The brain tumor is considered the deadly disease of the century. At present, neuroscience
and artificial intelligence conspire in the timely delineation, detection, and classification of …

This paper reviews the methods, benefits and challenges associated with the adoption and
translation of computational fluid dynamics (CFD) modelling within cardiovascular medicine …

Semantic image segmentation is the process of labeling each pixel of an image with its
corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a …

Despite recent progress of automatic medical image segmentation techniques, fully
automatic results usually fail to meet clinically acceptable accuracy, thus typically require …

Convolutional neural networks (CNNs) have achieved state-of-the-art performance for
automatic medical image segmentation. However, they have not demonstrated sufficiently …

The existence of humans and the preservation of the natural ecological equilibrium depend
greatly on trees. The semantic segmentation of trees is very important. It is crucial to learn …

Accurate medical image segmentation is essential for diagnosis, surgical planning and
many other applications. Convolutional Neural Networks (CNNs) have become the state-of …

The lack of annotated datasets is a major bottleneck for training new task-specific
supervised machine learning models, considering that manual annotation is extremely …

Segmentation of organs or lesions from medical images plays an essential role in many
clinical applications such as diagnosis and treatment planning. Though Convolutional …

In computer vision, edge and object contour detection is essential for higher-level vision
tasks, such as shape matching, visual salience, image segmentation, and object recognition …