AtlasNet: Multi-atlas Non-linear Deep Networks for Medical Image Segmentation

it in their minds during the diagnostic process, we took an image of a healthy person as reference image (such as images of the 1st row in Fig. 1) and the same image with

A series of experiments are designed to verify the anxiolytic effect of ethyl acetate extract and laetispicine in the light/dark test, elevated plus maze test and

We show our qualitative results in Figures 4.10 and 4.11. Although, our approach is an unsu- pervised learning method, it can generate the synthetic images which have the same

In this paper, we for- mulate the single image relighting task and propose a novel Deep Relighting Network (DRN) with three parts: 1) scene reconver- sion, which aims to reveal

Sidib´ e, Ex- ploration of Deep Learning-based Multimodal Fusion for Semantic Road Scene Segmentation, in: VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference

To summarise, our contributions are: (i) the propo- sition of a mathematically well-motivated and computation- ally tractable method to train a constrained deep network, the

In this step, the atlas images and label images are down-sampled and resized to generate low-resolution atlas images I Ai l (i = 1,. , N ) which are then aligned with the

X-Net+, evaluated using a 10-fold cross validation protocol, provides better results in the ground truth resolution than all the other methods in the state-of- the-art (9