def extract_segs_a2c_a4c(dicomdir, videofile, model, sess, lv_label, la_label,
lvo_label):
framedict = create_imgdict_from_dicom(dicomdir, videofile)
images, orig_images = extract_images(framedict)
segs = []
preds = np.argmax(model.predict(sess, images[0:1])[0, :, :, :], 2)
label_all = range(1, 8)
label_good = [lv_label, la_label, lvo_label]
for i in label_all:
if not i in label_good:
preds[preds == i] = 0
for i in range(len(images)):
seg = np.argmax(model.predict(sess, images[i:i + 1])[0, :, :, :], 2)
segs.append(seg)
lv_segs = []
lvo_segs = []
la_segs = []
for seg in segs:
la_seg = create_seg(seg, la_label)
lvo_seg = create_seg(seg, lvo_label)
lv_seg = create_seg(seg, lv_label)
lv_segs.append(lv_seg)
lvo_segs.append(lvo_seg)
la_segs.append(la_seg)
return lv_segs, la_segs, orig_images, lvo_segs, preds
def segmentChamber(videofile, dicomdir, view):
mean = 24
weight_decay = 1e-12
learning_rate = 1e-4
maxout = False
sesses = []
models = []
if view == "a4c":
g_1 = tf.Graph()
with g_1.as_default():
label_dim = 6 #a4c
sess1 = tf.Session()
model1 = Unet(mean, weight_decay, learning_rate, label_dim , maxout = maxout)
sess1.run(tf.local_variables_initializer())
sess = sess1
model = model1
with g_1.as_default():
saver = tf.train.Saver()
saver.restore(sess1,'./models/a4c_45_20_all_model.ckpt-9000')
elif view == "a2c":
g_2 = tf.Graph()
with g_2.as_default():
label_dim = 4
sess2 = tf.Session()
model2 = Unet(mean, weight_decay, learning_rate, label_dim , maxout = maxout)
sess2.run(tf.local_variables_initializer())
sess = sess2
model = model2
with g_2.as_default():
saver = tf.train.Saver()
saver.restore(sess2,'./models/a2c_45_20_all_model.ckpt-10600')
elif view == "a3c":
g_3 = tf.Graph()
with g_3.as_default():
label_dim = 4
sess3 = tf.Session()
model3 = Unet(mean, weight_decay, learning_rate, label_dim , maxout = maxout)
sess3.run(tf.local_variables_initializer())
sess = sess3
model = model3
with g_3.as_default():
saver.restore(sess3,'./models/a3c_45_20_all_model.ckpt-10500')
elif view == "psax":
g_4 = tf.Graph()
with g_4.as_default():
label_dim = 4
sess4 = tf.Session()
model4 = Unet(mean, weight_decay, learning_rate, label_dim , maxout = maxout)
sess4.run(tf.local_variables_initializer())
sess = sess4
model = model4
with g_4.as_default():
saver = tf.train.Saver()
saver.restore(sess4,'./models/psax_45_20_all_model.ckpt-9300')
elif view == "plax":
g_5 = tf.Graph()
with g_5.as_default():
label_dim = 7
sess5 = tf.Session()
model5 = Unet(mean, weight_decay, learning_rate, label_dim , maxout = maxout)
sess5.run(tf.local_variables_initializer())
sess = sess5
model = model5
with g_5.as_default():
saver = tf.train.Saver()
saver.restore(sess5,'./models/plax_45_20_all_model.ckpt-9600')
outpath = "./segment/" + view + "/"
if not os.path.exists(outpath):
os.makedirs(outpath)
framedict = create_imgdict_from_dicom(dicomdir, videofile)
images, orig_images = extract_images(framedict)
if view == "a4c":
a4c_lv_segs, a4c_la_segs, a4c_lvo_segs, preds = extract_segs(images, orig_images, model, sess, 2, 4, 1)
np.save(outpath + '/' + videofile + '_lv', np.array(a4c_lv_segs).astype('uint8'))
np.save(outpath + '/' + videofile + '_la', np.array(a4c_la_segs).astype('uint8'))
np.save(outpath + '/' + videofile + '_lvo', np.array(a4c_lvo_segs).astype('uint8'))
elif view == "a2c":
a2c_lv_segs, a2c_la_segs, a2c_lvo_segs, preds = extract_segs(images, orig_images, model, sess, 2, 3, 1)
np.save(outpath + '/' + videofile + '_lv', np.array(a2c_lv_segs).astype('uint8'))
np.save(outpath + '/' + videofile + '_la', np.array(a2c_la_segs).astype('uint8'))
np.save(outpath + '/' + videofile + '_lvo', np.array(a2c_lvo_segs).astype('uint8'))
elif view == "psax":
psax_lv_segs, psax_lvo_segs, psax_rv_segs, preds = extract_segs(images, orig_images, model, sess, 2, 1, 3)
np.save(outpath + '/' + videofile + '_lv', np.array(psax_lv_segs).astype('uint8'))
np.save(outpath + '/' + videofile + '_lvo', np.array(psax_lvo_segs).astype('uint8'))
elif view == "a3c":
a3c_lv_segs, a3c_la_segs, a3c_lvo_segs, preds = extract_segs(images, orig_images, model, sess, 2, 3, 1)
np.save(outpath + '/' + videofile + '_lvo', np.array(a3c_lvo_segs).astype('uint8'))
np.save(outpath + '/' + videofile + '_lv', np.array(a3c_lv_segs).astype('uint8'))
np.save(outpath + '/' + videofile + '_la', np.array(a3c_la_segs).astype('uint8'))
elif view == "plax":
plax_chamber_segs, preds = extract_segs(images, orig_images, model, sess, 1)
np.save(outpath + '/' + videofile + '_chamber_B1', np.array(plax_chamber_segs).astype('uint8'))
j = 0
nrow = orig_images[0].shape[0]
ncol = orig_images[0].shape[1]
print(nrow, ncol)
plt.figure(figsize = (25, 25))
plt.axis('off')
plt.imshow(imresize(preds, (nrow,ncol)))
plt.savefig(outpath + '/' + videofile + '_' + str(j) + '_' + 'CHOICE_FULL_segmentation_LV.png')
plt.close()
plt.figure(figsize = (25, 25))
plt.axis('off')
plt.imshow(orig_images[0])
plt.savefig(outpath + '/' + videofile + '_' + str(j) + '_' + 'CHOICE_FULL_original_LV.png')
plt.close()
background = Image.open(outpath + '/' + videofile + '_' + str(j) + '_' + 'CHOICE_FULL_original_LV.png')
overlay = Image.open(outpath + '/' + videofile + '_' + str(j) + '_' + 'CHOICE_FULL_segmentation_LV.png')
background = background.convert("RGBA")
overlay = overlay.convert("RGBA")
outImage = Image.blend(background, overlay, 0.5)
outImage.save(outpath + '/' + videofile + '_' + str(j) + '_' + 'CHOICE_FULL_overlay_LV.png', "PNG")
return 1
def outputcropped(videoFile, view):
flag = 1
print(videoFile, view)
outDir = "./straintmp/" + videoFile
dicomdir = "./dicomsample/"
if not os.path.exists(outDir):
os.makedirs(outDir)
outDir_left = outDir + "/maskedimages_left/"
if not os.path.exists(outDir_left):
os.makedirs(outDir_left)
outDir_right = outDir + "/maskedimages_right/"
if not os.path.exists(outDir_right):
os.makedirs(outDir_right)
npydir = "./segment/" + view
lvo_segs = np.load(npydir + "/" + videoFile + "_lvo.npy")
lv_segs = np.load(npydir + "/" + videoFile + "_lv.npy")
imgdict = create_imgdict_from_dicom(dicomdir, videoFile)
nrow = imgdict[0].shape[0]
ncol = imgdict[0].shape[1]
mask = create_mask(imgdict)
kernel_i = np.ones((20, 20), np.uint8) #narrower than 20,20
kernel_o = np.ones((25, 25), np.uint8) #narrower than 20,20
for i in range(0, len(lv_segs)):
lv_seg = lv_segs[i].copy()
lvo_seg = lvo_segs[i].copy()
lv_seg = imresize(lv_seg.copy(), (nrow, ncol), interp='nearest')
lv_seg_dilate = cv2.dilate(lv_seg, kernel_i, iterations=2)
lvo_seg = imresize(lvo_seg.copy(), (nrow, ncol), interp='nearest')
lvo_seg_dilate = cv2.dilate(lvo_seg, kernel_o, iterations=1)
y_i, x_i = np.where(lv_seg_dilate > 0)
y_o, x_o = np.where(lvo_seg_dilate > 0)
outer = np.transpose((y_o, x_o))
inner = np.transpose((y_i, x_i))
iset = set([tuple(x) for x in inner])
oset = set([tuple(x) for x in outer])
overlap = np.array([x for x in iset & oset])
if len(overlap.shape) == 2:
y = overlap[:, 0]
x = overlap[:, 1]
y_left_hi = y[(x < np.percentile(x, 43))
& (y < np.percentile(y, 50))]
y_left_lo = y[(x < np.percentile(x, 35))
& (y > np.percentile(y, 50)) &
(y < np.percentile(y, 90))]
x_left_hi = x[(x < np.percentile(x, 43))
& (y < np.percentile(y, 50))]
x_left_lo = x[(x < np.percentile(x, 35))
& (y > np.percentile(y, 50)) &
(y < np.percentile(y, 90))]
y_rite_hi = y[(x > np.percentile(x, 43))
& (y < np.percentile(y, 50))]
y_rite_lo = y[(x > np.percentile(x, 35))
& (y > np.percentile(y, 50)) &
(y < np.percentile(y, 90))]
x_rite_hi = x[(x > np.percentile(x, 43))
& (y < np.percentile(y, 50))]
x_rite_lo = x[(x > np.percentile(x, 35))
& (y > np.percentile(y, 50)) &
(y < np.percentile(y, 90))]
y_left = np.hstack((y_left_lo, y_left_hi))
x_left = np.hstack((x_left_lo, x_left_hi))
y_rite = np.hstack((y_rite_lo, y_rite_hi))
x_rite = np.hstack((x_rite_lo, x_rite_hi))
points_left = np.fliplr(np.transpose((y_left, x_left)))
points_right = np.fliplr(np.transpose((y_rite, x_rite)))
newimage_left = isolate_obj(imgdict[i], points_left)
outfile_left = outDir_left + "/" + videoFile + "_" + str(
i) + ".png"
cv2.imwrite(outfile_left, newimage_left)
newimage_right = isolate_obj(imgdict[i], points_right)
outfile_right = outDir_right + "/" + videoFile + "_" + str(
i) + ".png"
cv2.imwrite(outfile_right, newimage_right)
else:
flag = 0
if flag == 1:
return 1
else:
return 0
return 1