Convolution

Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI
Author(s)3: Seonyeong Park, H. Michael Gach, Siyong Kim, Suk Jin Lee, Yuichi Motai
Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI 150 150 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

Objective: To introduce an MRI in-plane resolution enhancement method that estimates High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs. Method & Materials: Previous CNN-based MRI super-resolution methods cause loss of input…

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Cardiac-DeepIED: Automatic Pixel-level Deep Segmentation for Cardiac Bi-ventricle Using Improved End-to-End Encoder-Decoder Network

Cardiac-DeepIED: Automatic Pixel-level Deep Segmentation for Cardiac Bi-ventricle Using Improved End-to-End Encoder-Decoder Network
Author(s)3: Xiuquan Du, Susu Yin, Renjun Tang, Yanping Zhang, Shuo Li
Cardiac-DeepIED: Automatic Pixel-level Deep Segmentation for Cardiac Bi-ventricle Using Improved End-to-End Encoder-Decoder Network 780 435 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM)

     Abstract Accurate segmentation of cardiac bi-ventricle (CBV) from magnetic resonance (MR) images has a great significance to analyze and evaluate the function of the cardiovascular system. However, the complex…

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