--- a
+++ b/Region/predict_region_sag.py
@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Oct 31 09:05:57 2018
+
+@author: Josefine
+"""
+
+import tensorflow as tf
+import numpy as np
+import nibabel as nib
+import glob
+import re
+from skimage.transform import resize
+
+imgDim = 128
+
+##############################################################################
+###                              Data functions                         ######
+##############################################################################
+def natural_sort(l): 
+    convert = lambda text: int(text) if text.isdigit() else text.lower() 
+    alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] 
+    return sorted(l, key = alphanum_key)
+
+def create_image(filename_img,direction):
+    images = []
+    a = nib.load(filename_img)
+    a = a.get_data()
+    # Normalize:
+    a2 = np.clip(a,-1000,1000)
+    a3 = np.interp(a2, (a2.min(), a2.max()), (-1, +1))
+    # Reshape:
+    img = np.zeros([512,512,512])+np.min(a3)
+    index1 = int(np.ceil((512-a.shape[2])/2))
+    index2 = int(512-np.floor((512-a.shape[2])/2))
+    img[:,:,index1:index2] = a3
+    im = resize(img,(imgDim,imgDim,imgDim),order=0)
+    if direction == 'sag':
+        for i in range(im.shape[0]):
+            images.append((im[i,:,:]))
+    if direction == 'cor':
+        for i in range(im.shape[1]):
+            images.append((im[:,i,:]))
+    if direction == 'axial':
+        for i in range(im.shape[2]):
+            images.append((im[:,:,i]))            
+    images = np.asarray(images)
+    images = images.reshape(-1, imgDim,imgDim,1)
+    return images
+
+# Load test data
+filelist_test = natural_sort(glob.glob('WHS/ct_train_test/ct_test/*_image.nii.gz')) # list of file names
+
+# Load train data for segmentation network
+filelist_train = natural_sort(glob.glob('WHS/Augment_data/*_image.nii')) # list of file names
+
+##############################################################################
+###                  Reload network and predict                         ######
+##############################################################################
+print("====================== LOAD SAGITTAL NETWORK: ===========================")
+
+# Doing predictions with the model 
+tf.reset_default_graph()      
+
+new_saver = tf.train.import_meta_graph('WHS/Results/region/model_sag/model.ckpt.meta')
+
+with tf.Session() as sess:
+    new_saver.restore(sess, tf.train.latest_checkpoint('WHS/Results/region/model_sag/'))
+    graph = tf.get_default_graph()       
+    x = graph.get_tensor_by_name("x_train:0")
+    op_to_restore = graph.get_tensor_by_name("output/Softmax:0") #ME
+#
+#    for i in range(len(filelist_test)):
+#        print('Processing test image', (i+1),'out of',(np.max(range(len(filelist_test)))+1))
+#        # Find renderings corresponding to the given name
+#        prob_maps = []
+#        x_test = create_image(filelist_test[i],'sag')
+#        for k in range(x_test.shape[0]):
+#            x_test_image = np.expand_dims(x_test[k,:,:,:], axis=0)
+#            y_output = sess.run(tf.nn.softmax(op_to_restore), feed_dict={x: x_test_image,'Placeholder:0':1.0})
+#            prob_maps.append(y_output[0,:,:,:])
+#        np.savez('WHS/Results/Predictions/region/test_prob_maps_sag_{}'.format(i),prob_maps=prob_maps)                            
+#    print("================ DONE WITH TEST PREDICTIONS! ==================")  
+
+    for i in range(30,len(filelist_train)):
+        print('Processing test image', (i+1),'out of',(np.max(range(len(filelist_train)))+1))
+        # Find renderings corresponding to the given name
+        prob_maps = []
+        x_test = create_image(filelist_train[i],'sag')
+        for k in range(x_test.shape[0]):
+            x_test_image = np.expand_dims(x_test[k,:,:,:], axis=0)
+            y_output = sess.run(tf.nn.softmax(op_to_restore), feed_dict={x: x_test_image,'Placeholder:0':1.0})
+            prob_maps.append(y_output[0,:,:,:])
+        np.savez('WHS/Results/Predictions/region/train_prob_maps_sag_{}'.format(i),prob_maps=prob_maps)                            
+    print("================ DONE WITH TRAIN PREDICTIONS! ==================")  
+
+print("================ DONE WITH AXIAL PREDICTIONS! ==================")
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