update configure files and readme, using pymic_net_run

Author: taigw

segmentation/JSRT/README.md

@@ -22,7 +22,7 @@ If you don't want to train the model by yourself, you can download a pre-trained
 1. Edit `config/train_test.cfg` by setting the value of `root_dir` as your `JSRT_root`. Then start to train by running:
 
 ```bash
-pymic_net_run_seg train config/train_test.cfg
+pymic_net_run  train config/train_test.cfg
 ```
 
 2. During training or after training, run `tensorboard --logdir model/unet` and you will see a link in the output, such as `http://your-computer:6006`. Open the link in the browser and you can observe the average Dice score and loss during the training stage, such as shown in the following images, where blue and red curves are for training set and validation set respectively. We can observe some over-fitting on the training set. 
@@ -35,7 +35,7 @@ pymic_net_run_seg train config/train_test.cfg
 
 ```bash
 mkdir result
-pymic_net_run_seg test config/train_test.cfg
+pymic_net_run test config/train_test.cfg
 ```
 
 2. Then edit `config/evaluation.cfg` by setting `ground_truth_folder_root` as your `JSRT_root`, and run the following command to obtain quantitative evaluation results in terms of dice. 

segmentation/JSRT/config/train_test.cfg

@@ -2,6 +2,7 @@
 # tensor type (float or double)
 tensor_type = float
 
+task_type = seg
 root_dir  = /home/disk2t/data/JSRT
 train_csv = config/jsrt_train.csv
 valid_csv = config/jsrt_valid.csv

segmentation/JSRT2/config/train_test.cfg

@@ -2,6 +2,7 @@
 # tensor type (float or double)
 tensor_type = float
 
+task_type = seg
 root_dir  = /home/disk2t/data/JSRT
 train_csv = ../JSRT/config/jsrt_train.csv
 valid_csv = ../JSRT/config/jsrt_valid.csv

segmentation/fetal_hc/README.md

@@ -17,7 +17,7 @@ In this example, we use 2D U-Net to segment the fetal brain from ultrasound imag
 1. Edit `config/train_test.cfg` by setting the value of `root_dir` as your `HC_root`. Then start to train by running:
 
 ```bash
-pymic_net_run_seg train config/train_test.cfg
+pymic_net_run train config/train_test.cfg
 ```
 
 2. During training or after training, run `tensorboard --logdir model/unet` and you will see a link in the output, such as `http://your-computer:6006`. Open the link in the browser and you can observe the average Dice score and loss during the training stage, such as shown in the following images, where red and blue curves are for training set and validation set respectively. 
@@ -30,7 +30,7 @@ pymic_net_run_seg train config/train_test.cfg
 
 ```bash
 mkdir result
-pymic_net_run_seg test config/train_test.cfg
+pymic_net_run test config/train_test.cfg
 ```
 
 2. Then edit `config/evaluation.cfg` by setting `ground_truth_folder_root` as your `HC_root`, and run the following command to obtain quantitative evaluation results in terms of Dice. 

segmentation/fetal_hc/config/train_test.cfg

@@ -2,6 +2,7 @@
 # tensor type (float or double)
 tensor_type = float
 
+task_type = seg
 root_dir  = /home/disk2t/data/Fetal_HC
 train_csv = config/fetal_hc_train.csv
 valid_csv = config/fetal_hc_valid.csv

segmentation/prostate/README.md

@@ -14,7 +14,7 @@ In this example, we use 3D U-Net to segment the prostate from 3D MRI. First we d
 1. Edit `config/train_test.cfg` by setting the value of `root_dir` as your `data/promise12/preprocess`. Then start to train by running:
 
 ```bash
-pymic_net_run_seg train config/train_test.cfg
+pymic_net_run train config/train_test.cfg
 ```
 
 2. During training or after training, run `tensorboard --logdir model` and you will see a link in the output, such as `http://your-computer:6006`. Open the link in the browser and you can observe the average Dice score and loss during the training stage, such as shown in the following images, where blue and red curves are for training set and validation set respectively. 
@@ -27,7 +27,7 @@ pymic_net_run_seg train config/train_test.cfg
 
 ```bash
 mkdir result
-pymic_net_run_seg test config/train_test.cfg
+pymic_net_run test config/train_test.cfg
 ```
 
 2. Then edit `config/evaluation.cfg` by setting `ground_truth_folder_root` as your `data/promise12/preprocess`, and run the following command to obtain quantitative evaluation results in terms of Dice. 

segmentation/prostate/config/train_test.cfg

@@ -2,6 +2,7 @@
 # tensor type (float or double)
 tensor_type = float
 
+task_type = seg
 root_dir  = /home/disk2t/data/prostate/promise12/preprocess/train
 train_csv = config/data/image_train.csv
 valid_csv = config/data/image_valid.csv