import numpy as np
import keras
import argparse
import os
import tf_models
import tensorflow as tf
from keras.models import Sequential, Model
from keras.layers import Dense, Conv3D, Dropout, Flatten, Input, concatenate, Reshape, Lambda, Permute
from keras.layers.core import Dense, Dropout, Activation, Reshape
from keras.layers.convolutional import Conv3D, Conv3DTranspose, UpSampling3D
from keras.layers.pooling import AveragePooling3D
from keras.layers import Input
from keras.layers.merge import concatenate
from keras.layers.normalization import BatchNormalization
from tensorflow.contrib.keras.python.keras.backend import learning_phase
from nibabel import load as load_nii
from sklearn.preprocessing import scale
import matplotlib.pyplot as plt
# SAVE_PATH = 'unet3d_baseline.hdf5'
# OFFSET_W = 16
# OFFSET_H = 16
# OFFSET_C = 4
# HSIZE = 64
# WSIZE = 64
# CSIZE = 16
# batches_h, batches_w, batches_c = (224-HSIZE)/OFFSET_H+1, (224-WSIZE)/OFFSET_W+1, (152 - CSIZE)/OFFSET_C+1
def parse_inputs():
parser = argparse.ArgumentParser(description='Test different nets with 3D data.')
parser.add_argument('-r', '--root-path', dest='root_path', default='/mnt/disk1/dat/lchen63/brain/data/data2')
parser.add_argument('-m', '--model-path', dest='model_path',
default='NoneDense-0')
parser.add_argument('-ow', '--offset-width', dest='offset_w', type=int, default=12)
parser.add_argument('-oh', '--offset-height', dest='offset_h', type=int, default=12)
parser.add_argument('-oc', '--offset-channel', dest='offset_c', nargs='+', type=int, default=12)
parser.add_argument('-ws', '--width-size', dest='wsize', type=int, default=38)
parser.add_argument('-hs', '--height-size', dest='hsize', type=int, default=38)
parser.add_argument('-cs', '--channel-size', dest='csize', type=int, default=38)
parser.add_argument('-ps', '--pred-size', dest='psize', type=int, default=12)
parser.add_argument('-gpu', '--gpu', dest='gpu', type=str, default='0')
parser.add_argument('-mn', '--model_name', dest='model_name', type=str, default='dense24')
parser.add_argument('-nc', '--correction', dest='correction', type=bool, default=True)
return vars(parser.parse_args())
options = parse_inputs()
os.environ["CUDA_VISIBLE_DEVICES"] = options['gpu']
def segmentation_loss(y_true, y_pred, n_classes):
y_true = tf.reshape(y_true, (-1, n_classes))
y_pred = tf.reshape(y_pred, (-1, n_classes))
return tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true,
logits=y_pred))
def vox_preprocess(vox):
vox_shape = vox.shape
vox = np.reshape(vox, (-1, vox_shape[-1]))
vox = scale(vox, axis=0)
return np.reshape(vox, vox_shape)
def one_hot(y, num_classees):
y_ = np.zeros([len(y), num_classees])
y_[np.arange(len(y)), y] = 1
return y_
def dice_coef_np(y_true, y_pred, num_classes):
"""
:param y_true: sparse labels
:param y_pred: sparse labels
:param num_classes: number of classes
:return:
"""
y_true = y_true.astype(int)
y_pred = y_pred.astype(int)
y_true = y_true.flatten()
y_true = one_hot(y_true, num_classes)
y_pred = y_pred.flatten()
y_pred = one_hot(y_pred, num_classes)
intersection = np.sum(y_true * y_pred, axis=0)
return (2. * intersection) / (np.sum(y_true, axis=0) + np.sum(y_pred, axis=0))
def DenseNetUnit3D(x, growth_rate, ksize, n, bn_decay=0.99):
for i in range(n):
concat = x
x = BatchNormalization(center=True, scale=True, momentum=bn_decay)(x)
x = Activation('relu')(x)
x = Conv3D(filters=growth_rate, kernel_size=ksize, padding='same', kernel_initializer='he_uniform',
use_bias=False)(x)
x = concatenate([concat, x])
return x
def DenseNetTransit(x, rate=1, name=None):
if rate != 1:
out_features = x.get_shape().as_list()[-1] * rate
x = BatchNormalization(center=True, scale=True, name=name + '_bn')(x)
x = Activation('relu', name=name + '_relu')(x)
x = Conv3D(filters=out_features, kernel_size=1, strides=1, padding='same', kernel_initializer='he_normal',
use_bias=False, name=name + '_conv')(x)
x = AveragePooling3D(pool_size=2, strides=2, padding='same')(x)
return x
def dense_net(input):
x = Conv3D(filters=24, kernel_size=3, strides=1, kernel_initializer='he_uniform', padding='same', use_bias=False)(
input)
x = DenseNetUnit3D(x, growth_rate=12, ksize=3, n=4)
x = DenseNetTransit(x)
x = DenseNetUnit3D(x, growth_rate=12, ksize=3, n=4)
x = DenseNetTransit(x)
x = DenseNetUnit3D(x, growth_rate=12, ksize=3, n=4)
x = BatchNormalization()(x)
x = Activation('relu')(x)
return x
def dense_model(patch_size, num_classes):
merged_inputs = Input(shape=patch_size + (4,), name='merged_inputs')
flair = Reshape(patch_size + (1,))(
Lambda(
lambda l: l[:, :, :, :, 0],
output_shape=patch_size + (1,))(merged_inputs),
)
t2 = Reshape(patch_size + (1,))(
Lambda(lambda l: l[:, :, :, :, 1], output_shape=patch_size + (1,))(merged_inputs)
)
t1 = Lambda(lambda l: l[:, :, :, :, 2:], output_shape=patch_size + (2,))(merged_inputs)
flair = dense_net(flair)
t2 = dense_net(t2)
t1 = dense_net(t1)
t2 = concatenate([flair, t2])
t1 = concatenate([t2, t1])
tumor = Conv3D(2, kernel_size=1, strides=1, name='tumor')(flair)
core = Conv3D(3, kernel_size=1, strides=1, name='core')(t2)
enhancing = Conv3D(num_classes, kernel_size=1, strides=1, name='enhancing')(t1)
net = Model(inputs=merged_inputs, outputs=[tumor, core, enhancing])
return net
def norm(image):
image = np.squeeze(image)
image_nonzero = image[np.nonzero(image)]
return (image - image_nonzero.mean()) / image_nonzero.std()
def vox_generator_test(all_files):
path = options['root_path']
while 1:
for file in all_files:
p = file
if options['correction']:
flair = load_nii(os.path.join(path, file, file + '_flair_corrected.nii.gz')).get_data()
t2 = load_nii(os.path.join(path, file, file + '_t2_corrected.nii.gz')).get_data()
t1 = load_nii(os.path.join(path, file, file + '_t1_corrected.nii.gz')).get_data()
t1ce = load_nii(os.path.join(path, file, file + '_t1ce_corrected.nii.gz')).get_data()
else:
flair = load_nii(os.path.join(path, p, p + '_flair.nii.gz')).get_data()
t2 = load_nii(os.path.join(path, p, p + '_t2.nii.gz')).get_data()
t1 = load_nii(os.path.join(path, p, p + '_t1.nii.gz')).get_data()
t1ce = load_nii(os.path.join(path, p, p + '_t1ce.nii.gz')).get_data()
data = np.array([flair, t2, t1, t1ce])
data = np.transpose(data, axes=[1, 2, 3, 0])
data_norm = np.array([norm(flair), norm(t2), norm(t1), norm(t1ce)])
data_norm = np.transpose(data_norm, axes=[1, 2, 3, 0])
labels = load_nii(os.path.join(path, p, p + '_seg.nii.gz')).get_data()
yield data, data_norm, labels
def main():
test_files = []
with open('test.txt') as f:
for line in f:
test_files.append(line[:-1])
num_labels = 5
OFFSET_H = options['offset_h']
OFFSET_W = options['offset_w']
OFFSET_C = options['offset_c']
HSIZE = options['hsize']
WSIZE = options['wsize']
CSIZE = options['csize']
PSIZE = options['psize']
SAVE_PATH = options['model_path']
model_name = options['model_name']
OFFSET_PH = (HSIZE - PSIZE) / 2
OFFSET_PW = (WSIZE - PSIZE) / 2
OFFSET_PC = (CSIZE - PSIZE) / 2
batches_w = int(np.ceil((240 - WSIZE) / float(OFFSET_W))) + 1
batches_h = int(np.ceil((240 - HSIZE) / float(OFFSET_H))) + 1
batches_c = int(np.ceil((155 - CSIZE) / float(OFFSET_C))) + 1
flair_t2_node = tf.placeholder(dtype=tf.float32, shape=(None, HSIZE, WSIZE, CSIZE, 2))
t1_t1ce_node = tf.placeholder(dtype=tf.float32, shape=(None, HSIZE, WSIZE, CSIZE, 2))
if model_name == 'dense48':
flair_t2_15, flair_t2_27 = tf_models.BraTS2ScaleDenseNetConcat_large(input=flair_t2_node, name='flair')
t1_t1ce_15, t1_t1ce_27 = tf_models.BraTS2ScaleDenseNetConcat_large(input=t1_t1ce_node, name='t1')
elif model_name == 'no_dense':
flair_t2_15, flair_t2_27 = tf_models.PlainCounterpart(input=flair_t2_node, name='flair')
t1_t1ce_15, t1_t1ce_27 = tf_models.PlainCounterpart(input=t1_t1ce_node, name='t1')
elif model_name == 'dense24':
flair_t2_15, flair_t2_27 = tf_models.BraTS2ScaleDenseNetConcat(input=flair_t2_node, name='flair')
t1_t1ce_15, t1_t1ce_27 = tf_models.BraTS2ScaleDenseNetConcat(input=t1_t1ce_node, name='t1')
elif model_name == 'dense24_nocorrection':
flair_t2_15, flair_t2_27 = tf_models.BraTS2ScaleDenseNetConcat(input=flair_t2_node, name='flair')
t1_t1ce_15, t1_t1ce_27 = tf_models.BraTS2ScaleDenseNetConcat(input=t1_t1ce_node, name='t1')
else:
print' No such model name '
t1_t1ce_15 = concatenate([t1_t1ce_15, flair_t2_15])
t1_t1ce_27 = concatenate([t1_t1ce_27, flair_t2_27])
t1_t1ce_15 = Conv3D(num_labels, kernel_size=1, strides=1, padding='same', name='t1_t1ce_15_cls')(t1_t1ce_15)
t1_t1ce_27 = Conv3D(num_labels, kernel_size=1, strides=1, padding='same', name='t1_t1ce_27_cls')(t1_t1ce_27)
t1_t1ce_score = t1_t1ce_15[:, 13:25, 13:25, 13:25, :] + \
t1_t1ce_27[:, 13:25, 13:25, 13:25, :]
saver = tf.train.Saver()
data_gen_test = vox_generator_test(test_files)
dice_whole, dice_core, dice_et = [], [], []
with tf.Session() as sess:
saver.restore(sess, SAVE_PATH)
for i in range(len(test_files)):
print 'predicting %s' % test_files[i]
x, x_n, y = data_gen_test.next()
pred = np.zeros([240, 240, 155, 5])
for hi in range(batches_h):
offset_h = min(OFFSET_H * hi, 240 - HSIZE)
offset_ph = offset_h + OFFSET_PH
for wi in range(batches_w):
offset_w = min(OFFSET_W * wi, 240 - WSIZE)
offset_pw = offset_w + OFFSET_PW
for ci in range(batches_c):
offset_c = min(OFFSET_C * ci, 155 - CSIZE)
offset_pc = offset_c + OFFSET_PC
data = x[offset_h:offset_h + HSIZE, offset_w:offset_w + WSIZE, offset_c:offset_c + CSIZE, :]
data_norm = x_n[offset_h:offset_h + HSIZE, offset_w:offset_w + WSIZE, offset_c:offset_c + CSIZE, :]
data_norm = np.expand_dims(data_norm, 0)
if not np.max(data) == 0 and np.min(data) == 0:
score = sess.run(fetches=t1_t1ce_score,
feed_dict={flair_t2_node: data_norm[:, :, :, :, :2],
t1_t1ce_node: data_norm[:, :, :, :, 2:],
learning_phase(): 0}
)
pred[offset_ph:offset_ph + PSIZE, offset_pw:offset_pw + PSIZE, offset_pc:offset_pc + PSIZE,
:] += np.squeeze(score)
pred = np.argmax(pred, axis=-1)
pred = pred.astype(int)
print 'calculating dice...'
whole_pred = (pred > 0).astype(int)
whole_gt = (y > 0).astype(int)
core_pred = (pred == 1).astype(int) + (pred == 4).astype(int)
core_gt = (y == 1).astype(int) + (y == 4).astype(int)
et_pred = (pred == 4).astype(int)
et_gt = (y == 4).astype(int)
dice_whole_batch = dice_coef_np(whole_gt, whole_pred, 2)
dice_core_batch = dice_coef_np(core_gt, core_pred, 2)
dice_et_batch = dice_coef_np(et_gt, et_pred, 2)
dice_whole.append(dice_whole_batch)
dice_core.append(dice_core_batch)
dice_et.append(dice_et_batch)
print dice_whole_batch
print dice_core_batch
print dice_et_batch
dice_whole = np.array(dice_whole)
dice_core = np.array(dice_core)
dice_et = np.array(dice_et)
print 'mean dice whole:'
print np.mean(dice_whole, axis=0)
print 'mean dice core:'
print np.mean(dice_core, axis=0)
print 'mean dice enhance:'
print np.mean(dice_et, axis=0)
np.save(model_name + '_dice_whole', dice_whole)
np.save(model_name + '_dice_core', dice_core)
np.save(model_name + '_dice_enhance', dice_et)
print 'pred saved'
if __name__ == '__main__':
main()
Datasets
Models
