updaye readme

ReadME.md

@@ -38,14 +38,14 @@ Results will be saved under `test_results` by default.
 |  	| UKBB test (600) 	|  	|  	| ACDC (100) 	|  	|  	| M&Ms (150) 	|  	|  	|
 |-	|:-:	|:-:	|:-:	|:-:	|:-:	|:-:	|:-:	|:-:	|:-:	|
 | configurations 	| LV 	| MYO 	| RV 	| LV 	| MYO 	| RV 	| LV 	| MYO 	| RV 	|
-| bs=1, roi size = 256, z_score 	| 0.9383 	| 0.8780 	| 0.8979 	| 0.8940 	| 0.8034 	| 0.8237 	| 0.8862 	| 0.7889 	| 0.8168 	|
-| bs=1, roi size = 192, z_score 	| 0.9371 	| 0.8775 	| 0.8962 	| 0.8891 	| 0.7981 	| 0.8103 	| 0.8725 	| 0.7716 	| 0.7954 	|
-| bs=-1, roi size = 192, z_score 	| 0.9139 	| 0.8388 	| 0.8779 	| 0.8790 	| 0.7818 	| 0.8069 	| 0.8679 	| 0.7675 	| 0.7926 	|
+| batch_size = 1, roi size = 256, z_score 	| 0.9383 	| 0.8780 	| 0.8979 	| 0.8940 	| 0.8034 	| 0.8237 	| 0.8862 	| 0.7889 	| 0.8168 	|
+| batch_size = 1, roi size = 192, z_score 	| 0.9371 	| 0.8775 	| 0.8962 	| 0.8891 	| 0.7981 	| 0.8103 	| 0.8725 	| 0.7716 	| 0.7954 	|
+| batch_size = -1 (use the whole volume as batch), roi size = 192, z_score 	| 0.9139 	| 0.8388 	| 0.8779 	| 0.8790 	| 0.7818 	| 0.8069 	| 0.8679 	| 0.7675 	| 0.7926 	|
 
 The model params are stored in `./checkpoints/Unet_LVSA_trained_from_UKBB.pkl`
 For optimal performance at deployment, please set it to instance normalization (--batch_size 1) and crop images to (--roi_size 256). 
 e.g. 
-- If we want to predict test images from UKBB, we can simply ran
+- If we want to predict test images from UKBB, we can simply run the following command:
 `python predict.py --sequence LVSA \
 --model_path './checkpoints/Unet_LVSA_trained_from_UKBB.pkl' \
 --root_dir '/vol/medic02/users/wbai/data/cardiac_atlas/UKBB_2964/sa/test' \