… basic tasks is known as panoptic segmentation. In the recent era, the success of deep
convolutional neural networks (CNN) has influenced the field of segmentation greatly and gave us …

… used framework – convolutional neural networks (CNN) is … the deep networks developed for
image segmentation. Under this … segmentation operation ie semantic segmentation, instance …

… automatic segmentation of MR brain images into a number of tissue classes using a
convolutional neural network. To ensure that the method obtains accurate segmentation details as …

… In this work, we used 3D convolutional neural networks for brain segmentation. 3D DCNNs
have been used for medical applications before (Li et al., 2014, Brebisson and Montana, …

… image segmentation tasks. To improve the performance of U-Net on various segmentation
tasks… called DoubleU-Net, which is a combination of two U-Net architectures stacked on top of …

… Convolutional neural networks represent a growing field … of segmentation involving
convolutional neural networks. The … relating each step of the segmentation process. The main …

… automatic brain focal pathology segmentation, in MR images … We propose a Convolutional
Neural Network (CNN) approach … convolutional nature of our model also allows us to segment …

… This paper shows how a convolutional neural network (CNN) … to improve the high-level
segmentation. Various design choices of … The results of the full classification and segmentation on …

… 3D segmentation approach that leverages the power of a fully convolutional neural network,
… a novel objective function designed specifically for medical image segmentation, which …

… , the algorithms of convolutional neural network (CNN) … segmentation. While medical imaging
classification has been widely reported, the object detection and semantic segmentation of …