def plot_3d_no_gt(im_st, pred_st, res, thr = 0.5, plot_size_xy = 256):
zoom_fac = [res[0]/res[1]/(im_st.shape[1]/plot_size_xy), plot_size_xy/im_st.shape[1], plot_size_xy/im_st.shape[1]]
im_st = zoom(im_st.copy(), zoom = zoom_fac, order = 1)
mask = np.zeros((im_st.shape[0],im_st.shape[1], im_st.shape[2]))
#for i in range(im_st.shape[0]):
# mask[i, :, :] = lungmask_pro(im_st[i, :, :], morph = False)
mask[:,:,:] = lungmask3D(im_st[:, :, :], morph = False)
for i in range(im_st.shape[1]):
mask[:, i, :] = cv2.Canny(np.uint8(maxminscale(mask[:, i, :])),50,100)
pred_st[pred_st < thr] = 0
pred_st[pred_st >= thr] = 180
pred_st = zoom(pred_st.copy(), zoom = zoom_fac, order = 0)
mlab.figure(1, size = (1000,800))
mlab.contour3d(pred_st, colormap = 'hot', opacity = 1.0, vmin = 0, vmax = 255)
mlab.contour3d(mask, colormap = 'Greys', opacity = 0.1, vmin = 0, vmax = 150)
mlab.title('Prediction')
mlab.show()
def plot_lungmask(im_st):
mask = np.zeros((im_st.shape[0], im_st.shape[1], im_st.shape[2], 2))
print('get mask')
#for i in range(im_st.shape[0]):
# mask[i, :, :, 1] = lungmask(im_st[i, :, :, 0])
mask[:,:,:,1] = lungmask3D(im_st[:, :, :, 0], morph = False)
for i in range(im_st.shape[1]):
mask[:, i, :, 1] = cv2.Canny(np.uint8(maxminscale(mask[:, i, :, 1])),50,100)
print('plotting')
mlab.contour3d(mask[:, :, :, 1], colormap = 'Blues', opacity = 0.1)
mlab.show()
def plot_lung_and_tumor(im_st, pred_st, gt_st, res = [], thr = 0.5):
mask = np.zeros((im_st.shape[0],im_st.shape[1], im_st.shape[2],2))
#for i in range(im_st.shape[0]):
# mask[i, :, :, 1] = lungmask_pro(im_st[i, :, :, 0], morph = False)
mask[:,:,:,1] = lungmask3D(im_st[:, :, :, 0], morph = False)
for i in range(im_st.shape[1]):
mask[:, i, :, 1] = cv2.Canny(np.uint8(maxminscale(mask[:, i, :, 1])),50,100)
pred_st[pred_st < thr] = 0
pred_st[pred_st >= thr] = 180
gt_st[gt_st >= thr] = 180
# TODO - make zoom_fac dependent on resolution in z dir (true values)
if not res:
zoom_fac = im_st.shape[1]/im_st.shape[0]
else:
zoom_fac = res[0]/res[1]/2
mask = zoom(mask[:,:,:,1], zoom = [zoom_fac,1,1])
pred_st = zoom(pred_st[:, :, :, 1], zoom = [zoom_fac,1,1])
gt_st = zoom(gt_st[:, :, :, 1], zoom = [zoom_fac,1,1])
mlab.figure(1, size = (1000,800))
mlab.contour3d(pred_st, colormap = 'hot', opacity = 1.0, vmin = 0, vmax = 255)
mlab.contour3d(mask, colormap = 'Greys', opacity = 0.1, vmin = 0, vmax = 150)
mlab.title('Prediction')
mlab.figure(2, size = (1000,800))
mlab.contour3d(gt_st, colormap = 'hot', opacity = 1.0, vmin = 0, vmax = 255)
mlab.contour3d(mask, colormap = 'Greys', opacity = 0.1, vmin = 0, vmax = 150)
mlab.title('Ground truth')
mlab.show()
#name of the created files
name = 'pat_12'
if not os.path.exists(save_path):
os.mkdir(save_path)
# read dicom
images, res, offset = import_images(image_path)
#predict/segment with unet
unet_pred = pred_unet(images, unet_model_path)
#classifiy with VGG
VGG_pred = pred_VGG(images, unet_pred, res, VGG_model_path)
#---- To custusX ----
#save segmentation in stl format
pred_to_stl(unet_pred, res, save_path, name)
#save raw and mhd
pred_to_raw(unet_pred, save_path, name, res, offset)
#save lungmask as raw with mhd
mask = lungmask3D(images, morph=False)
images[images < -1024] = -1024
images[images >= 400] = 400
lung = images.copy()
lung[mask == 0] = np.amin(lung)
lung = lung - np.amin(lung)
lung = lung / np.amax(lung)
pred_to_raw(lung, save_path, name + '_lung', res, offset)
def test2d3d(im_st, pred_st, res, thr = 0.1, plot_size_xy = 150, VGG_model_path = ''):
t = TicToc()
im_ct = im_st.copy()
zoom_fac = [res[0]/res[1]/(im_st.shape[1]/plot_size_xy), plot_size_xy/im_st.shape[1], plot_size_xy/im_st.shape[1]]
im_st = zoom(im_st.copy(), zoom = zoom_fac, order = 1)
mask = np.zeros((im_st.shape[0],im_st.shape[1], im_st.shape[2]))
#for i in range(im_st.shape[0]):
# mask[i, :, :] = lungmask_pro(im_st[i, :, :], morph = False)
mask[:,:,:] = lungmask3D(im_st[:, :, :], morph = False)
for i in range(im_st.shape[1]):
mask[:, i, :] = cv2.Canny(np.uint8(maxminscale(mask[:, i, :])),50,100)
pred_st_tmp = pred_st.copy()
pred_st_tmp = zoom(pred_st, zoom = zoom_fac, order = 0)
bin_pred_3d = pred_st_tmp.copy()
bin_pred_3d[bin_pred_3d < thr] = 0
bin_pred_3d[bin_pred_3d >= thr] = 1
bin_pred_2d = pred_st.copy()
bin_pred_2d[bin_pred_2d < thr] = 0
bin_pred_2d[bin_pred_2d >= thr] = 1
#labels
label_3d = label(bin_pred_3d)
label_2d = label(bin_pred_2d)
print('Number of nodules found: ' + str(len(np.unique(label_2d))-1))
mlab.figure(1, size = (1000,800))
mlab.contour3d(pred_st_tmp, colormap = 'hot', opacity = 1.0, vmin = 0, vmax = 1)
def new_thr(event):
tmp_thr = pred_st[int(slider2.val)].copy()
tmp_3d = pred_st_tmp.copy()
threshold = event
tmp_thr[tmp_thr < threshold] = 0
tmp_thr[tmp_thr >= threshold] = 1
tmp_3d[tmp_3d < threshold] = 0
tmp_3d[tmp_3d >= threshold] = 1
mlab.clf()
#mlab.figure(1, size = (1000,800))
mlab.contour3d(tmp_3d, colormap = 'hot', opacity = 1.0, vmin = 0, vmax = 1)
if button1.get_status()[0]:
mlab.contour3d(mask, colormap = 'Greys', opacity = 0.1, vmin = 0, vmax = 150)
#mlab.title('Prediction')
ax.clear()
ax.imshow(im_ct[int(slider2.val), :, :], cmap = 'gray')
#ax.imshow(tmp_thr, cmap = 'inferno', vmin = 0, vmax = 1, alpha = 0.3)
ax.imshow(tmp_thr, cmap = 'gnuplot', vmin = 0, vmax = 2, alpha = 0.3)
#ax.set_title('predicted')
ax.set_axis_off()
f.suptitle('slice '+str(slider2.val))
f.canvas.draw_idle()
def images(event):
threshold = slider1.val
tmp = pred_st[int(event)].copy()
tmp[tmp < threshold] = 0
tmp[tmp >= threshold] = 1
ax.clear()
ax.imshow(im_ct[int(event), :, :], cmap = 'gray')
#ax.imshow(tmp, cmap = 'inferno', vmin = 0, vmax = 1, alpha = 0.3)
ax.imshow(tmp, cmap = 'gnuplot', vmin = 0, vmax = 2, alpha = 0.3)
ax.set_title('predicted')
ax.set_axis_off()
f.suptitle('slice '+str(int(slider2.val)))
f.canvas.draw_idle()
def up_scroll_alt(event):
if event.key == "up":
if (slider2.val + 2 > im_ct.shape[0]):
1
#print("Whoops, end of stack", print(slider2.val))
else:
slider2.set_val(slider2.val + 1)
def down_scroll_alt(event):
if event.key == "down":
if (slider2.val - 1 < 0):
1
#print("Whoops, end of stack", print(slider2.val))
else:
slider2.set_val(slider2.val - 1)
def up_scroll(event):
if event.button == 'up':
if (slider2.val + 2 > im_ct.shape[0]):
1
#print("Whoops, end of stack", print(slider2.val))
else:
slider2.set_val(slider2.val + 1)
def down_scroll(event):
if event.button == 'down':
if (slider2.val - 1 < 0):
1
#print("Whoops, end of stack", print(slider2.val))
else:
slider2.set_val(slider2.val - 1)
def show_lung(event):
if not button4.get_status()[0]:
tmp_3d = pred_st_tmp.copy()
threshold = slider1.val
tmp_3d[tmp_3d < threshold] = 0
tmp_3d[tmp_3d >= threshold] = 1
mlab.clf()
mlab.contour3d(tmp_3d, colormap = 'hot', opacity = 1.0, vmin = 0, vmax = 1)
if button1.get_status()[0]:
mlab.contour3d(mask, colormap = 'Greys', opacity = 0.1, vmin = 0, vmax = 150)
elif button4.get_status()[0]:
pred_class = pred_VGG(im_ct, pred_st, res, VGG_model_path)
pred_class = zoom(pred_class.copy(), zoom = zoom_fac, order = 0)
mlab.clf()
print(np.unique(pred_class)[1:])
for i in np.unique(pred_class):
if i == 0:
continue
tmp = pred_class.copy()
tmp[pred_class != i] = 0
mlab.contour3d(tmp, colormap = 'OrRd', color = tuple(colors[int(round((9/5)*i)),:]), vmin = 1, vmax = 5)
mlab.scalarbar(orientation = 'vertical', nb_labels = 9, label_fmt='%.1f')
if button1.get_status()[0]:
mlab.contour3d(mask, colormap = 'Greys', opacity = 0.1, vmin = 0, vmax = 150)
mlab.orientation_axes(xlabel = 'z', ylabel = 'y', zlabel = 'x')
def remove(event):
# only add when seed point selected is at an axis
if (event.ydata != None) or (event.xdata != None):
ix, iy = int(event.ydata), int(event.xdata)
# if already in add mode -> switch to remove mode
# if button3.get_status()[0]:
# button3.set_active(0)
#print(event)
if (str(event.inaxes).split('(')[0] == 'AxesSubplot') and button2.get_status()[0]:
# remove
coords_zoom = (np.array([slider2.val, ix, iy])*np.array(zoom_fac)).astype(int)
coords_orig = (int(slider2.val), int(ix), int(iy))
print(coords_zoom,coords_orig)
val_3d = label_3d[tuple(coords_zoom)]
pred_st_tmp[label_3d == val_3d] = 0
val_2d = label_2d[coords_orig]
pred_st[label_2d == val_2d] = 0
# re-plot
tmp_thr = pred_st[int(slider2.val)].copy()
tmp_3d = pred_st_tmp.copy()
threshold = slider1.val
tmp_thr[tmp_thr < threshold] = 0
tmp_thr[tmp_thr >= threshold] = 1
tmp_3d[tmp_3d < threshold] = 0
tmp_3d[tmp_3d >= threshold] = 1
mlab.clf()
mlab.contour3d(tmp_3d, colormap = 'hot', opacity = 1.0, vmin = 0, vmax = 1)
if button1.get_status()[0]:
mlab.contour3d(mask, colormap = 'Greys', opacity = 0.1, vmin = 0, vmax = 150)
ax.clear()
ax.imshow(im_ct[int(slider2.val), :, :], cmap = 'gray')
#ax.imshow(tmp_thr, cmap = 'inferno', vmin = 0, vmax = 1, alpha = 0.3)
ax.imshow(tmp_thr, cmap = 'gnuplot', vmin = 0, vmax = 2, alpha = 0.3)
cursor = Cursor(ax, useblit=True, color='orange', linewidth=0.5)
#ax.set_title('predicted')
ax.set_axis_off()
f.suptitle('slice '+str(slider2.val))
f.canvas.draw_idle()
# def add_window(event):
# # only add when seed point selected is at an axis
# if (event.ydata != None) or (event.xdata != None):
# ix, iy = int(event.ydata), int(event.xdata)
# if (str(event.inaxes).split('(')[0] == 'AxesSubplot') and button3.get_status()[0]:
# figs.show()
def add(event):
def adv_exit(event):
print(1)
plt.close(figs)
#plt.close()
def adv_start(event):
print(2)
plt.close(figs)
def adv_window():
figs, axx = plt.subplots(num = 'Advanced settings')
figs.canvas.mpl_connect('key_press_event', adv_exit)
axx.axis('off')
#bx1_as = plt.axes([0.05, 0.3, 0.15, 0.11])
#bx1_as.set_axis_off()
#button_as1 = CheckButtons(bx1_as, ['lambda1'], [1])
axx.axis('off')
ax_as1 = plt.axes([0.15, 0.02, 0.5, 0.05])
slider_as1 = Slider(ax_as1, 'lambda1', 0.1, 4, dragging = True, valstep = 0.1)
ax_as2 = plt.axes([0.15, 0.10, 0.5, 0.05])
slider_as2 = Slider(ax_as2, 'lambda2', 0.1, 4, dragging = True, valstep = 0.1)
ax_as3 = plt.axes([0.15, 0.18, 0.5, 0.05])
slider_as3 = Slider(ax_as3, 'smoothing', 0, 4, dragging = True, valstep = 1)
ax_as4 = plt.axes([0.15, 0.26, 0.5, 0.05])
slider_as4 = Slider(ax_as4, 'iterations', 1, 1000, dragging = True, valstep = 1)
ax_as5 = plt.axes([0.15, 0.34, 0.5, 0.05])
slider_as5 = Slider(ax_as5, 'radius', 0.5, 5, dragging = True, valstep = 0.1)
ax_b1 = plt.axes([0.85, 0.15, 0.07, 0.08])
ax_b2 = plt.axes([0.85, 0.05, 0.07, 0.08])
but_as1 = Button(ax_b1, 'exit', color = 'beige', hovercolor = 'beige')
but_as2 = Button(ax_b2, 'start', color = 'beige', hovercolor = 'beige')
#ax_textbox = plt.axes([0, 0.4, 0.5, 0.4])
#axx.axis('off')
textstr = "Press ENTER in terminal to start segmentation. \n Shouldn't be necessairy to change settings below, but can be tuned if \n resulting segmentation is not ideal. \n Especially if small nodule: try setting lambda1 <= lambda2 \n Or if very nonhomogeneous nodule: try setting lambda1 > lambda2."
props = dict(boxstyle='round', facecolor='wheat')
axx.text(-0.18, 0.25, textstr, transform=ax.transAxes, fontsize=12,
verticalalignment='top', bbox=props)
slider_as1.set_val(lambda1)
slider_as2.set_val(lambda2)
slider_as3.set_val(smoothing)
slider_as4.set_val(iterations)
slider_as5.set_val(rad)
but_as1.on_clicked(adv_exit)
but_as2.on_clicked(adv_start)
figs.canvas.draw_idle()
return figs, axx, slider_as1, slider_as2, slider_as3, slider_as4, slider_as5
# only add when seed point selected is at an axis
if (event.ydata != None) or (event.xdata != None):
ix, iy = int(event.ydata), int(event.xdata)
if (str(event.inaxes).split('(')[0] == 'AxesSubplot') and button3.get_status()[0]:
# default settings for levet set
lambda1=1; lambda2=4; smoothing = 1; iterations = 100; rad = 3
# advanced settings window pop-up
figs, axx, slider_as1, slider_as2, slider_as3, slider_as4, slider_as5 = adv_window()
figs.show()
# to start segmentation
input('Press enter to start segmentation: ')
plt.close(figs)
# add
coords_zoom = (np.array([slider2.val, ix, iy])*np.array(zoom_fac)).astype(int)
coords_orig = (int(slider2.val), int(ix), int(iy))
print(coords_zoom,coords_orig)
# apply level set to grow in 3D from single seed point
seg_tmp = level_set3D(im_ct, coords_orig, list(reversed(res)), lambda1=slider_as1.val, lambda2=slider_as2.val, smoothing = int(slider_as3.val), iterations = int(slider_as4.val), rad = slider_as5.val)
#seg_tmp = level_set3D(im_ct, coords_orig, list(reversed(res)), smoothing = int(slider_as3.val), iterations = int(slider_as4.val), rad = slider_as5.val, method = 'GAC', alpha = 150, sigma = 5, balloon = 1)
# if no nodule was segmented, break; else continue
if (seg_tmp is None):
print('No nodule was segmented. Try changing parameters...')
return None
else:
# because of interpolation, went from {0,1} -> [0,1]. Need to threshold to get binary segment
seg_tmp[seg_tmp < 0.5] = 0
seg_tmp[seg_tmp >= 0.5] = 1
pred_st[seg_tmp == 1] = 1
label_2d[seg_tmp == 1] = len(np.unique(label_2d)) # OBS: need to add new label for new segment, in order to remove it properly!
seg_tmp_3d = zoom(seg_tmp.copy(), zoom = zoom_fac, order = 1)
seg_tmp_3d[seg_tmp_3d < 0.5] = 0
seg_tmp_3d[seg_tmp_3d >= 0.5] = 1
pred_st_tmp[seg_tmp_3d == 1] = 1
label_3d[seg_tmp_3d == 1] = len(np.unique(label_3d))
# re-plot
tmp_thr = pred_st[int(slider2.val)].copy()
tmp_3d = pred_st_tmp.copy()
threshold = slider1.val
tmp_thr[tmp_thr < threshold] = 0
tmp_thr[tmp_thr >= threshold] = 1
tmp_3d[tmp_3d < threshold] = 0
tmp_3d[tmp_3d >= threshold] = 1
mlab.clf()
mlab.contour3d(tmp_3d, colormap = 'hot', opacity = 1.0, vmin = 0, vmax = 1)
if button1.get_status()[0]:
mlab.contour3d(mask, colormap = 'Greys', opacity = 0.1, vmin = 0, vmax = 150)
ax.clear()
ax.imshow(im_ct[int(slider2.val), :, :], cmap = 'gray')
#ax.imshow(tmp_thr, cmap = 'inferno', vmin = 0, vmax = 1, alpha = 0.3)
ax.imshow(tmp_thr, cmap = 'gnuplot', vmin = 0, vmax = 2, alpha = 0.3)
cursor = Cursor(ax, useblit=True, color='orange', linewidth=0.5)
#ax.set_title('predicted')
ax.set_axis_off()
f.suptitle('slice '+str(slider2.val))
f.canvas.draw_idle()
def remove_mode(event):
# if already in add mode -> switch to remove mode
if button3.get_status()[0]:
button3.set_active(0)
f.canvas.mpl_connect('button_press_event', remove)
def add_mode(event):
# if already in remove mode -> switch to add mode
if button2.get_status()[0]:
button2.set_active(0)
f.canvas.mpl_connect('button_press_event', add)
def classify(event):
if button4.get_status()[0]:
pred_class = pred_VGG(im_ct, pred_st, res, VGG_model_path)
pred_class = zoom(pred_class.copy(), zoom = zoom_fac, order = 0)
mlab.clf()
print(np.unique(pred_class)[1:])
for i in np.unique(pred_class):
if i == 0:
continue
tmp = pred_class.copy()
tmp[pred_class != i] = 0
mlab.contour3d(tmp, colormap = 'OrRd', color = tuple(colors[int(round((9/5)*i)),:]), vmin = 1, vmax = 5)
mlab.scalarbar(orientation = 'vertical', nb_labels = 9, label_fmt='%.1f')
if button1.get_status()[0]:
mlab.contour3d(mask, colormap = 'Greys', opacity = 0.1, vmin = 0, vmax = 150)
mlab.orientation_axes(xlabel = 'z', ylabel = 'y', zlabel = 'x')
elif not button4.get_status()[0]:
# re-plot without classify
tmp_thr = pred_st[int(slider2.val)].copy()
tmp_3d = pred_st_tmp.copy()
threshold = slider1.val
tmp_thr[tmp_thr < threshold] = 0
tmp_thr[tmp_thr >= threshold] = 1
tmp_3d[tmp_3d < threshold] = 0
tmp_3d[tmp_3d >= threshold] = 1
mlab.clf()
mlab.contour3d(tmp_3d, colormap = 'hot', opacity = 1.0, vmin = 0, vmax = 1)
if button1.get_status()[0]:
mlab.contour3d(mask, colormap = 'Greys', opacity = 0.1, vmin = 0, vmax = 150)
ax.clear()
ax.imshow(im_ct[int(slider2.val), :, :], cmap = 'gray')
#ax.imshow(tmp_thr, cmap = 'inferno', vmin = 0, vmax = 1, alpha = 0.3)
ax.imshow(tmp_thr, cmap = 'gnuplot', vmin = 0, vmax = 2, alpha = 0.3)
ax.set_axis_off()
f.suptitle('slice '+str(slider2.val))
f.canvas.draw_idle()
# Exit app when ESC is pressed
def quit(event):
if event.key == "escape":
plt.close()
mlab.close()
# default settings for levet set for add-event
lambda1=1; lambda2=4; smoothing = 1; iterations = 100; rad = 3
# colormap for 3D-malignancy-plot for classify-event
dz = list(range(1,10))
norm = plt.Normalize()
colors = plt.cm.OrRd(norm(dz))
colors = np.array(colors)[:,:3]
# plot to make simulator gator
f, ax = plt.subplots(1,1, figsize = (12, 12))
f.canvas.mpl_connect('key_press_event', up_scroll_alt)
f.canvas.mpl_connect('key_press_event', down_scroll_alt)
f.canvas.mpl_connect('scroll_event', up_scroll)
f.canvas.mpl_connect('scroll_event', down_scroll)
f.canvas.mpl_connect('key_press_event', quit)
#f.canvas.mpl_connect('button_press_event', remove)
#f.canvas.mpl_connect('button_press_event', add)
b1ax = plt.axes([0.05, 0.2, 0.15, 0.11])
b1ax.set_axis_off()
b2ax = plt.axes([0.05, 0.35, 0.15, 0.11])
b2ax.set_axis_off()
b3ax = plt.axes([0.05, 0.5, 0.15, 0.11])
b3ax.set_axis_off()
b4ax = plt.axes([0.05, 0.65, 0.15, 0.11])
b4ax.set_axis_off()
s1ax = plt.axes([0.25, 0.08, 0.5, 0.03])
s2ax = plt.axes([0.25, 0.02, 0.5, 0.03])
slider1 = Slider(s1ax, 'threshold', 0.1, 1.0, dragging = True, valstep = 0.05)
slider2 = Slider(s2ax, 'slice', 0.0, im_ct.shape[0]-1, valstep = 1)
button1 = CheckButtons(b1ax, ['lung'], [False])
button2 = CheckButtons(b2ax, ['Remove'], [False])
button3 = CheckButtons(b3ax, ['Add'], [False])
button4 = CheckButtons(b4ax, ['Classify'], [False])
slider1.set_val(0.5)
slider2.set_val(0)
f.subplots_adjust(bottom = 0.15)
#ax.imshow(im_ct[int(slider2.val), :, :], cmap = 'gray')
#ax.imshow(pred_st[int(slider2.val), :, :], cmap = 'inferno', vmin = 0, vmax = 1, alpha = 0.3)
ax.imshow(im_ct[int(slider2.val), :, :], cmap = 'gray')
ax.imshow(pred_st[int(slider2.val), :, :], cmap = 'gnuplot', vmin = 0, vmax = 2, alpha = 0.3)
cursor = Cursor(ax, useblit=True, color='orange', linewidth=0.5)
ax.set_title('predicted')
ax.set_axis_off()
f.suptitle('slice '+str(slider2.val))
button1.on_clicked(show_lung)
button2.on_clicked(remove_mode)
button3.on_clicked(add_mode)
button4.on_clicked(classify)
slider1.on_changed(new_thr)
slider2.on_changed(images)
plt.show()