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Labooratory AI
FLASH
Commits
626d7db3
Commit
626d7db3
authored
3 years ago
by
Nassim Bouteldja
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626d7db3
# this file implements the postprocessing of prediction results (tubule dilation, hole filling, small area removal, instance extration, ...)
import
numpy
as
np
import
torch
import
torch.nn
as
nn
import
math
from
scipy.ndimage.measurements
import
label
from
scipy.ndimage.morphology
import
binary_dilation
,
binary_fill_holes
from
utils
import
getChannelSmootingConvLayer
structure
=
np
.
zeros
((
3
,
3
),
dtype
=
np
.
int
)
structure
[
1
,
:]
=
1
structure
[:,
1
]
=
1
# selected colors for label/class visualization
colors
=
np
.
array
([
[
0
,
0
,
0
],
# Black
[
255
,
0
,
0
],
# Red
[
0
,
128
,
0
],
# Green
[
0
,
0
,
255
],
# Blue
[
0
,
255
,
255
],
# Cyan
[
255
,
0
,
255
],
# Magenta
[
255
,
255
,
0
],
# Yellow
[
139
,
69
,
19
],
# Brown (saddlebrown)
[
128
,
0
,
128
],
# Purple
[
255
,
140
,
0
],
# Orange
[
255
,
255
,
255
]],
dtype
=
np
.
uint8
)
# White
# get random color for tubules instances that is not too similar to colors of other classes
def
getRandomTubuliColor
():
while
(
True
):
candidateColor
=
np
.
random
.
randint
(
low
=
0
,
high
=
256
,
size
=
3
,
dtype
=
np
.
uint8
)
if
not
((
np
.
abs
((
candidateColor
-
colors
[
0
:
7
])).
sum
(
1
)
<
50
).
any
()):
return
candidateColor
# this method gets postprocessed prediction results as well as the ground-truth label map and extract all instance channels of each
# label for further performance computation as well as instance visualization, and further applies the last postprocessing step of tubules dilation
# yielding final (instance) results
def
extractInstanceChannels
(
postprocessedPrediction
,
preprocessedGT
,
tubuliDilation
=
True
):
postprocessedPredictionRGB
=
np
.
zeros
(
shape
=
(
preprocessedGT
.
shape
[
0
],
preprocessedGT
.
shape
[
1
],
3
),
dtype
=
np
.
uint8
)
preprocessedGTrgb
=
postprocessedPredictionRGB
.
copy
()
for
i
in
range
(
2
,
7
):
postprocessedPredictionRGB
[
postprocessedPrediction
==
i
]
=
colors
[
i
]
preprocessedGTrgb
[
preprocessedGT
==
i
]
=
colors
[
i
]
labeledTubuli
,
numberTubuli
=
label
(
np
.
asarray
(
postprocessedPrediction
==
1
,
np
.
uint8
),
structure
)
labeledGlom
,
_
=
label
(
np
.
asarray
(
np
.
logical_or
(
postprocessedPrediction
==
2
,
postprocessedPrediction
==
3
),
np
.
uint8
),
structure
)
labeledTuft
,
_
=
label
(
np
.
asarray
(
postprocessedPrediction
==
3
,
np
.
uint8
),
structure
)
labeledVeins
,
_
=
label
(
np
.
asarray
(
postprocessedPrediction
==
4
,
np
.
uint8
),
structure
)
labeledArtery
,
_
=
label
(
np
.
asarray
(
np
.
logical_or
(
postprocessedPrediction
==
5
,
postprocessedPrediction
==
6
),
np
.
uint8
),
structure
)
labeledArteryLumen
,
_
=
label
(
np
.
asarray
(
postprocessedPrediction
==
6
,
np
.
uint8
),
structure
)
for
i
in
range
(
1
,
numberTubuli
+
1
):
if
tubuliDilation
:
tubuliSelection
=
binary_dilation
(
labeledTubuli
==
i
)
labeledTubuli
[
tubuliSelection
]
=
i
else
:
tubuliSelection
=
labeledTubuli
==
i
postprocessedPredictionRGB
[
tubuliSelection
]
=
getRandomTubuliColor
()
labeledTubuliGT
,
numberTubuliGT
=
label
(
np
.
asarray
(
preprocessedGT
==
1
,
np
.
uint8
),
structure
)
labeledGlomGT
,
_
=
label
(
np
.
asarray
(
np
.
logical_or
(
preprocessedGT
==
2
,
preprocessedGT
==
3
),
np
.
uint8
),
structure
)
labeledTuftGT
,
_
=
label
(
np
.
asarray
(
preprocessedGT
==
3
,
np
.
uint8
),
structure
)
labeledVeinsGT
,
_
=
label
(
np
.
asarray
(
preprocessedGT
==
4
,
np
.
uint8
),
structure
)
labeledArteryGT
,
_
=
label
(
np
.
asarray
(
np
.
logical_or
(
preprocessedGT
==
5
,
preprocessedGT
==
6
),
np
.
uint8
),
structure
)
labeledArteryLumenGT
,
_
=
label
(
np
.
asarray
(
preprocessedGT
==
6
,
np
.
uint8
),
structure
)
for
i
in
range
(
1
,
numberTubuliGT
+
1
):
tubuliSelectionGT
=
labeledTubuliGT
==
i
preprocessedGTrgb
[
tubuliSelectionGT
]
=
getRandomTubuliColor
()
return
[
labeledTubuli
,
labeledGlom
,
labeledTuft
,
labeledVeins
,
labeledArtery
,
labeledArteryLumen
],
[
labeledTubuliGT
,
labeledGlomGT
,
labeledTuftGT
,
labeledVeinsGT
,
labeledArteryGT
,
labeledArteryLumenGT
],
postprocessedPredictionRGB
,
preprocessedGTrgb
def
postprocessPredictionAndGT
(
prediction
,
GT
,
device
,
predictionsmoothing
,
holefilling
):
"""
:param prediction: Torch FloatTensor of size 1xCxHxW stored in VRAM/on GPU
:param GT: HxW ground-truth label map, numpy long tensor
:return: 1.postprocessed labelmap result (prediction smoothing, removal of small areas, hole filling)
2.network output prediction (w/o postprocessing)
"""
################# PREDICTION SMOOTHING ################
if
predictionsmoothing
:
smoothingKernel
=
getChannelSmootingConvLayer
(
8
).
to
(
device
)
prediction
=
smoothingKernel
(
prediction
)
# labelMap contains following labels: 0/1/2/3/4/5/6/7 => Background/tubuli/glom_full/glom_tuft/veins/artery_full/artery_lumen/border
labelMap
=
torch
.
argmax
(
prediction
,
dim
=
1
).
squeeze
(
0
).
to
(
"
cpu
"
).
numpy
()
# Label 0/1/2/3/4/5/6/7: Background/tubuli/glom_full/glom_tuft/veins/artery_full/artery_lumen/border
netOutputPrediction
=
labelMap
.
copy
()
################# REMOVING TOO SMALL CONNECTED REGIONS ################
# Tuft
labeledTubuli
,
numberTubuli
=
label
(
np
.
asarray
(
labelMap
==
3
,
np
.
uint8
),
structure
)
# datatype of 'labeledTubuli': int32
for
i
in
range
(
1
,
numberTubuli
+
1
):
tubuliSelection
=
(
labeledTubuli
==
i
)
if
tubuliSelection
.
sum
()
<
500
:
# remove too small noisy regions
labelMap
[
tubuliSelection
]
=
2
# Glomeruli
labeledTubuli
,
numberTubuli
=
label
(
np
.
asarray
(
np
.
logical_or
(
labelMap
==
3
,
labelMap
==
2
),
np
.
uint8
),
structure
)
# datatype of 'labeledTubuli': int32
for
i
in
range
(
1
,
numberTubuli
+
1
):
tubuliSelection
=
(
labeledTubuli
==
i
)
if
tubuliSelection
.
sum
()
<
1500
:
# remove too small noisy regions
labelMap
[
tubuliSelection
]
=
0
# Artery lumen
labeledTubuli
,
numberTubuli
=
label
(
np
.
asarray
(
labelMap
==
6
,
np
.
uint8
),
structure
)
# datatype of 'labeledTubuli': int32
for
i
in
range
(
1
,
numberTubuli
+
1
):
tubuliSelection
=
(
labeledTubuli
==
i
)
if
tubuliSelection
.
sum
()
<
20
:
# remove too small noisy regions
labelMap
[
tubuliSelection
]
=
5
# Full artery
labeledTubuli
,
numberTubuli
=
label
(
np
.
asarray
(
np
.
logical_or
(
labelMap
==
5
,
labelMap
==
6
),
np
.
uint8
),
structure
)
# datatype of 'labeledTubuli': int32
for
i
in
range
(
1
,
numberTubuli
+
1
):
tubuliSelection
=
(
labeledTubuli
==
i
)
if
tubuliSelection
.
sum
()
<
400
:
# remove too small noisy regions
labelMap
[
tubuliSelection
]
=
0
# Veins
labeledTubuli
,
numberTubuli
=
label
(
np
.
asarray
(
labelMap
==
4
,
np
.
uint8
),
structure
)
# datatype of 'labeledTubuli': int32
for
i
in
range
(
1
,
numberTubuli
+
1
):
tubuliSelection
=
(
labeledTubuli
==
i
)
if
tubuliSelection
.
sum
()
<
3000
:
# remove too small noisy regions
labelMap
[
tubuliSelection
]
=
0
# Tubuli
labeledTubuli
,
numberTubuli
=
label
(
np
.
asarray
(
labelMap
==
1
,
np
.
uint8
),
structure
)
# datatype of 'labeledTubuli': int32
for
i
in
range
(
1
,
numberTubuli
+
1
):
tubuliSelection
=
(
labeledTubuli
==
i
)
if
tubuliSelection
.
sum
()
<
400
:
# remove too small noisy regions
labelMap
[
tubuliSelection
]
=
0
################# HOLE FILLING ################
if
holefilling
:
labelMap
[
binary_fill_holes
(
labelMap
==
1
)]
=
1
#tubuli
labelMap
[
binary_fill_holes
(
labelMap
==
4
)]
=
4
#veins
tempTuftMask
=
binary_fill_holes
(
labelMap
==
3
)
#tuft
labelMap
[
binary_fill_holes
(
np
.
logical_or
(
labelMap
==
3
,
labelMap
==
2
))]
=
2
#glom
labelMap
[
tempTuftMask
]
=
3
#tuft
tempArteryLumenMask
=
binary_fill_holes
(
labelMap
==
6
)
#artery_lumen
labelMap
[
binary_fill_holes
(
np
.
logical_or
(
labelMap
==
5
,
labelMap
==
6
))]
=
5
#full_artery
labelMap
[
tempArteryLumenMask
]
=
6
#artery_lumen
return
labelMap
,
netOutputPrediction
,
GT
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