Most existing CNNs-based segmentation methods rely on local appearances learned on the
regular image grid, without consideration of the object global information. This article aims to …

Accurate segmentation of anatomic structure is an essential task for biomedical image
analysis. Recent popular object contours regression based segmentation methods have …

Variational Level Set (LS) has been a widely used method in medical segmentation.
However, it is limited when dealing with multi-instance objects in the real world. In addition …

Image segmentation is a fundamental topic in image processing and has been studied for
many decades. Deep learning-based supervised segmentation models have achieved state …

Deep convolutional neural network can effectively extract hidden patterns in images and
learn realistic image priors from the training set. And fully convolutional networks (FCNs) …

Image segmentation is vital to medical image analysis and clinical diagnosis. Recently,
convolutional neural networks (CNNs) have achieved tremendous success in this task …

We present an image segmentation method that iteratively evolves a polygon. At each
iteration, the vertices of the polygon are displaced based on the local value of a 2D shift map …

Segmenting the region of interest (ROI) from medical images is a fundamental but
challenging task due to the illumination change and imaging devices. Although many …

Medical image segmentation is one of the most challenging tasks in medical image analysis
and widely developed for many clinical applications. While deep learning-based …

Accurate automatic image segmentation is important in medical image analysis. A perfect
segmentation using fully convolutional network (FCN) means an accurate classification of …