In [1]:

from pathlib import Path
import utils
import trackmate_utils
import holoviews as hv
import panel as pn
pn.extension('tabulator')
hv.extension('bokeh')

from pathlib import Path import utils import trackmate_utils import holoviews as hv import panel as pn pn.extension('tabulator') hv.extension('bokeh')

1. Run Full Pipeline¶

Define data paths for data and configs. Using the utils.run_analysis() method we get a segmentation map and TrackMate results.

In [2]:

base_data_path = Path(r"data/fucci_60_frames")
red_stack_path = base_data_path / 'red.tif'
green_stack_path = base_data_path / 'green.tif'

base_model_path = Path(r"models")
red_segmentation_model = base_model_path / 'cellpose/nuclei_red_v2'
green_segmentation_model = base_model_path / 'cellpose/nuclei_green_v2'

trackmate_settings_xml = base_model_path / Path(r"trackmate/basic_settings.xml")

base_data_path = Path(r"data/fucci_60_frames") red_stack_path = base_data_path / 'red.tif' green_stack_path = base_data_path / 'green.tif' base_model_path = Path(r"models") red_segmentation_model = base_model_path / 'cellpose/nuclei_red_v2' green_segmentation_model = base_model_path / 'cellpose/nuclei_green_v2' trackmate_settings_xml = base_model_path / Path(r"trackmate/basic_settings.xml")

In [3]:

# utils.run_pipeline(red_stack_path, red_segmentation_model, trackmate_settings_xml)

# utils.run_pipeline(red_stack_path, red_segmentation_model, trackmate_settings_xml)

In [4]:

# utils.run_pipeline(green_stack_path, green_segmentation_model, trackmate_settings_xml)

# utils.run_pipeline(green_stack_path, green_segmentation_model, trackmate_settings_xml)

2. Measure Flouresence Over Time¶

Add plot

In [5]:

%load_ext autoreload
%autoreload 2

%load_ext autoreload %autoreload 2

In [6]:

red_segmentation_map = utils.read_stack(base_data_path / 'red_segmented.tiff')
tm_red = trackmate_utils.TrackmateXML(base_data_path / 'red_segmented.tiff.xml')
red_stack = utils.read_stack(base_data_path / 'red.tif')
green_stack = utils.read_stack(base_data_path / 'green.tif')

import trackmate_utils
track = tm_red.trace_track(0)
trackmate_utils.draw_fucci_measurement(track, segmentation_map=red_segmentation_map, red_stack=red_stack, green_stack=green_stack)

red_segmentation_map = utils.read_stack(base_data_path / 'red_segmented.tiff') tm_red = trackmate_utils.TrackmateXML(base_data_path / 'red_segmented.tiff.xml') red_stack = utils.read_stack(base_data_path / 'red.tif') green_stack = utils.read_stack(base_data_path / 'green.tif') import trackmate_utils track = tm_red.trace_track(0) trackmate_utils.draw_fucci_measurement(track, segmentation_map=red_segmentation_map, red_stack=red_stack, green_stack=green_stack)

Out[6]:

In [7]:

trackmate_utils.view_track([red_stack, green_stack], frame=54, track=track, zoom=True).cols(2)

trackmate_utils.view_track([red_stack, green_stack], frame=54, track=track, zoom=True).cols(2)

Out[7]:

3. Identify Matching Tracks¶

As S phase progresses, contrast on red channel decreases while increasing in the green channel. These contrast dynamics bleed into the segmentation and vary the size of a segment. As such, the S phase, during which segmentation on both channels is possible, provides an option to both merge tracks from each channel and getting a better estimate of the size of the nucleus.

For a given pair of tracks $R$, $G$ that overlap for $t_{overlap}$ we can define a Cartesian similarity metric of the form: $$S=\frac{\sum_{t_{overlap,min}}^{t_{overlap,max}}\sqrt{[R_{X}(t)-G_{X}(t)]^{2}+[R_{Y}(t)-G_{Y}(t)]^{2}}}{t_{overlap,max}-t_{overlap,min}}$$ Where $$min(t_{R})<=t_{overlap}<=max(t_{G})$$ Which estimates the average deviation between the two tracks in units of $\frac{pixel}{frame}$

In [8]:

green_segmentation_map = utils.read_stack(base_data_path / 'green_segmented.tiff')
tm_green = trackmate_utils.TrackmateXML(base_data_path / 'green_segmented.tiff.xml')

red_track = tm_red.trace_track(0)
green_track = tm_green.trace_track(0)

green_segmentation_map = utils.read_stack(base_data_path / 'green_segmented.tiff') tm_green = trackmate_utils.TrackmateXML(base_data_path / 'green_segmented.tiff.xml') red_track = tm_red.trace_track(0) green_track = tm_green.trace_track(0)

Let's look at candidate tracks red-0 and green-0:

In [9]:

red_track.head()

red_track.head()

Out[9]:

	ID	frame	POSITION_X	POSITION_Y	PERIMETER	image_id	AREA	ROI	roi_polygon	ELLIPSE_MAJOR	ELLIPSE_MINOR	mean_red	std_red	mean_green	std_green	low_red	high_red	low_green	high_green
0	856	0	247.581341	293.123523	95.61413819380628	41	606.5	[(5.918658972245112, 18.376477054135762), (8.9...	POLYGON ((254 312, 257 307, 258 305, 259 299, ...	17.470730857452068	11.618046610171142	0.010738	0.104057	0.077642	0.076447	-0.093318	0.114795	0.001195	0.154089
1	894	1	262.158762	285.241765	105.93964862074475	38	759.0	[(6.34123847167325, 18.258234519104064), (8.34...	POLYGON ((269 304, 271 303, 271 301, 274 298, ...	18.674833874323685	13.922507990542325	0.000000	0.112771	0.043456	0.080206	-0.112771	0.112771	-0.036750	0.123662
2	518	2	275.38465	265.978358	104.46013970824129	34	747.0	[(7.1153502900490935, 14.521642124051766), (9....	POLYGON ((283 281, 285 280, 286 278, 288 278, ...	17.035068889833056	14.472832948601143	0.001430	0.118055	0.000000	0.077172	-0.116626	0.119485	-0.077172	0.077172
3	401	3	283.703855	262.742857	104.04837559547113	31	735.0	[(7.796145124716531, 13.757142857142867), (12....	POLYGON ((292 277, 297 274, 299 273, 300 271, ...	17.695714184876877	13.549831667188052	0.059190	0.143261	0.052387	0.079176	-0.084072	0.202451	-0.026789	0.131563
4	517	4	288.930523	260.915313	100.04485365944433	40	646.5	[(8.569476669244636, 11.584686774941986), (13....	POLYGON ((298 273, 303 270, 305 269, 306 267, ...	18.732363212188634	11.12487641414746	0.145866	0.128231	0.098255	0.074736	0.017635	0.274098	0.023519	0.172990

In [10]:

green_track.head()

green_track.head()

Out[10]:

	ID	frame	POSITION_X	POSITION_Y	PERIMETER	image_id	AREA	ROI	roi_polygon	ELLIPSE_MAJOR	ELLIPSE_MINOR
0	247	5	263.440317	291.522857	88.41281378798725	10	525.0	[(11.05968253968257, 11.977142857142837), (12....	POLYGON ((275 304, 276 302, 278 301, 278 299, ...	14.998985013007266	11.790381737370799
1	166	6	272.387504	287.83557	87.00580543253196	10	521.5	[(9.112496005113428, 11.664429530201346), (11....	POLYGON ((282 300, 284 299, 286 296, 287 294, ...	14.692405636710518	11.773727659511218
2	17	7	280.07243	287.447819	94.78718006414792	10	642.0	[(6.427570093457916, 14.05218068535828), (10.4...	POLYGON ((287 302, 291 298, 293 297, 294 295, ...	15.405565107375924	13.793580452722283
3	288	8	286.350168	286.534632	99.54663927460682	10	693.0	[(7.14983164983164, 12.965367965367989), (9.14...	POLYGON ((294 300, 296 299, 297 297, 299 297, ...	17.23530044573699	13.18728497136877
4	389	9	288.332898	284.215622	105.08835412502395	10	766.0	[(5.16710182767622, 13.284377719756321), (7.16...	POLYGON ((294 298, 296 297, 298 297, 300 297, ...	19.236280991492926	13.179491125152014

The frame column indicates the tracks are overlapping temporally. Let's see if they overlap spatially:

In [11]:

trackmate_utils.view_red_green_track([red_stack, green_stack], red_track, green_track, frame=8)

trackmate_utils.view_red_green_track([red_stack, green_stack], red_track, green_track, frame=8)

Out[11]:

As seen above, the tracks intersect, but do not follow a similar path. It's also clear that at frame 8, the cell location does not overlap. As indicated above, we can calculate a similarity metric for these two tracks:

In [12]:

metric = trackmate_utils.CartesianSimilarity(tm_red, tm_green)
metric.calculate_metric(red_track_id=0, green_track_id=0)

metric = trackmate_utils.CartesianSimilarity(tm_red, tm_green) metric.calculate_metric(red_track_id=0, green_track_id=0)

Out[12]:

53.954147393090814

Similarly, we can calculate a metric for all possible track pairs:

In [13]:

metric_df = metric.calculate_metric_for_all_tracks()
metric_df.sort_values('metric', ascending=True).head()

metric_df = metric.calculate_metric_for_all_tracks() metric_df.sort_values('metric', ascending=True).head()

Calculating similarity metric: 100%|██████████| 5184/5184 [00:15<00:00, 340.06it/s]

Out[13]:

	red_track	green_track	metric
0	32	40	1.018302
1	19	9	1.101308
2	10	53	1.123107
3	31	1	1.134302
4	29	11	1.153568

Based on the table above, red track 32 and green track 40 should match up pretty well:

In [14]:

red_track = tm_red.trace_track(32)
green_track = tm_green.trace_track(40)
trackmate_utils.view_red_green_track([red_stack, green_stack], red_track, green_track)

red_track = tm_red.trace_track(32) green_track = tm_green.trace_track(40) trackmate_utils.view_red_green_track([red_stack, green_stack], red_track, green_track)

Out[14]:

In [15]:

metric_df.query('metric < 10').metric.hvplot.hist()

metric_df.query('metric < 10').metric.hvplot.hist()

Out[15]:

In [16]:

metric_df.query('2 < metric < 10')

metric_df.query('2 < metric < 10')

Out[16]:

	red_track	green_track	metric
29	30	21	2.004808
30	3	52	2.027253
31	22	59	2.470495
32	15	66	2.984784
33	55	63	3.131836
34	43	19	6.059927

In [17]:

# red_track = tm_red.trace_track(43)
# green_track = tm_green.trace_track(19)
red_track = tm_red.trace_track(30)
green_track = tm_green.trace_track(21)
trackmate_utils.view_red_green_track([red_stack, green_stack], red_track, green_track, frame=42)

# red_track = tm_red.trace_track(43) # green_track = tm_green.trace_track(19) red_track = tm_red.trace_track(30) green_track = tm_green.trace_track(21) trackmate_utils.view_red_green_track([red_stack, green_stack], red_track, green_track, frame=42)

Out[17]:

4. Measure Merged Tracks¶

Colorful plot showing red/yellow/green tracks and a flouresence time plot\

In [18]:

df = metric.merge_tracks(red_track_id=30, green_track_id=21)

df = metric.merge_tracks(red_track_id=30, green_track_id=21)

In [19]:

df.sort_values('frame')

df.sort_values('frame')

Out[19]:

	ID	frame	POSITION_X	POSITION_Y	PERIMETER	image_id	AREA	ROI	roi_polygon	ELLIPSE_MAJOR	ELLIPSE_MINOR	color	source_track
0	718	0	145.030227	163.693012	125.33054812633623	22	901.5	[(11.4697726012202, 11.806988352745435), (13.4...	POLYGON ((157 176, 159 175, 163 175, 164 174, ...	26.299283446267776	11.48226335338557	red	red
1	741	1	149.173954	168.741993	128.99426723294698	20	983.5	[(20.326046432808, 12.758007117437728), (21.32...	POLYGON ((170 182, 171 180, 171 178, 173 177, ...	27.0710084671051	11.769487268328414	red	red
2	492	2	152.348485	171.77013	130.94655166861943	21	962.5	[(17.151515151515156, 13.729870129870136), (18...	POLYGON ((170 186, 171 184, 173 184, 174 182, ...	26.91498053377485	11.757541165177717	red	red
3	390	3	156.796843	176.663781	129.06424194635238	20	982.0	[(15.703156822810598, 14.836218601493528), (22...	POLYGON ((173 192, 180 188, 181 186, 183 183, ...	26.35417119999956	11.903600703556869	red	red
4	443	4	169.444444	177.348399	131.4679621358243	26	1062.0	[(16.055555555555543, 16.151600753295668), (19...	POLYGON ((186 194, 189 192, 191 191, 194 186, ...	26.806163063017053	13.124673158309058	red	red
5	338	5	181.742109	173.718119	131.36082021898807	25	1056.0	[(15.757891414141426, 17.781881313131294), (17...	POLYGON ((198 192, 200 191, 202 188, 204 187, ...	25.84557839311181	13.381886075280141	red	red
6	1585	6	194.315309	170.128553	130.98605948579228	26	1049.5	[(8.184691122756874, 20.37144672066063), (10.1...	POLYGON ((203 191, 205 190, 207 190, 209 190, ...	25.092830921966282	13.903626146657459	red	red
7	763	7	206.218106	167.849004	123.60216180843078	28	978.5	[(14.281894055527175, 18.650996423096586), (18...	POLYGON ((221 187, 225 183, 226 181, 226 175, ...	24.482642026064465	13.379202097509504	red	red
8	2101	8	214.156015	164.848577	120.86521463430552	27	907.5	[(7.343985307621665, 19.651423324150585), (10....	POLYGON ((222 185, 225 183, 227 182, 229 182, ...	23.664448506950603	12.433145032904472	red	red
9	731	9	220.131111	167.885185	121.22565986229465	25	900.0	[(10.368888888888875, 18.61481481481482), (13....	POLYGON ((231 187, 234 185, 236 184, 239 179, ...	23.108214931082493	12.781370643871394	red	red
10	944	10	225.79107	172.066755	123.95114441827712	29	942.5	[(11.708930150309442, 19.43324491600356), (15....	POLYGON ((238 192, 242 188, 244 187, 245 183, ...	24.15816248847182	12.685061884072159	red	red
11	960	11	232.071949	171.887614	123.61291164546542	25	915.0	[(7.428051001821473, 20.612386156648455), (9.4...	POLYGON ((240 193, 242 192, 244 192, 245 191, ...	23.729931824392796	12.428093187422572	red	red
12	1101	12	233.151844	168.834816	118.24712223146368	25	899.5	[(8.34815638317582, 19.665184361682435), (13.3...	POLYGON ((242 189, 247 186, 249 185, 250 181, ...	23.096285858644507	12.866044729139361	red	red
13	1247	13	235.302352	160.544058	126.27382070233604	23	949.5	[(10.197647884851676, 19.955941723714233), (12...	POLYGON ((246 181, 248 180, 249 179, 252 176, ...	24.15249115417699	12.627237489354933	red	red
14	1032	14	238.531952	152.851473	128.5628046122555	23	1001.5	[(11.968047928107836, 18.648527209186227), (13...	POLYGON ((251 172, 253 171, 255 171, 259 167, ...	25.38667506538604	12.895847321307675	red	red
15	127	15	240.522328	148.489549	132.82193283195625	21	1052.5	[(13.977672209026139, 19.0104513064133), (14.9...	POLYGON ((255 168, 256 166, 258 166, 260 165, ...	25.563152203774727	13.072850182631	red	red
16	1237	16	241.973095	141.511344	141.2619136665493	21	1146.0	[(13.526905177428716, 19.988656195462482), (15...	POLYGON ((256 162, 258 161, 260 161, 264 157, ...	28.19661504505331	13.360532462155327	red	red
17	1226	17	248.769481	136.605597	136.82378353388197	22	1078.0	[(16.73051948051949, 18.894403215831773), (18....	POLYGON ((266 156, 268 155, 269 154, 272 149, ...	27.9071087451404	12.649391882194358	red	red
18	891	18	249.824427	131.430821	134.0791209031914	19	1048.0	[(14.675572519083971, 19.06917938931298), (17....	POLYGON ((265 151, 268 149, 270 148, 272 144, ...	27.296024106779782	12.271062423129175	red	red
19	54	19	250.445381	127.749376	135.48573019755784	22	1068.0	[(17.054619225967514, 17.75062421972534), (19....	POLYGON ((268 146, 270 145, 272 142, 274 141, ...	28.00559225619811	12.351566673187254	red	red
20	1051	20	254.497906	125.766338	136.086419052908	23	1114.5	[(14.002093614475825, 19.733662329893818), (16...	POLYGON ((269 146, 271 145, 272 144, 275 141, ...	27.76621408053037	12.960925284817689	red	red
21	142	21	256.189006	126.409906	137.79744455399282	22	1134.0	[(17.310993533215765, 19.090094062316282), (19...	POLYGON ((274 146, 276 145, 276 143, 278 142, ...	27.877599504419102	13.293614436060066	red	red
22	141	22	258.955238	126.993333	141.5390818709381	23	1225.0	[(15.544761904761913, 19.50666666666666), (18....	POLYGON ((275 147, 278 145, 280 144, 282 140, ...	28.04285428292613	14.164202448222582	red	red
23	1144	23	261.845357	128.93807	136.8748279180853	21	1150.5	[(15.654642908880191, 17.561929595827905), (17...	POLYGON ((278 147, 280 146, 281 145, 284 142, ...	26.73983563215506	14.019659805100623	red	red
24	1608	24	259.566951	126.989837	136.812267298496	21	1172.5	[(14.933049040511719, 17.51016346837241), (19....	POLYGON ((275 145, 280 142, 282 141, 283 139, ...	27.122272161881092	14.285077935245377	red	red
27	1615	25	266.878741	125.806623	135.1169069292797	21	1141.5	[(16.621258577894594, 16.404730617608408), (18...	POLYGON ((284 142, 286 141, 287 140, 290 137, ...	26.83942671707665	13.835804507770662	yellow	red
28	1799	26	271.636779	115.25946	140.48433766379102	20	1132.0	[(19.863221436984702, 16.462897526501763), (21...	POLYGON ((292 132, 294 131, 296 131, 296 129, ...	29.268474203297547	12.604463541927396	yellow	red
29	1932	27	284.954156	110.713004	142.9881729591448	19	1127.0	[(19.545844424726454, 17.251848565513157), (20...	POLYGON ((305 128, 306 126, 308 126, 312 119, ...	30.22263116758034	12.398260790127539	yellow	red
30	2042	28	292.859839	103.279019	144.94307400523576	18	1201.0	[(15.640160976963614, 18.862614487926734), (20...	POLYGON ((309 122, 314 119, 316 118, 316 116, ...	28.72953234164841	13.60065841044609	yellow	red
31	1315	29	294.923801	87.094489	140.6249006881293	16	1133.0	[(18.57619888202413, 18.987202118270076), (20....	POLYGON ((314 106, 316 105, 316 103, 317 101, ...	28.06173377761514	13.060552691169141	yellow	red
32	2325	30	293.779073	74.822738	139.99034726589903	13	1136.5	[(17.720926822114677, 18.10881360903359), (19....	POLYGON ((312 93, 314 92, 315 91, 316 89, 318 ...	29.455371086094015	12.489612085154226	yellow	red
33	18	31	299.536421	67.359804	139.11984777958693	12	1112.0	[(14.963579136690669, 17.547362110311752), (15...	POLYGON ((315 85, 316 83, 318 83, 319 81, 321 ...	27.60148830311041	13.104101910283019	yellow	red
34	554	32	301.627546	59.920107	134.74052824587886	10	1088.5	[(13.872454448017152, 16.780278670953912), (15...	POLYGON ((316 77, 318 76, 320 76, 321 74, 322 ...	26.572024402815025	13.098016190049485	yellow	red
35	454	33	304.127693	53.814788	133.6679912674727	10	1013.5	[(15.37230718631804, 16.051307350764674), (16....	POLYGON ((320 70, 321 68, 323 68, 324 66, 326 ...	26.469694692071474	12.458949900894075	yellow	red
36	444	34	309.374932	53.813522	127.3604291992227	5	919.5	[(15.125067971723752, 15.4205183976799), (18.1...	POLYGON ((325 70, 328 67, 330 67, 331 65, 333 ...	26.000167015793096	11.440255231463105	yellow	green
37	637	35	308.934026	53.79858	129.91010582746156	5	890.5	[(16.565974171813593, 14.505334081976414), (17...	POLYGON ((326 69, 327 67, 329 67, 331 66, 332 ...	27.5980621754936	10.387840098881021	yellow	green
38	591	36	310.106737	51.838931	132.04286187524482	6	851.0	[(18.393262828045465, 13.527614571092833), (20...	POLYGON ((329 66, 331 65, 331 63, 333 62, 334 ...	27.655478106308475	9.688110840495888	yellow	green
39	671	37	307.409933	50.207912	122.2625311414868	7	792.0	[(17.090067340067378, 11.292087542087543), (22...	POLYGON ((325 62, 330 59, 331 57, 331 51, 331 ...	26.997829194646965	9.844547128835986	yellow	green
40	720	38	307.700038	51.625051	128.3373467592068	7	876.5	[(15.799961969956314, 12.932876972808515), (20...	POLYGON ((324 65, 329 62, 331 61, 332 57, 333 ...	27.45433104761784	10.351862959314394	yellow	green
41	534	39	305.75884	52.343755	127.5163814176686	6	839.0	[(17.741160111243573, 11.981922924116013), (19...	POLYGON ((324 65, 326 64, 326 62, 328 61, 329 ...	26.92581219921457	10.411247271377048	yellow	green
42	706	40	307.257904	51.633318	130.85965406226356	7	922.5	[(19.242095754290858, 10.84878048780488), (22....	POLYGON ((327 63, 330 61, 332 60, 334 54, 334 ...	27.313279390612177	11.13093992039244	yellow	green
43	499	41	312.977087	49.130937	121.44125444160726	6	796.5	[(16.52291274325171, 11.262816488805193), (20....	POLYGON ((330 61, 334 57, 336 56, 336 52, 337 ...	24.798528612278783	10.337600433691717	yellow	green
25	753	42	301.775593	42.119199	99.02562265223223	6	470.5	[(-2.27559334041797, 18.38080056677294), (2.72...	POLYGON ((300 61, 305 52, 306 51, 306 49, 306 ...	19.138897317023822	7.609520524225114	green	green
26	562	43	300.408819	35.671898	59.64942284741085	6	223.0	[(2.091180866965601, 9.828101644245145), (3.09...	POLYGON ((303 46, 304 44, 306 43, 306 41, 306 ...	10.560586044446321	7.031626777227055	green	green

In [20]:

red_track.hvplot.line(x='frame', y='AREA').opts(color='red') * green_track.hvplot.line(x='frame', y='AREA').opts(color='green')

red_track.hvplot.line(x='frame', y='AREA').opts(color='red') * green_track.hvplot.line(x='frame', y='AREA').opts(color='green')

Out[20]:

In [21]:

df.sort_values('frame').hvplot.line(x='frame', y='AREA')

df.sort_values('frame').hvplot.line(x='frame', y='AREA')

Out[21]:

In [22]:

trackmate_utils.draw_fucci_measurement_merged_track(df.sort_values('frame'), red_segmentation_map=red_segmentation_map, green_segmentation_map=green_segmentation_map, red_stack=red_stack, green_stack=green_stack)

trackmate_utils.draw_fucci_measurement_merged_track(df.sort_values('frame'), red_segmentation_map=red_segmentation_map, green_segmentation_map=green_segmentation_map, red_stack=red_stack, green_stack=green_stack)

Out[22]:

In [23]:

metric_df.query('red_track == 0').sort_values('metric')

metric_df.query('red_track == 0').sort_values('metric')

Out[23]:

	red_track	green_track	metric
8	0	41	1.218965
100	0	0	53.954147
133	0	51	59.848973
179	0	54	69.392551
188	0	23	71.639376
...	...	...	...
4081	0	70	inf
4071	0	13	inf
4070	0	12	inf
4074	0	26	inf
4092	0	71	inf

72 rows × 3 columns

In [24]:

red_track = tm_red.trace_track(0)
green_track = tm_green.trace_track(41)
(trackmate_utils.draw_fucci_measurement(red_track, segmentation_map=red_segmentation_map, red_stack=red_stack, green_stack=green_stack) + trackmate_utils.draw_fucci_measurement(green_track, segmentation_map=green_segmentation_map, red_stack=red_stack, green_stack=green_stack)).cols(1)

red_track = tm_red.trace_track(0) green_track = tm_green.trace_track(41) (trackmate_utils.draw_fucci_measurement(red_track, segmentation_map=red_segmentation_map, red_stack=red_stack, green_stack=green_stack) + trackmate_utils.draw_fucci_measurement(green_track, segmentation_map=green_segmentation_map, red_stack=red_stack, green_stack=green_stack)).cols(1)

Out[24]:

In [25]:

merged_track = metric.merge_tracks(red_track_id=0, green_track_id=41).sort_values('frame')
trackmate_utils.draw_fucci_measurement_merged_track(merged_track, red_segmentation_map=red_segmentation_map,green_segmentation_map=green_segmentation_map, red_stack=red_stack, green_stack=green_stack)

merged_track = metric.merge_tracks(red_track_id=0, green_track_id=41).sort_values('frame') trackmate_utils.draw_fucci_measurement_merged_track(merged_track, red_segmentation_map=red_segmentation_map,green_segmentation_map=green_segmentation_map, red_stack=red_stack, green_stack=green_stack)

Out[25]:

In [25]: