def get_val_dice(cnn, config):
val_dsc = []
taskdir = cfg['taskdir']
# load array from selected val to test dsc and compare with cart vw label
if os.path.exists(taskdir + '/valarray_cart.npy'):
xarray_val = np.load(taskdir + '/valarray_cart.npy')
xlabel_val = np.load(taskdir + '/vallabel_cart.npy')
else:
val_exams = dbloader.list_exams('val', shuffle=False)
xarray_val = []
xlabel_val = []
bi = 0
for ei in val_exams:
caseloader = CaseLoader(ei)
print(caseloader)
for slicei in caseloader.slices:
if caseloader.valid_slicei(slicei) == False:
continue
if bi % 20 == 0:
polarstack = caseloader.loadstack(slicei,
'cart_patch',
nei=config['depth'] // 2)
xarray_val.append(polarstack[..., None])
cartabel = caseloader.loadstack(slicei,
'cart_label',
nei=config['depth'] //
2)[...,
config['depth'] // 2]
xlabel_val.append(cartabel[:, :, 1] - cartabel[:, :, 0])
bi += 1
xarray_val = np.array(xarray_val)
xlabel_val = np.array(xlabel_val)
print(xarray_val.shape, xlabel_val.shape)
np.save(taskdir + '/valarray_cart.npy', xarray_val)
np.save(taskdir + '/vallabel_cart.npy', xlabel_val)
polarbd_all = cnn.predict(xarray_val,
batch_size=config['G'] * config['batchsize'])
for ti in range(xarray_val.shape[0]):
carlabel = xlabel_val[ti]
polarimg = xarray_val[ti]
polarbd = polarbd_all[ti] * config['width'] // 2
# polarprob = polarprobr[:,:,0,1]-polarprobr[:,:,0,0]
contourin, contourout = toctbd(polarbd, config['patchheight'],
config['patchheight'])
carseg = plotct(polarimg.shape[1], contourin, contourout)
cdsc = DSC(carlabel, carseg)
val_dsc.append(cdsc)
# pxconf = (np.sum(polarprob[polarseg>0])-np.sum(polarprob[polarseg==0]))/np.sum(polarseg>0)
# sconf.append(pxconf)
print('\rPred',
ti,
'/',
xarray_val.shape[0],
'val dice',
'%.5f' % np.mean(val_dsc),
end="")
return np.mean(val_dsc)
def polar_seg_slice(vwcnn,
vwcfg,
dicomstack,
slicei,
cts,
SCALE=4,
usegrad=False,
ITERPRED=True,
cartreg=False):
DEBUG = 0
contourin = None
contourout = None
polarconsistency = None
recenters = None
if DEBUG:
print(slicei, 'cts', cts)
if len(cts) == 1:
ct = cts[0]
ctx = ct[0]
cty = ct[1]
if ITERPRED == False:
cartstack = crop_dcm_stack(dicomstack, slicei, cty, ctx, 64, 1)
cartstack = cartstack / np.max(cartstack)
polarimg = topolar(cartstack, 64, 64)
polar_img_rz = cv2.resize(polarimg, (0, 0), fx=SCALE, fy=SCALE)
if cartreg:
cart_stack_rz = cv2.resize(cartstack, (0, 0), fx=2, fy=2)
contbd = vwcnn.predict(cart_stack_rz[None, :, :, :,
None])[0] * 256
contour_in_roi = contbd[:, :2] + 256
contour_out_roi = contbd[:, 2:] + 256
contourin = to_dcm_cord(contour_in_roi, BB(ctx, cty, 0, 0))
contourout = to_dcm_cord(contour_out_roi, BB(ctx, cty, 0, 0))
polarconsistency = None
else:
polarbd, polarsd = polar_pred_cont_cst(polar_img_rz[:, :, :,
None],
vwcfg,
vwcnn,
usegrad=usegrad)
polarconsistency = 1 - np.mean(polarsd, axis=0)
# contour positions [x, y] in original dicom space
contourin, contourout = toctbd(polarbd / SCALE, ctx, cty)
if DEBUG:
contourin, contourout = toctbd(polarbd, 256, 256)
cartvw = plotct(512, contourin, contourout)
plt.subplot(1, 2, 1)
cartstackdsp = copy.copy(cartstack)
cartstackdsp[:,
cartstackdsp.shape[1] // 2] = np.max(cartstackdsp)
cartstackdsp[cartstackdsp.shape[0] // 2] = np.max(cartstackdsp)
plt.imshow(cartstackdsp)
plt.subplot(1, 2, 2)
cartvwdsp = copy.copy(cartvw)
cartvwdsp[:, cartvwdsp.shape[1] // 2] = np.max(cartvwdsp)
cartvwdsp[cartvwdsp.shape[0] // 2] = np.max(cartvwdsp)
plt.imshow(cartvwdsp)
plt.show()
# offset in 512 dcm cordinate
else:
# iterative pred and refine center
REPS = 20
MOVEFACTOR = 0.5
MAX_OFF_STEP = 1
mincty = cty
minctx = ctx
mindiff = np.inf
lastoffset = [0, 0]
if DEBUG:
print('init', ctx, cty)
for repi in range(REPS):
if repi == REPS - 1 or MAX_OFF_STEP < 0.1:
if DEBUG:
print('use min diff cts', minctx, mincty,
'with mindiff', mindiff)
cty = mincty
ctx = minctx
cartstack = crop_dcm_stack(dicomstack, slicei, cty, ctx, 64, 1)
cartstack = cartstack / np.max(cartstack)
polarimg = topolar(cartstack, 64, 64)
polar_img_rz = cv2.resize(polarimg, (0, 0), fx=SCALE, fy=SCALE)
polarbd, polarsd = polar_pred_cont_cst(polar_img_rz[:, :, :,
None],
vwcfg,
vwcnn,
usegrad=usegrad)
if DEBUG:
contourin, contourout = toctbd(polarbd, 256, 256)
cartvw = plotct(512, contourin, contourout)
plt.subplot(1, 2, 1)
cartstackdsp = copy.copy(cartstack)
cartstackdsp[:, cartstackdsp.shape[1] //
2] = np.max(cartstackdsp)
cartstackdsp[cartstackdsp.shape[0] //
2] = np.max(cartstackdsp)
plt.imshow(cartstackdsp)
plt.subplot(1, 2, 2)
cartvwdsp = copy.copy(cartvw)
cartvwdsp[:, cartvwdsp.shape[1] // 2] = np.max(cartvwdsp)
cartvwdsp[cartvwdsp.shape[0] // 2] = np.max(cartvwdsp)
plt.imshow(cartvwdsp)
plt.show()
#offset in 512 dcm cordinate
polar_cont_offset = cal_polar_offset(polarbd)
if repi > 0 and MAX_OFF_STEP > 0.1 and lastoffset == polar_cont_offset:
#MAX_OFF_STEP += 0.5/SCALE
MAX_OFF_STEP = 0.09
if DEBUG:
print('move ct no change', MAX_OFF_STEP, lastoffset,
polar_cont_offset)
continue
cofftype = [polar_cont_offset[0] > 0, polar_cont_offset[1] > 0]
if repi == 0:
offtype = cofftype
if repi > 2 and cofftype != offtype:
MAX_OFF_STEP /= 2
if DEBUG:
print('reduce max move to', MAX_OFF_STEP)
offtype = cofftype
polarconsistency = 1 - np.mean(polarsd, axis=0)
#ccstl = polarconsistency[0]
#ccstw = polarconsistency[1]
# contour positions [x, y] in original dicom space
contourin, contourout = toctbd(polarbd / SCALE, ctx, cty)
cdif = np.max(abs(np.array(polar_cont_offset)))
if cdif < 1 or MAX_OFF_STEP < 0.1:
if cdif < 1:
print(polar_cont_offset)
print('==', slicei, 'Break tracklet ref',
polar_cont_offset)
break
if cdif < mindiff:
mindiff = cdif
mincty = cty
minctx = ctx
cofx = polar_cont_offset[0]
cofy = polar_cont_offset[1]
if abs(polar_cont_offset[0]) < 1:
cofx = 0
if abs(polar_cont_offset[1]) < 1:
cofy = 0
if repi < 2:
ctx += cofx * MOVEFACTOR
cty += cofy * MOVEFACTOR
else:
ctx = ctx + max(-MAX_OFF_STEP,
min(MAX_OFF_STEP, cofx * MOVEFACTOR))
cty = cty + max(-MAX_OFF_STEP,
min(MAX_OFF_STEP, cofy * MOVEFACTOR))
print('repeat', repi, 'offset', polar_cont_offset, ctx, cty)
lastoffset = polar_cont_offset
recenters = [[ctx, cty]]
else:
print('multiple cts', cts)
all_contour_in = []
all_contour_out = []
all_polarconsistency = []
for ct in cts:
ctx = ct[0]
cty = ct[1]
cartstack = crop_dcm_stack(dicomstack, slicei, cty, ctx, 64, 1)
cartstack = cartstack / np.max(cartstack)
polarimg = topolar(cartstack, 64, 64)
polar_img_rz = cv2.resize(polarimg, (0, 0), fx=SCALE, fy=SCALE)
polarbd, polarsd = polar_pred_cont_cst(polar_img_rz[:, :, :, None],
vwcfg, vwcnn)
polarconsistency_c = 1 - np.mean(polarsd, axis=0)
contour_in_c, contour_out_c = toctbd(polarbd / SCALE, ctx, cty)
if DEBUG:
plt.subplot(1, 2, 1)
plt.imshow(polarimg)
plt.subplot(1, 2, 2)
polarseg = plotpolar(256, polarbd)
plt.imshow(polarseg)
plt.show()
all_contour_in.append(contour_in_c)
all_contour_out.append(contour_out_c)
all_polarconsistency.append(polarconsistency_c)
contourin = mergecont(all_contour_in, cts)
contourout = mergecont(all_contour_out, cts)
ctx, cty = np.mean(cts, axis=0)
if DEBUG:
seg_vw_merge = plotct(512 * SCALE, contourin * SCALE,
contourout * SCALE)
predseg = croppatch(seg_vw_merge, cty * SCALE, ctx * SCALE,
64 * SCALE, 64 * SCALE)
plt.imshow(predseg)
plt.show()
polarconsistency = np.mean(np.array(all_polarconsistency), axis=0)
#no center adjustment for multi ct prediction
recenters = [[ctx, cty]]
return [contourin, contourout], polarconsistency, recenters
def gen_aug_patch(caseloader, slicei, dcmstack=None):
DEBUG = 0
ctx, cty = caseloader.loadct(slicei)
if dcmstack is None:
dcmstack = caseloader.loadstack(slicei, 'dcm')
cartcont = caseloader.load_cart_cont(slicei)
#cart_patch = croppatch(dcmstack,cty,ctx,256,256)
#plt.imshow(cart_patch)
#plt.show()
mindist = caseloader.load_cart_min_dist(slicei)
dist_pred_ct_gt, nms_dist_map_p_gt = find_nms_center(mindist, fig=1)
validy, validx = np.where(nms_dist_map_p_gt[:, :, 0] > 128)
'''
for rd in range(5):
rnd = random.randint(0,validx.shape[0]-1)
ckey = '%d-%d'%(validx[rnd],validy[rnd])
dist_pred_ct_gt[ckey] = nms_dist_map_p_gt[validy[rnd],validx[rnd],0]/256
'''
xypos = [[validx[i], validy[i]] for i in range(len(validx))]
kmeans = KMeans(n_clusters=5, random_state=0).fit(xypos)
for kmcti in kmeans.cluster_centers_:
kmcx = int(round(kmcti[0]))
kmcy = int(round(kmcti[1]))
ckey = '%d-%d' % (kmcx, kmcy)
if nms_dist_map_p_gt[kmcy, kmcx, 0] < 127:
print('kmean cluster center not large enough', kmcti,
nms_dist_map_p_gt[kmcy, kmcx, 0])
dist_pred_ct_gt[ckey] = nms_dist_map_p_gt[kmcy, kmcx, 0] / 256
#print(dist_pred_ct_gt)
aug_patch_obj = {}
targetdir = DATADESKTOPdir + '/DVWIMAGES/'
aug_cart_patch_batch = []
new_cart_patch_batch_filename = '/casepatch/'+caseloader.art+'/'+caseloader.pjname+'/augcart/'+caseloader.pi+os.path.basename(caseloader.dcmpath(slicei))[:-4]+\
caseloader.side+'.npy'
aug_patch_obj['cartpatchbatchname'] = new_cart_patch_batch_filename
if not os.path.exists(targetdir + '/casepatch/' + caseloader.art + '/' +
caseloader.pjname + '/augcart/'):
os.mkdir(targetdir + '/casepatch/' + caseloader.art + '/' +
caseloader.pjname + '/augcart/')
aug_polar_patch_batch = []
new_polar_patch_batch_filename = '/casepatch/'+caseloader.art+'/'+caseloader.pjname+'/augpolar/'+caseloader.pi+os.path.basename(caseloader.dcmpath(slicei))[:-4]+\
caseloader.side+'.npy'
aug_patch_obj['polarpatchbatchname'] = new_polar_patch_batch_filename
if not os.path.exists(targetdir + '/casepatch/' + caseloader.art + '/' +
caseloader.pjname + '/augpolar/'):
os.mkdir(targetdir + '/casepatch/' + caseloader.art + '/' +
caseloader.pjname + '/augpolar/')
aug_polar_cont_batch = []
new_polar_cont_batch_filename = '/casepatch/'+caseloader.art+'/'+caseloader.pjname+'/augpolarcont/'+caseloader.pi+os.path.basename(caseloader.dcmpath(slicei))[:-4]+\
caseloader.side+'.npy'
aug_patch_obj['polarcoutbatchname'] = new_polar_cont_batch_filename
if not os.path.exists(targetdir + '/casepatch/' + caseloader.art + '/' +
caseloader.pjname + '/augpolarcont/'):
os.mkdir(targetdir + '/casepatch/' + caseloader.art + '/' +
caseloader.pjname + '/augpolarcont/')
aug_patch_obj['auginfo'] = []
#plt.imshow(dcmstack)
#plt.title('dcmstack')
#plt.show()
for cti in dist_pred_ct_gt.keys():
cctx = int(round(int(cti.split('-')[0]) + ctx - 256))
ccty = int(round(int(cti.split('-')[1]) + cty - 256))
#print(cctx,ccty)
tx = cctx - ctx
ty = ccty - cty
new_cart_patch = croppatch(dcmstack, ccty, cctx, 256, 256)
#plt.imshow(new_cart_patch[:,:,1])
#plt.title('new_cart_patch')
#plt.show()
new_cart_patch = new_cart_patch / np.max(new_cart_patch)
aug_cart_patch_batch.append(new_cart_patch)
new_polar_patch = topolar(new_cart_patch, 256, 256)
new_polar_patch = new_polar_patch / np.max(new_polar_patch)
#plt.imshow(new_polar_patch)
#plt.show()
aug_polar_patch_batch.append(new_polar_patch)
#rebase polar cont
new_polar_cont_lumen = tocordpolar(cartcont[0], cctx, ccty)
new_polar_cont_wall = tocordpolar(cartcont[1], cctx, ccty)
aug_polar_cont = np.zeros((256, 2))
aug_polar_cont[:, 0] = new_polar_cont_lumen
aug_polar_cont[:, 1] = new_polar_cont_wall
#exportpolarcontour(new_polar_cont_filename,[new_polar_cont_lumen,new_polar_cont_wall])
aug_polar_cont_batch.append(aug_polar_cont)
if DEBUG:
#check polar ct matches with cart patch
cart_patch_dsp = np.zeros(
(new_cart_patch.shape[0], new_cart_patch.shape[1], 3))
cart_patch_dsp[:, :,
0] = new_cart_patch[:, :,
new_cart_patch.shape[2] // 2]
polarbd = np.concatenate(
[new_polar_cont_lumen[:, None], new_polar_cont_wall[:, None]],
axis=1) * 256
contourin, contourout = toctbd(polarbd, 256, 256)
cart_vw_seg = plotct(512, contourin, contourout)
plt.imshow(cart_vw_seg)
cart_patch_dsp[:, :, 1] = cart_vw_seg
plt.imshow(cart_patch_dsp)
plt.show()
augobj = {}
augobj['ctofx'] = cctx
augobj['ctofy'] = ccty
augobj['transx'] = tx
augobj['transy'] = ty
augobj['augid'] = len(aug_cart_patch_batch) - 1
aug_patch_obj['auginfo'].append(copy.copy(augobj))
#print(augobj)
np.save(targetdir + new_cart_patch_batch_filename,
np.array(aug_cart_patch_batch, dtype=np.float16))
np.save(targetdir + new_polar_patch_batch_filename,
np.array(aug_polar_patch_batch, dtype=np.float16))
np.save(targetdir + new_polar_cont_batch_filename,
np.array(aug_polar_cont_batch, dtype=np.float16))
caseloader.caselist['slices'][slicei]['augpatch'] = aug_patch_obj
with open(caseloader.caselistname, 'wb') as fp:
pickle.dump(caseloader.caselist, fp)
def plotvw(self,slicei):
if self.seg['cartcont']['lumen'][slicei] is None:
return np.zeros((512,512))
vw_seg = plotct(512, self.seg['cartcont']['lumen'][slicei], self.seg['cartcont']['wall'][slicei])
return vw_seg
