  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. A modified UNET architecture
with generalized dice loss objective function was implemented. Three 2D CNNs
were trained, one for each image type obtained from the RADTSE sequence. On
evaluating the performance of the CNNs on the validation set, we found that
CNNs trained on TE images or the T2 maps had higher average dice scores than
the composite images. This, in turn, implies that the information regarding T2
variation in tissues aids in improving the segmentation performance.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning