Skip to main content

Utilities for computing common accuracy metrics on cell tracking challenge solutions with ground truth

Project description

traccuracy: Evaluate Cell Tracking Solutions

License PyPI Python Version CI Benchmarking Documentation Status codecov

traccuracy provides a suite of benchmarking functions that can be used to evaluate cell tracking solutions against ground truth annotations. The goal of this library is to provide a convenient way to run rigorous evaluation and to document and consolidate the wide variety of metrics used in the field.

traccuracy can compute a comprehensive set of metrics for evaluating the cell linking and division performance, and can compute biologically meaningful metrics such as number of correctly reconstructed lineages over N frames and cell cycle length accuracy. As matching ground truth and predicted lineages is a crucial step for performing evaluation, traccuracy includes a number of algorithms for matching ground truth and predicted lineages, both with and without segmentation masks.

Learn more in the documentation or check out the source code.

Installation

pip install traccuracy

Getting Started

The traccuracy library has three main components: loaders, matchers, and metrics. Loaders load tracking graphs from other formats, such as the CTC format, into a TrackingGraph object. A TrackingGraph is a spatiotemporal graph. Nodes represent a single cell in a given time point, and are annotated with a time and a location. Edges point from a node representing a cell in time point t to the same cell or its daughter in t+1. To load TrackingGraphs from a custom format, you will likely need to implement a loader: see documentation here for more information.

Matchers take a ground truth and a predicted TrackingGraph with optional segmentation masks and match the nodes and edges to allow evaluation to occur. Metrics are then computed on the matched graphs, and a summary is printed out.

The traccuracy library has a command line interface for running common metrics pipelines, documented here, and a flexible Python API, shown in this example notebook.

Implemented Metrics

  • CTC-DET from the Cell Tracking Challenge Evaluation Methodology
  • CTC-TRA from the Cell Tracking Challenge Evaluation Methodology
  • Acyclic Oriented Graph Metric (AOGM) from Matula et al. 2015. A generalized form the CTC metrics where you can supply different weights for each component of the overall metric.
  • Division Precision. Optionally allows detection within N frames of ground truth division.
  • Division Recall. Optionally allows detection within N frames of ground truth division.
  • Division F1 score. Optionally allows detection within N frames of ground truth division.
  • Mitotic Branching Correctness from Ulicna et al. 2021. TP / (TP + FP + FN). Optionally allows detection within N frames of ground truth division.

Glossary

Tracklet : A single non-dividing cell tracked over time. In graph terms, this is the connected component of a track between divisions (daughter to next parent). Tracklets can also start or end with a non-dividing cell at the beginning and end of the captured time or if the track leaves the field of view.

Track : A single cell and all of its progeny. In graph terms, a connected component including divisions.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

traccuracy-0.1.1.tar.gz (1.2 MB view details)

Uploaded Source

Built Distribution

traccuracy-0.1.1-py3-none-any.whl (37.0 kB view details)

Uploaded Python 3

File details

Details for the file traccuracy-0.1.1.tar.gz.

File metadata

  • Download URL: traccuracy-0.1.1.tar.gz
  • Upload date:
  • Size: 1.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.4

File hashes

Hashes for traccuracy-0.1.1.tar.gz
Algorithm Hash digest
SHA256 afa385243d9aca1c6630a691c9d9fb5d1ad711364f8a4be7e41526a87ebd6fa2
MD5 6340a024f8a9a1c39bcc14a33cd58c01
BLAKE2b-256 eee1fc36e12b4d4f82e1613a8be85c30431419bdcdff188620dd5b0ba7c551eb

See more details on using hashes here.

Provenance

File details

Details for the file traccuracy-0.1.1-py3-none-any.whl.

File metadata

  • Download URL: traccuracy-0.1.1-py3-none-any.whl
  • Upload date:
  • Size: 37.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.4

File hashes

Hashes for traccuracy-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 2a6092e349584796d2d0f90d7872f09d4f6f04a6f610ffe36e3d380f10912b41
MD5 af736c5f6e3df13b1c06fe32d856ab77
BLAKE2b-256 22afd5c1bdde459023257c1be09e62f3ddbc003793eb0ed6019b8527eb562ec3

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page