Abstract: Deep neural network (DNN)-based image segmentation has the endemic problems caused by iterative sampling, i.e., inaccurate and uncertain boundaries. On the other hand, conventional image ...

Neural networks are powerful tools for processing visual inputs ... function of recurrent connections in biological visual systems. We study image segmentation using spatiotemporal dynamics in a ...

By breaking an image into smaller groups called “image segments,” an image segmentation ... based U-NET technique was used to find extrapolation-based reconstructions for alloys, porous media, and ...

This paper proposes an end-to-end trained fully convolutional neural network model to process 3D image ... using the Dice coefficient is proposed to be optimized, and the authors demonstrate the fast ...

The Major Steps involved in the Detection of kidney stone using Deep Neural Networks ... on the image resulting from gamma adjustment to allow segmentation of image the foreground (stone and bones) ...

And for image processing ... the usual way through forward inference and backpropagation. At the end is a pixel-wise prediction layer that will create the segmentation map. The decoder has a number of ...

Therefore, multimodal medical imaging has been widely used in the segmentation of gliomas through computational neural ... sub-network uses a dual encoder structure (DES) and a channel spatial ...

Therefore, this paper completes the task of brain tumor segmentation by building a self-supervised deep learning network. Specifically, it designs a multi-modal encoder-decoder network based on the ..