def nearest_neighbor(img,
label,
container,
holder,
DISPLACEMENT=100,
MASSTHRES=0.2):
"""Link two cells if they are the only two cells within DISPLACEMENT and MASSTHRES.
Args:
DISPLACEMENT (int): The maximum distance (in pixel)
MASSTHRES (float): The maximum difference of total intensity changes.
0.2 means it allows for 20% total intensity changes.
"""
DISPLACEMENT = DISPLACEMENT
MASSTHRES = MASSTHRES
withinarea = container._dist_unlinked < DISPLACEMENT
withinmass = abs(container._masschange_unlinked) < MASSTHRES
binarymat = withinarea * withinmass
binarycost = binarymat
good_curr_idx, good_prev_idx = find_one_to_one_assign(binarycost)
prev_cells, curr_cells = container.unlinked
for ci, pi in zip(good_curr_idx, good_prev_idx):
prev_cells[pi].next = curr_cells[ci]
return container
def watershed_distance(img, label, container, holder, ERODI=5,
DEBRISAREA=50, DISPLACEMENT=50, MASSTHRES=0.2):
'''watershed existing label, meaning make a cut at the deflection.
After the cuts, objects will be linked if they are within DISPLACEMENT and MASSTHRES.
If two candidates are found, it will pick a closer one.
Args:
ERODI (int): Erosion size element for generating watershed seeds.
Smaller ERODI will allow more cuts.
DISPLACEMENT (int): The maximum distance (in pixel)
MASSTHRES (float): The maximum difference of total intensity changes.
0.2 means it allows for 20% total intensity changes.
'''
untr_prev, untr_curr = container.unlinked
mask_untracked = container._label_untracked.astype(bool)
wshed_label = watershed(mask_untracked, ERODI)
wshed_label = skilabel(sizefilterandopen(wshed_label, DEBRISAREA, np.Inf, 0))
newcells = CellListMaker(img, wshed_label, holder, holder.frame).make_list()
distanceUntracked = _distance_diff(untr_prev, newcells)
masschangeUntracked = _totalintensity_difference(untr_prev, newcells)
withinarea = distanceUntracked < DISPLACEMENT
withinmass = abs(masschangeUntracked) < MASSTHRES
withinareaMass = withinarea * withinmass
withinareaMass = pick_closer_binarycostmat(withinareaMass, distanceUntracked)
good_curr_idx, good_prev_idx = find_one_to_one_assign(withinareaMass)
# update the link
for ci, pi in zip(good_curr_idx, good_prev_idx):
untr_prev[pi].next = newcells[ci]
# Get all linear coordinates from good newly segmented cells
good_curr_coords = [newcells[n].prop.coords for n in good_curr_idx]
lin_curr_coords = [convert_coords_to_linear(i, holder.img_shape) for i in flatlist(good_curr_coords)]
# find cells in old mask (in current) that overlaps with good new cells
old_cells_to_remove, lin_old_coords_remove = find_coords_overlap_coords(untr_curr, lin_curr_coords, holder.img_shape)
# find cells in new mask which overlaps with the cells in old mask
newcells_to_update = find_cells_overlap_linear_coords(newcells, lin_old_coords_remove, holder.img_shape)
# remove old cells
for old_cell in old_cells_to_remove:
container.curr_cells.remove(old_cell)
# add new cells
container.curr_cells.extend(newcells_to_update)
return container
def track_neck_cut(img,
label,
container,
holder,
ERODI=5,
DEBRISAREA=50,
DISPLACEMENT=50,
MASSTHRES=0.2,
LIM=10,
EDGELEN=5,
THRES_ANGLE=180,
STEPLIM=10):
"""
Adaptive segmentation by using tracking informaiton.
Separate two objects by making a cut at the deflection. For each points on the outline,
it will make a triangle separated by EDGELEN and calculates the angle facing inside of concave.
EDGELEN (int): A length of edges of triangle on the nuclear perimeter.
THRES_ANGLE (int): Define the neck points if a triangle has more than this angle.
STEPLIM (int): points of neck needs to be separated by at least STEPLIM in parimeters.
"""
untr_prev, untr_curr = container.unlinked
label_untracked = container._label_untracked
unique_labels = np.unique(label_untracked)
unique_labels = unique_labels[unique_labels > 0]
newcells = []
all_new_cells = []
for label_id in unique_labels:
mask = label_untracked == label_id
cl_label = clear_border(mask)
outlines = labels2outlines(cl_label).astype(np.uint16)
rps = regionprops(outlines)
rps = [i for i in rps if i.perimeter > STEPLIM]
for cell in rps:
score, coords = calc_neck_score_thres_filtered(cell.coords,
edgelen=EDGELEN,
thres=THRES_ANGLE,
steplim=STEPLIM)
if len(score) > 1:
r0, c0 = coords[0, :]
if coords.shape[0] > LIM:
coords = coords[:LIM, :]
for cand in coords[1:, :]:
untr_prev = container.unlinked[0]
cut_label = skilabel(cut_neck(cl_label, r0, c0, cand[0],
cand[1]),
conn=1)
new_cells_temp = CellListMaker(img, cut_label, holder,
holder.frame).make_list()
if len(new_cells_temp) > 1:
distanceUntracked = _distance_diff(
untr_prev, new_cells_temp)
masschangeUntracked = _totalintensity_difference(
untr_prev, new_cells_temp)
withinarea = distanceUntracked < DISPLACEMENT
withinmass = abs(masschangeUntracked) < MASSTHRES
withinareaMass = withinarea * withinmass
withinareaMass = pick_closer_binarycostmat(
withinareaMass, distanceUntracked)
good_curr_idx, good_prev_idx = find_one_to_one_assign(
withinareaMass)
if len(good_curr_idx) > 0:
# update the link
all_new_cells.append(new_cells_temp)
for ci, pi in zip(good_curr_idx, good_prev_idx):
newcells.append(new_cells_temp[ci])
untr_prev[pi].next = new_cells_temp[ci]
break
good_curr_coords = [n.prop.coords for n in newcells]
lin_curr_coords = [
convert_coords_to_linear(i, holder.img_shape)
for i in flatlist(good_curr_coords)
]
# find cells in old mask (in current) that overlaps with good new cells
old_cells_to_remove, lin_old_coords_remove = find_coords_overlap_coords(
untr_curr, lin_curr_coords, holder.img_shape)
# find cells in new mask which overlaps with the cells in old mask
all_new_cells = [i for j in all_new_cells for i in j]
newcells_to_update = find_cells_overlap_linear_coords(
all_new_cells, lin_old_coords_remove, holder.img_shape)
# remove old cells
for old_cell in old_cells_to_remove:
container.curr_cells.remove(old_cell)
# add new cells
container.curr_cells.extend(newcells_to_update)
return container