byotrack 0.2.3

Biological particle tracking with Python

ByoTrack

Unified python API for biological particle tracking.

Many bioimage informatics tools already implement their own tracking tools (Icy, ImageJ, TrackMate...) but most of them are implemented in Java which makes it difficult for non-Java developers to experiment with the code. It is also difficult to integrate deep learning algorithms (mainly developed in Python) into these software.

We provide a unified python API for tracking that can be easily extended with new (and old) algorithms. We also provide implementations of well-known algorithms following our API.

Overview:

• Video
  • Able to read classical format (supported by opencv) + tiff
• Particle Tracking
  • MultiStepTracker (Detect / Link / Refine)
• Particle Detections
  • Wavelet Detector [2] (Similar as the one in Icy [1] but coded in pytorch)
  • Stardist [3] (Inference only. Training should be done with the official implementation)
• Particle Linking
  • EMHT [4] (Wrapper to the one implemented in Icy [1], requires Icy to be installed)
• Tracks Refining
  • Cleaning
  • EMC2 [5]: Track stitching (gap closing)
  • Interpolate missing positions

Install

$ pip install byotrack

Some tracker implementations require additional dependencies that are not installed with the library, to use them you need to install their dependencies on your own. Here is the complete list:

• StarDistDetector
  • stardist (+ tensorflow): Install stardist
• IcyEMHTLinker
  • Icy: Download Icy

For visualization, wtih byotrack.visualize module you need to install matplotlib.

Getting started

import byotrack

# Load some specific implementations
from byotrack.implementation.detector.wavelet import WaveletDetector
from byotrack.implementation.linker.icy_emht import IcyEMHTLinker
from byotrack.implementation.refiner.cleaner import Cleaner
from byotrack.implementation.refiner.stitching import EMC2Stitcher

# Read a video from a path, normalize and aggregate channels
video = byotrack.Video(video_path)
transform_config = VideoTransformConfig(aggregate=True, normalize=True, q_min=0.01, q_max=0.999)
video.set_transform(transform_config)

# Create a multi step tracker
## First the detector
## Smaller scale <=> search for smaller spots
## The noise threshold is linear with k. If you increase it, you will retrieve less spots.
detector = WaveletDetector(scale=1, k=3.0, min_area=5)

## Second the linker
## Hyperparameters are automatically chosen by Icy
linker = IcyEMHTLinker(icy_path)

## Finally refiners
## If needed you can add Cleaning and Stitching operations
refiners = []
if True:
    refiners.append(Cleaner(5, 3.5))  # Split tracks on position jumps and drop small ones
    refiners.append(EMC2Stitcher())  # Merge tracks if they track the same particle

tracker = byotrack.MultiStepTracker(detector, linker, refiners)

# Run the tracker
tracks = tracker.run(video)

# Save tracks
byotrack.Track.save(tracks, output_path)

Please refer to the .

Contribute

In coming...

References

• [1] F. De Chaumont, S. Dallongeville, N. Chenouard, et al., “Icy: an open bioimage informatics platform for extended reproducible research”, Nature methods, vol. 9, no. 7, pp. 690–696, 2012.
• [2] J.-C. Olivo-Marin, “Extraction of spots in biological images using multiscale products”, Pattern Recognition, vol. 35, no. 9, pp. 1989–1996, 2002.
• [3] U. Schmidt, M. Weigert, C. Broaddus, and G. Myers, “Cell de- tection with star-convex polygons,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11. Springer, 2018, pp. 265–273.
• [4] N. Chenouard, I. Bloch, and J.-C. Olivo-Marin, “Multiple hypothesis tracking for cluttered biological image sequences”, IEEE transactions on pattern analysis and machine intelligence, vol. 35, no. 11, pp. 2736–3750, 2013.
• [5] T. Lagache, A. Hanson, J. Perez-Ortega, et al., “Tracking calcium dynamics from individual neurons in behaving animals”, PLoS computational biology, vol. 17, pp. e1009432, 10 2021.