Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks

2018-01-08 · Yuhua Chen, Yibin Xie, Zhengwei Zhou, Feng Shi, Anthony G. Christodoulou, Debiao Li

Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical information and is often necessary for accurate quantitative analysis. However, high spatial resolution typically comes at the expense of longer scan time, less spatial coverage, and lower signal to noise ratio (SNR). Single Image Super-Resolution (SISR), a technique aimed to restore high-resolution (HR) details from one single low-resolution (LR) input image, has been improved dramatically by recent breakthroughs in deep learning. In this paper, we introduce a new neural network architecture, 3D Densely Connected Super-Resolution Networks (DCSRN) to restore HR features of structural brain MR images. Through experiments on a dataset with 1,113 subjects, we demonstrate that our network outperforms bicubic interpolation as well as other deep learning methods in restoring 4x resolution-reduced images.

📄 PDF Abstract BibTeX arXiv:1801.02728

Code (0)

등록된 구현이 없습니다.

Tasks

Deep LearningImage Super-Resolution Super-Resolution

On the Brain Networks of Complex Problem Solving

2018-10-10 · Abdullah Alchihabi, Omer Ekmekci, Baran B. Kivilcim, Sharlene D. Newman 외

Complex problem solving is a high level cognitive process which has been thoroughly studied over the last decade. The Tower of London (TOL) is a task that has been widely used to study problem-solving. In this study, we …

Optimized High Resolution 3D Dense-U-Net Network for Brain and Spine Segmentation

2019-01-25 · Applied Sciences 2019 1 · Martin Kolařík, Radim Burget, Václav Uher, Kamil Říha 외

The 3D image segmentation is the process of partitioning a digital 3D volumes into multiple segments. This paper presents a fully automatic method for high resolution 3D volumetric segmentation of medical image data usin…

Image Segmentation Segmentation Semantic Segmentation UNET Segmentation

Isointense Infant Brain Segmentation with a Hyper-dense Connected Convolutional Neural Network

2017-10-16 · Jose Dolz, Ismail Ben Ayed, Jing Yuan, Christian Desrosiers

Neonatal brain segmentation in magnetic resonance (MR) is a challenging problem due to poor image quality and low contrast between white and gray matter regions. Most existing approaches for this problem are based on mul…

Brain Segmentation Infant Brain Mri Segmentation MRI segmentation Segmentation

DVNet: A Memory-Efficient Three-Dimensional CNN for Large-Scale Neurovascular Reconstruction

2020-02-04 · Leila Saadatifard, Aryan Mobiny, Pavel Govyadinov, Hien Nguyen 외

Maps of brain microarchitecture are important for understanding neurological function and behavior, including alterations caused by chronic conditions such as neurodegenerative disease. Techniques such as knife-edge scan…

Decoder Segmentation Semantic Segmentation

Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network

2018-03-04 · Yuhua Chen, Feng Shi, Anthony G. Christodoulou, Zhengwei Zhou 외

High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical information important for clinical application and quantitative image analysis. However, HR MRI conventionally comes at the cost of longer …

Generative Adversarial Network Image Super-Resolution Super-Resolution