# Cell Tracking via Proposal Generation and Selection

Source: [Cell Tracking via Proposal Generation and Selection](<https://www.sota2.com/research/paper/68fa63531e209131ce9fe2f5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:39:15.914Z

Authors: Saad Ullah Akram, Juho Kannala, Lauri Eklund, Janne Heikkil\\"a

Published: 2017-05-09T15:30:39.000Z

## Abstract

Microscopy imaging plays a vital role in understanding many biological processes in development and disease. The recent advances in automation of microscopes and development of methods and markers for live cell imaging has led to rapid growth in the amount of image data being captured. To efficiently and reliably extract useful insights from these captured sequences, automated cell tracking is essential. This is a challenging problem due to large variation in the appearance and shapes of cells depending on many factors including imaging methodology, biological characteristics of cells, cell matrix composition, labeling methodology, etc. Often cell tracking methods require a sequence-specific segmentation method and manual tuning of many tracking parameters, which limits their applicability to sequences other than those they are designed for. In this paper, we propose 1) a deep learning based cell proposal method, which proposes candidates for cells along with their scores, and 2) a cell tracking method, which links proposals in adjacent frames in a graphical model using edges representing different cellular events and poses joint cell detection and tracking as the selection of a subset of cell and edge proposals. Our method is completely automated and given enough training data can be applied to a wide variety of microscopy sequences. We evaluate our method on multiple fluorescence and phase contrast microscopy sequences containing cells of various shapes and appearances from ISBI cell tracking challenge, and show that our method outperforms existing cell tracking methods.   Code is available at: https://github.com/SaadUllahAkram/CellTracker

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Cell Tracking on CTC HeLa-01 (train)](<https://www.sota2.com/research/sota/cell-tracking-on-ctc-hela-01-train.md>): Cell tracking; CTC HeLa-01 (train); TRA: 98.69.

- [Cell Tracking on CTC HeLa-02 (train)](<https://www.sota2.com/research/sota/cell-tracking-on-ctc-hela-02-train.md>): Cell tracking; CTC HeLa-02 (train); TRA: 98.26.

- [Cell Tracking on CTC U373-02 (train)](<https://www.sota2.com/research/sota/cell-tracking-on-ctc-u373-02-train.md>): Cell tracking; CTC U373-02 (train); TRA: 93.46.

- [Cell Tracking on GOWT1-02 CTC (train)](<https://www.sota2.com/research/sota/cell-tracking-on-gowt1-02-ctc-train.md>): Cell tracking; GOWT1-02 CTC (train); TRA (Tracking Accuracy): 97.19.

- [Cell Tracking on GOWT CTC 1-01 (train)](<https://www.sota2.com/research/sota/cell-tracking-on-gowt-ctc-1-01-train.md>): Cell tracking; GOWT CTC 1-01 (train); Tracking Accuracy: 98.64.

- [Cell Tracking on CTC U373-01 (train)](<https://www.sota2.com/research/sota/cell-tracking-on-ctc-u373-01-train.md>): Cell tracking; CTC U373-01 (train); TRA Score: 95.94.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1705.03386>)

[PDF](<https://arxiv.org/pdf/1705.03386.pdf>)