Tracking by overlaps

This example illustrates the usage of OverLapTrack API by tracking segmented cells by their overlaps.

%pip install -q --upgrade -r requirements.txt

Importing packages

laptrack.OverLapTrack is an extension of laptrack.LapTrack, which can be used for tracking labeled objects by overlaps.

We also import regionprops_table from skimage to calculate centroids of the segmentation masks.

import napari
from skimage.measure import regionprops_table
from matplotlib import pyplot as plt
from itertools import product
import numpy as np
import pandas as pd
from laptrack import OverLapTrack
from laptrack import datasets
from laptrack.data_conversion import convert_split_merge_df_to_napari_graph

Loading data

Loading segmentation data and show it in the viewer.

Note: this data is generated by cropping segmentation.npy in https://github.com/NoneqPhysLivingMatterLab/cell_interaction_gnn .

labels = datasets.mouse_epidermis()

viewer = napari.Viewer()
viewer.add_labels(labels)

<Labels layer 'labels' at 0x7fb241233c10>

Tracking

Tracking by overlap

OverLapTrack API can be used instead of LapTrack to track integer-labeled objects in a image. Note that predict_overlap_dataframe is used instead predict or predict_dataframe, which takes labeled images as the input instead of the coordinates.

For labeled regions with the label label1 and label2 at the frame frame1 < frame2, respectively, the cost function is calculated using four quantities:

• overlap : the area of the intersection of the labeled regions

• iou : overlap over the area of the union of the labeled regions

• ratio_1 : overlap over the area of the label1 region

• ratio_2 : overlap over the area of the label2 region.

For each connection (tracking, gap_closing, splitting and merging), the cost is calculated by:

offset + overlap_coef * overlap + iou_coef * iou + ratio_1_coef * ratio_1 + ratio_2_coef * ratio_2

The coefficients can be specified by a tuple of 5 floats of (offset, overlap_coef, iou_coef, ratio_1_coef, ratio_2_coef). For example, to use the 1-overlap for first tracking distance and 1-ratio_2 for splitting distance, one can write as follows:

OverLapTrack(
    cutoff=0.9,
    metric_coefs = (1.0, -1.0, 0.0, 0.0, 0.0),
    gap_closing_metric_coefs = (1.0, -1.0, 0.0, 0.0, 0.0),
    gap_closing_max_frame_count=1,
    splitting_cutoff=0.9,
    splitting_metric_coefs = (1.0, 0.0, 0.0, 0.0, -1.0),
)

olt = OverLapTrack(
    cutoff=0.9,
    metric_coefs=(1.0, -1.0, 0.0, 0.0, 0.0),
    gap_closing_metric_coefs=(1.0, -1.0, 0.0, 0.0, 0.0),
    gap_closing_max_frame_count=1,
    splitting_cutoff=0.9,
    splitting_metric_coefs=(1.0, 0.0, 0.0, 0.0, -1.0),
)
track_df, split_df, merge_df = olt.predict_overlap_dataframe(labels)

display(track_df.head())
display(split_df.head())

		tree_id	track_id
frame	label
0	1	0	0
	2	1	1
	3	2	2
	4	3	3
	5	4	4

	parent_track_id	child_track_id
0	11	134
1	11	132
2	15	135
3	15	133
4	42	138

Showing clonal cells by the same colors

To check the result, we show the clonal cell regions by the same colors.

new_labels = np.zeros_like(labels)
for tree_id, grp in track_df.reset_index().groupby("tree_id"):
    for _, row in grp.iterrows():
        frame = int(row["frame"])
        label = int(row["label"])
        new_labels[frame][labels[frame] == label] = tree_id + 1

viewer.layers["labels"].visible = False
viewer.add_labels(new_labels)

<Labels layer 'new_labels' at 0x7fb250d453c0>

Adding tracks connecting the centroids

Create coordinate dataframe

Make the coordinate dataframe including the centroids to show the track data in the viewer.

dfs = []
for frame in range(len(labels)):
    df = pd.DataFrame(
        regionprops_table(labels[frame], properties=["label", "centroid"])
    )
    df["frame"] = frame
    dfs.append(df)
coordinate_df = pd.concat(dfs).set_index(["frame", "label"])
track_df2 = pd.merge(
    track_df, coordinate_df, right_index=True, left_index=True
).reset_index()
display(track_df2.head())

	frame	label	tree_id	track_id	centroid-0	centroid-1
0	0	1	0	0	148.526627	2.278107
1	0	2	1	1	108.118367	3.742857
2	0	3	2	2	62.957143	2.964286
3	0	4	3	3	249.611111	0.222222
4	0	5	4	4	5.279279	3.927928

To check the result, we add the tracks to to a Tracks layer.

graph = convert_split_merge_df_to_napari_graph(split_df, merge_df)
viewer.add_tracks(
    track_df2[["track_id", "frame", "centroid-0", "centroid-1"]].values,
    graph=graph,
    tail_length=1,
)

<Tracks layer 'Tracks' at 0x7fb2457595d0>

Show screenshot

viewer.dims.current_step = (4, 0, 0)
plt.imshow(viewer.screenshot())
plt.xticks([])
plt.yticks([])

([], [])