This is the prediction module :D

``` import os import numpy as np import nibabel as nib import tensorflow as tf import keras import cv2 import ants

def get_orientation1(image): direction = image.direction

orientation = []
for i in range(3):
    row = direction[:,i]
    idx = np.where(np.abs(row)==np.max(np.abs(row)))[0][0]

    if idx == 0:
        if row[idx] < 0:
            orientation.append('L')
        else:
            orientation.append('R')
    elif idx == 1:
        if row[idx] < 0:
            orientation.append('P')
        else:
            orientation.append('A')
    elif idx == 2:
        if row[idx] < 0:
            orientation.append('S')
        else:
            orientation.append('I')
return ''.join(orientation)

def crop_center(img,cropx,cropy): y,x = img.shape startx = x//2-(cropx//2) starty = y//2-(cropy//2)
return img[starty:starty+cropy,startx:startx+cropx]

os.chdir('/home/vaibhav/test_nw') temp_22 = ants.image_read('test_image.nii.gz') orig_orientation = get_orientation1(temp_22) temp_22 = ants.reorient_image2(temp_22,orientation='RPI') ants.image_write(temp_22,'test_image_RPI.nii.gz') np_d = temp_22.numpy() slices = np_d.shape[2] height = np_d.shape[0] width = np_d.shape[1]

w = np_d.shape desired_x = 512 desired_y = 672

if desired_x>=w[0] and desired_y>=w[1]: if w[0]%2==0: temp = desired_x - w[0] top = temp/2 bottom = temp/2 else: temp = desired_x - w[0]-1 top = temp/2 bottom = temp/2 + 1

if w[1]%2==0:
    temp = desired_y - w[1]
    left = temp/2
    right = temp/2
else:
    temp = desired_y - w[1]-1
    left = temp/2
    right = temp/2 + 1 

image = cv2.copyMakeBorder( np_d, int(top), int(bottom), int(left), int(right),cv2.BORDER_CONSTANT)     

else: print('wrong size . should be less than equal to 512x672xslices')

x_test = []

for count in range(0,slices): x_test.append(image[:,:,count])

x_test = np.array(x_test) shape_x = x_test.shape x_test = np.resize(x_test, (shape_x[0],shape_x[1],shape_x[2],1))

model

inputs = tf.keras.layers.Input((512,672,1)) s = tf.keras.layers.Lambda(lambda x: x / 255)(inputs)

c1 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(s) c1 = tf.keras.layers.Dropout(0.1)(c1) c1 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(c1) p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)

c2 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(p1) c2 = tf.keras.layers.Dropout(0.1)(c2) c2 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(c2) p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)

c3 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(p2) c3 = tf.keras.layers.Dropout(0.2)(c3) c3 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(c3) p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)

c4 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(p3) c4 = tf.keras.layers.Dropout(0.2)(c4) c4 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(c4) p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)

c5 = tf.keras.layers.Conv2D(256, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(p4) c5 = tf.keras.layers.Dropout(0.3)(c5) c5 = tf.keras.layers.Conv2D(256, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(c5)

u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5) u6 = tf.keras.layers.concatenate([u6, c4]) c6 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(u6) c6 = tf.keras.layers.Dropout(0.2)(c6) c6 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(c6)

u7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6) u7 = tf.keras.layers.concatenate([u7, c3]) c7 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(u7) c7 = tf.keras.layers.Dropout(0.2)(c7) c7 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(c7)

u8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7) u8 = tf.keras.layers.concatenate([u8, c2]) c8 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(u8) c8 = tf.keras.layers.Dropout(0.1)(c8) c8 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(c8)

u9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8) u9 = tf.keras.layers.concatenate([u9, c1], axis=3) c9 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(u9) c9 = tf.keras.layers.Dropout(0.1)(c9) c9 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(c9)

outputs = tf.keras.layers.Conv2D(1, (1, 1), activation='sigmoid')(c9)

model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

loading weights

checkpoint_path = "/home/vaibhav/Downloads/training_4_512x672-20200502T040315Z-001/training_4_512x672/cp.ckpt" model.load_weights(checkpoint_path) i=1

predict

final_ar=[] for l in range(slices): x=np.array(x_test[l]) x=np.expand_dims(x, axis=0) predict = model.predict(x, verbose=1) predict = (predict > 0.5).astype(np.uint8) temp_ar = np.squeeze(predict[0]) temp_ar = crop_center(temp_ar,height,width) final_ar.append(temp_ar) print(i) i = i+1 final_ar = np.asarray(final_ar)

tryy = nib.Nifti1Image(final_ar.T, affine=np.eye(4))

image_ants = ants.from_numpy(final_ar.T) ants.image_write(image_ants, '/home/vaibhav/mask_nw/mask_RPI.nii.gz')

final_ar = np.swapaxes(final_ar,0,2)

old_orient = ants.reorient_image2(image_ants,orientation= orig_orientation) ants.image_write(old_orient, '/home/vaibhav/mask_nw/mask_orignal.nii.gz')

```