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main.py
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main.py
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## Deep Active Lesion Segmention (DALS), Code by Ali Hatamizadeh ( http://web.cs.ucla.edu/~ahatamiz/ )
import os
import numpy as np
import tensorflow as tf
import DataGen
import glob
import architectures
from sklearn.metrics import f1_score
from utils import load_image,dice_hard,dice_soft,my_func,resolve_status
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--logdir', default='network', type=str)
parser.add_argument('--mu', default=0.2, type=float)
parser.add_argument('--nu', default=5.0, type=float)
parser.add_argument('--batch_size', default=1, type=int)
parser.add_argument('--train_sum_freq', default=150, type=int)
parser.add_argument('--train_iter', default=150000, type=int)
parser.add_argument('--acm_iter_limit', default=300, type=int)
parser.add_argument('--img_resize', default=512, type=int)
parser.add_argument('--f_size', default=15, type=int)
parser.add_argument('--train_status', default=1, type=int)
parser.add_argument('--narrow_band_width', default=1, type=int)
parser.add_argument('--save_freq', default=1000, type=int)
parser.add_argument('--demo_type', default=1, type=int)
parser.add_argument('--lr', default=1e-3, type=float)
parser.add_argument('--gpu', default='0', type=str)
args = parser.parse_args()
restore,is_training =resolve_status(args.train_status)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]=args.gpu
input_location = './dataset/Train'
valid_location = './dataset/Valid'
test_location = './dataset/Test'
def re_init_phi(phi, dt):
D_left_shift = tf.cast(tf.manip.roll(phi, -1, axis=1), dtype='float32')
D_right_shift = tf.cast(tf.manip.roll(phi, 1, axis=1), dtype='float32')
D_up_shift = tf.cast(tf.manip.roll(phi, -1, axis=0), dtype='float32')
D_down_shift = tf.cast(tf.manip.roll(phi, 1, axis=0), dtype='float32')
bp = D_left_shift - phi
cp = phi - D_down_shift
dp = D_up_shift - phi
ap = phi - D_right_shift
an = tf.identity(ap)
bn = tf.identity(bp)
cn = tf.identity(cp)
dn = tf.identity(dp)
ap = tf.clip_by_value(ap, 0, 10 ^ 38)
bp = tf.clip_by_value(bp, 0, 10 ^ 38)
cp = tf.clip_by_value(cp, 0, 10 ^ 38)
dp = tf.clip_by_value(dp, 0, 10 ^ 38)
an = tf.clip_by_value(an, -10 ^ 38, 0)
bn = tf.clip_by_value(bn, -10 ^ 38, 0)
cn = tf.clip_by_value(cn, -10 ^ 38, 0)
dn = tf.clip_by_value(dn, -10 ^ 38, 0)
area_pos = tf.where(phi > 0)
area_neg = tf.where(phi < 0)
pos_y = area_pos[:, 0]
pos_x = area_pos[:, 1]
neg_y = area_neg[:, 0]
neg_x = area_neg[:, 1]
tmp1 = tf.reduce_max([tf.square(tf.gather_nd(t, area_pos)) for t in [ap, bn]], axis=0)
tmp1 += tf.reduce_max([tf.square(tf.gather_nd(t, area_pos)) for t in [cp, dn]], axis=0)
update1 = tf.sqrt(tf.abs(tmp1)) - 1
indices1 = tf.stack([pos_y, pos_x], 1)
tmp2 = tf.reduce_max([tf.square(tf.gather_nd(t, area_neg)) for t in [an, bp]], axis=0)
tmp2 += tf.reduce_max([tf.square(tf.gather_nd(t, area_neg)) for t in [cn, dp]], axis=0)
update2 = tf.sqrt(tf.abs(tmp2)) - 1
indices2 = tf.stack([neg_y, neg_x], 1)
indices_final = tf.concat([indices1, indices2], 0)
update_final = tf.concat([update1, update2], 0)
dD = tf.scatter_nd(indices_final, update_final, shape=[input_image_size, input_image_size])
S = tf.divide(phi, tf.square(phi) + 1)
phi = phi - tf.multiply(dt * S, dD)
return phi
def get_curvature(phi, x, y):
phi_shape = tf.shape(phi)
dim_y = phi_shape[0]
dim_x = phi_shape[1]
x = tf.cast(x, dtype="int32")
y = tf.cast(y, dtype="int32")
y_plus = tf.cast(y + 1, dtype="int32")
y_minus = tf.cast(y - 1, dtype="int32")
x_plus = tf.cast(x + 1, dtype="int32")
x_minus = tf.cast(x - 1, dtype="int32")
y_plus = tf.minimum(tf.cast(y_plus, dtype="int32"), tf.cast(dim_y - 1, dtype="int32"))
x_plus = tf.minimum(tf.cast(x_plus, dtype="int32"), tf.cast(dim_x - 1, dtype="int32"))
y_minus = tf.maximum(y_minus, 0)
x_minus = tf.maximum(x_minus, 0)
d_phi_dx = tf.gather_nd(phi, tf.stack([y, x_plus], 1)) - tf.gather_nd(phi, tf.stack([y, x_minus], 1))
d_phi_dx_2 = tf.square(d_phi_dx)
d_phi_dy = tf.gather_nd(phi, tf.stack([y_plus, x], 1)) - tf.gather_nd(phi, tf.stack([y_minus, x], 1))
d_phi_dy_2 = tf.square(d_phi_dy)
d_phi_dxx = tf.gather_nd(phi, tf.stack([y, x_plus], 1)) + tf.gather_nd(phi, tf.stack([y, x_minus], 1)) - \
2 * tf.gather_nd(phi, tf.stack([y, x], 1))
d_phi_dyy = tf.gather_nd(phi, tf.stack([y_plus, x], 1)) + tf.gather_nd(phi, tf.stack([y_minus, x], 1)) - \
2 * tf.gather_nd(phi, tf.stack([y, x], 1))
d_phi_dxy = 0.25 * (- tf.gather_nd(phi, tf.stack([y_minus, x_minus], 1)) - tf.gather_nd(phi, tf.stack(
[y_plus, x_plus], 1)) + tf.gather_nd(phi, tf.stack([y_minus, x_plus], 1)) + tf.gather_nd(phi, tf.stack(
[y_plus, x_minus], 1)))
tmp_1 = tf.multiply(d_phi_dx_2, d_phi_dyy) + tf.multiply(d_phi_dy_2, d_phi_dxx) - \
2 * tf.multiply(tf.multiply(d_phi_dx, d_phi_dy), d_phi_dxy)
tmp_2 = tf.add(tf.pow(d_phi_dx_2 + d_phi_dy_2, 1.5), 2.220446049250313e-16)
tmp_3 = tf.pow(d_phi_dx_2 + d_phi_dy_2, 0.5)
tmp_4 = tf.divide(tmp_1, tmp_2)
curvature = tf.multiply(tmp_3, tmp_4)
mean_grad = tf.pow(d_phi_dx_2 + d_phi_dy_2, 0.5)
return curvature, mean_grad
def get_intensity(image, masked_phi, filter_patch_size=5):
u_1 = tf.layers.average_pooling2d(tf.multiply(image, masked_phi), [filter_patch_size, filter_patch_size], 1,padding='SAME')
u_2 = tf.layers.average_pooling2d(masked_phi, [filter_patch_size, filter_patch_size], 1, padding='SAME')
u_2_prime = 1 - tf.cast((u_2 > 0), dtype='float32') + tf.cast((u_2 < 0), dtype='float32')
u_2 = u_2 + u_2_prime + 2.220446049250313e-16
return tf.divide(u_1, u_2)
def active_contour_layer(elems):
img = elems[0]
init_phi = elems[1]
map_lambda1_acl = elems[2]
map_lambda2_acl = elems[3]
wind_coef = 3
zero_tensor = tf.constant(0, shape=[], dtype="int32")
def _body(i, phi_level):
band_index = tf.reduce_all([phi_level <= narrow_band_width, phi_level >= -narrow_band_width], axis=0)
band = tf.where(band_index)
band_y = band[:, 0]
band_x = band[:, 1]
shape_y = tf.shape(band_y)
num_band_pixel = shape_y[0]
window_radii_x = tf.ones(num_band_pixel) * wind_coef
window_radii_y = tf.ones(num_band_pixel) * wind_coef
def body_intensity(j, mean_intensities_outer, mean_intensities_inner):
xnew = tf.cast(band_x[j], dtype="float32")
ynew = tf.cast(band_y[j], dtype="float32")
window_radius_x = tf.cast(window_radii_x[j], dtype="float32")
window_radius_y = tf.cast(window_radii_y[j], dtype="float32")
local_window_x_min = tf.cast(tf.floor(xnew - window_radius_x), dtype="int32")
local_window_x_max = tf.cast(tf.floor(xnew + window_radius_x), dtype="int32")
local_window_y_min = tf.cast(tf.floor(ynew - window_radius_y), dtype="int32")
local_window_y_max = tf.cast(tf.floor(ynew + window_radius_y), dtype="int32")
local_window_x_min = tf.maximum(zero_tensor, local_window_x_min)
local_window_y_min = tf.maximum(zero_tensor, local_window_y_min)
local_window_x_max = tf.minimum(tf.cast(input_image_size - 1, dtype="int32"), local_window_x_max)
local_window_y_max = tf.minimum(tf.cast(input_image_size - 1, dtype="int32"), local_window_y_max)
local_image = img[local_window_y_min: local_window_y_max + 1,local_window_x_min: local_window_x_max + 1]
local_phi = phi_level[local_window_y_min: local_window_y_max + 1,local_window_x_min: local_window_x_max + 1]
inner = tf.where(local_phi <= 0)
area_inner = tf.cast(tf.shape(inner)[0], dtype='float32')
outer = tf.where(local_phi > 0)
area_outer = tf.cast(tf.shape(outer)[0], dtype='float32')
image_loc_inner = tf.gather_nd(local_image, inner)
image_loc_outer = tf.gather_nd(local_image, outer)
mean_intensity_inner = tf.cast(tf.divide(tf.reduce_sum(image_loc_inner), area_inner), dtype='float32')
mean_intensity_outer = tf.cast(tf.divide(tf.reduce_sum(image_loc_outer), area_outer), dtype='float32')
mean_intensities_inner = tf.concat(axis=0, values=[mean_intensities_inner[:j], [mean_intensity_inner]])
mean_intensities_outer = tf.concat(axis=0, values=[mean_intensities_outer[:j], [mean_intensity_outer]])
return (j + 1, mean_intensities_outer, mean_intensities_inner)
if fast_lookup:
phi_4d = phi_level[tf.newaxis, :, :, tf.newaxis]
image = img[tf.newaxis, :, :, tf.newaxis]
band_index_2 = tf.reduce_all([phi_4d <= narrow_band_width, phi_4d >= -narrow_band_width], axis=0)
band_2 = tf.where(band_index_2)
u_inner = get_intensity(image, tf.cast((([phi_4d <= 0])), dtype='float32')[0], filter_patch_size=f_size)
u_outer = get_intensity(image, tf.cast((([phi_4d > 0])), dtype='float32')[0], filter_patch_size=f_size)
mean_intensities_inner = tf.gather_nd(u_inner, band_2)
mean_intensities_outer = tf.gather_nd(u_outer, band_2)
else:
mean_intensities_inner = tf.constant([0], dtype='float32')
mean_intensities_outer = tf.constant([0], dtype='float32')
j = tf.constant(0, dtype=tf.int32)
_, mean_intensities_outer, mean_intensities_inner = tf.while_loop(
lambda j, mean_intensities_outer, mean_intensities_inner:
j < num_band_pixel, body_intensity, loop_vars=[j, mean_intensities_outer, mean_intensities_inner],
shape_invariants=[j.get_shape(), tf.TensorShape([None]), tf.TensorShape([None])])
lambda1 = tf.gather_nd(map_lambda1_acl, [band])
lambda2 = tf.gather_nd(map_lambda2_acl, [band])
curvature, mean_grad = get_curvature(phi_level, band_x, band_y)
kappa = tf.multiply(curvature, mean_grad)
term1 = tf.multiply(tf.cast(lambda1, dtype='float32'),tf.square(tf.gather_nd(img, [band]) - mean_intensities_inner))
term2 = tf.multiply(tf.cast(lambda2, dtype='float32'),tf.square(tf.gather_nd(img, [band]) - mean_intensities_outer))
force = -nu + term1 - term2
force /= (tf.reduce_max(tf.abs(force)))
d_phi_dt = tf.cast(force, dtype="float32") + tf.cast(mu * kappa, dtype="float32")
dt = .45 / (tf.reduce_max(tf.abs(d_phi_dt)) + 2.220446049250313e-16)
d_phi = dt * d_phi_dt
update_narrow_band = d_phi
phi_level = phi_level + tf.scatter_nd([band], tf.cast(update_narrow_band, dtype='float32'),shape=[input_image_size, input_image_size])
phi_level = re_init_phi(phi_level, 0.5)
return (i + 1, phi_level)
i = tf.constant(0, dtype=tf.int32)
phi = init_phi
_, phi = tf.while_loop(lambda i, phi: i < iter_limit, _body, loop_vars=[i, phi])
phi = tf.round(tf.cast((1 - tf.nn.sigmoid(phi)), dtype=tf.float32))
return phi,init_phi, map_lambda1_acl, map_lambda2_acl
fast_lookup = True
config = tf.ConfigProto(allow_soft_placement=True)
input_shape = [args.batch_size, args.img_resize, args.img_resize, 1]
input_shape_dt = [args.batch_size, args.img_resize, args.img_resize]
iter_limit = args.acm_iter_limit
narrow_band_width = args.narrow_band_width
mu = args.mu
nu = args.nu
f_size = args.f_size
input_image_size = args.img_resize
x = tf.placeholder(shape=input_shape, dtype=tf.float32, name="x")
y = tf.placeholder(dtype=tf.float32, name="y")
phase = tf.placeholder(tf.bool, name='phase')
global_step = tf.Variable(0, name='global_step', trainable=False)
out_seg, map_lambda1, map_lambda2 = architectures.ddunet(x,is_training)
y_out_dl = tf.round(out_seg)
x_acm = x[:, :, :, 0]
rounded_seg_acl = y_out_dl[:, :, :, 0]
dt_trans = tf.py_func(my_func, [rounded_seg_acl], tf.float32)
dt_trans.set_shape([args.batch_size, input_image_size, input_image_size])
phi_out,_, lambda1_tr, lambda2_tr = tf.map_fn(fn=active_contour_layer, elems=(x_acm, dt_trans, map_lambda1[:, :, :, 0], map_lambda2[:, :, :, 0]))
Dice = dice_soft(out_seg, y)
seg_loss = 1 - Dice
l2_loss = tf.losses.get_regularization_loss()
seg_loss += l2_loss
total_loss = seg_loss
rounded_seg = tf.round(out_seg)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
Dice_hard = dice_hard(out_seg, y)
adaptive_lr = tf.train.exponential_decay(args.lr, global_step, 100000, 0.96, staircase=False, name=None)
with tf.control_dependencies(update_ops):
train_op = tf.train.AdamOptimizer(adaptive_lr).minimize(total_loss, global_step=global_step,
colocate_gradients_with_ops=True, name='train_op')
train_summary = []
train_summary.append(tf.summary.scalar('train/total_loss', total_loss))
train_summary.append(tf.summary.scalar('train/Dice_pipeline', Dice_hard))
train_summary.append(tf.summary.image('Output Segmentation', y_out_dl))
train_summary.append(tf.summary.image('Ground Truth', y))
train_summary_op = tf.summary.merge(train_summary)
saver = tf.train.Saver(tf.global_variables())
with tf.Session(config=config) as sess:
if is_training:
if restore:
saver.restore(sess, tf.train.latest_checkpoint(args.logdir))
graph = tf.get_default_graph()
globalstep = graph.get_tensor_by_name("global_step:0")
gb_step = sess.run([globalstep])[0]
print("Model restored at Global step %d :" % (gb_step))
summary_writer_train = tf.summary.FileWriter(args.logdir + '/train', graph=sess.graph)
summary_writer_valid = tf.summary.FileWriter(args.logdir + '/valid', graph=sess.graph)
else:
sess.run(tf.global_variables_initializer())
summary_writer_train = tf.summary.FileWriter(args.logdir + '/train', graph=sess.graph)
summary_writer_valid = tf.summary.FileWriter(args.logdir + '/valid', graph=sess.graph)
gb_step = 0
data_suffix = '_input.npy'
mask_suffix = '_label.npy'
data_provider_train = DataGen.ImageGen(input_location, data_suffix=data_suffix, mask_suffix=mask_suffix,shuffle_data=True, n_class=1)
data_provider_valid = DataGen.ImageGen(valid_location, data_suffix=data_suffix, mask_suffix=mask_suffix,shuffle_data=True, n_class=1)
try:
for iters in range(args.train_iter):
print("Global step %d :" % (gb_step))
gb_step = gb_step + 1
images, labels, _ = data_provider_train(args.batch_size)
if gb_step % args.train_sum_freq == 0:
summary_str_train = sess.run(train_summary_op, {x: images, y: labels, phase: True})
summary_writer_train.add_summary(summary_str_train, gb_step)
images, labels, _ = data_provider_valid(args.batch_size)
summary_str_valid = sess.run(train_summary_op, {x: images, y: labels, phase: False})
summary_writer_valid.add_summary(summary_str_valid, gb_step)
else:
_, Dicevalue = sess.run([train_op, Dice], {x: images, y: labels, phase: True})
print('Dice value train is {}'.format(Dicevalue))
if gb_step % args.save_freq == 0 and gb_step != 0:
ckpt_path = os.path.join(args.logdir, 'model.ckpt')
saver.save(sess, ckpt_path + '/_globalstep_%02d' % (gb_step), global_step=gb_step)
print('model saved for global step %02d' % (gb_step))
except KeyboardInterrupt:
print("Manual interruption occurred.")
saver.save(sess, args.logdir + '/model_globalstep_%02d' % (gb_step), global_step=gb_step)
print('model saved for global step %02d' % (gb_step))
else:
print("########### Inference ############")
saver = tf.train.Saver(tf.global_variables())
saver.restore(sess, tf.train.latest_checkpoint(args.logdir))
directory = test_location
img_ext = '*_input.npy'
label_ext = 'label.npy'
test_dice = []
count = 0
for fullpath in glob.glob(os.path.join(directory, img_ext)):
print('Processing Case {} '.format(count+1))
count+=1
filename = os.path.basename(fullpath)
print(filename)
label_name = filename.split('input')[0] + label_ext
label_path = fullpath.replace(filename, label_name)
image = load_image(fullpath,args.batch_size,False)
labels = load_image(label_path, args.batch_size,True)
labels[labels != 0] = 1
seg_out_acm= sess.run(phi_out,{x: image, y: labels, phase: False})
seg_out_acm = seg_out_acm[0, :, :]
gt_mask = labels[0, :, :, 0]
f2 = f1_score(gt_mask, seg_out_acm, labels=None, average='micro', sample_weight=None)
print('Dice is {0:0.4f}'.format(f2))
test_dice.append(f2)
print("Avg Test Dice Score : {0:0.4f}".format(np.average(test_dice)))