety of …

Segmentation of medical imagery is a challenging problem due to the complexity of the
images, as well as to the absence of models of the anatomy that fully capture the possible …

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 …

Obtaining quantitative measures from biomedical images often requires segmentation, ie,
finding and outlining the structures of interest. Multi-modality imaging datasets, in which …

Brain tumor segmentation is an important procedure for early tumor diagnosis and
radiotherapy planning. Although numerous brain tumor segmentation methods have been …

The primary brain image segmentation goal is to partition a given brain image into different
regions representing anatomical structures. Magnetic resonance image (MRI) segmentation …

One of the challenging tasks in the medical area is brain tumor segmentation which consists
on the extraction process of tumor regions from images. Generally, this task is done …