Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …

Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

Abstract Despite that Convolutional Neural Networks (CNNs) have achieved promising
performance in many medical image segmentation tasks, they rely on a large set of labeled …

Edge detection is one of the most important and fundamental problems in the field of
computer vision and image processing. Edge contours extracted from images are widely …

Deep learning architecture with convolutional neural network achieves outstanding success
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …

Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …

The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …

We present a model-agnostic post-processing scheme to improve the boundary quality for
the segmentation result that is generated by any existing segmentation model. Motivated by …

In semi-supervised medical image segmentation, most previous works draw on the common
assumption that higher entropy means higher uncertainty. In this paper, we investigate a …

Semantic segmentation of nighttime images plays an equally important role as that of
daytime images in autonomous driving, but the former is much more challenging due to poor …