Motion-robust 2D-RADTSE can provide a high-resolution composite, T2-weighted images at multiple echo times (TEs), and a quantitative T2 map, all from a single k-space acquisition. We use deep-learning CNN for segmentation of liver in abdominal RADTSE images. An enhanced UNET architecture with generalized dice loss based objective function was implemented. Three nets were trained, one for each image type obtained from the sequence. On evaluating net performances on the validation set, we found that nets trained on TE images or T2 maps had higher average dice scores than the one trained on composites, implying information regarding T2 variation aids in segmentation.