A Comparison of Deep Learning Convolution Neural Networks for Liver Segmentation in Radial Turbo Spin Echo Images

13 Apr 2020Lavanya UmapathyMahesh Bharath KeerthivasanJean-Phillipe GalonsWyatt UngerDiego MartinMaria I AltbachAli Bilgin

Motion-robust 2D Radial Turbo Spin Echo (RADTSE) pulse sequence can provide a high-resolution composite image, T2-weighted images at multiple echo times (TEs), and a quantitative T2 map, all from a single k-space acquisition. In this work, we use a deep-learning convolutional neural network (CNN) for the segmentation of liver in abdominal RADTSE images... (read more)

PDF Abstract

Code


No code implementations yet. Submit your code now

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet