Commit 2b94cd99 authored by katharina.loeffler's avatar katharina.loeffler
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Update README.md

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##Setup
This repository contains the code to the publication:
A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction
Katharina Löffler, Tim Scherr, Ralf Mikut
bioRxiv 2021.03.16.435631; doi: https://doi.org/10.1101/2021.03.16.435631
## Setup
### 1) create folder structure
- create local directory LOCAL_DIR
- clone code to LOCAL_DIR
- add folders data and results in LOCAL_DIR
so the final structure is
--LOCAL_DIR
|
-- data (contains the ground truth data sets)
|
-- code
|
-- results (synthetically degraded segmentation masks stored will be stored here)
```
LOCAL_DIR
└───data (contains the ground truth data sets)
└───code
└───results (synthetically degraded segmentation masks stored will be stored here)
```
### 2) create environment
- install packages from requirements
- install gurobi 9.1.1 (see help_gurobi.txt)
## run tracking
- the tracking algorithm can be used with any 2D/3D image data with a segmentation which needs to be provided by the user
- it is assumed that the image data and segmentation data have a similar naming convention as used by the cell tracking challenge (http://celltrackingchallenge.net)
## Reproduce data sets
## Reproduce synthetic data sets
### 1) download data sets
- go to http://celltrackingchallenge.net
and download : Fluo-N2DH-SIM+ and FLuo-N3DH-SIM+ and unpack and save in data directory
and download : Fluo-N2DH-SIM+ and FLuo-N3DH-SIM+ and unpack and save in data directory:
```
LOCAL_DIR
└───data (contains the ground truth data sets)
│ └───Fluo-N2DH-SIM+
│ └───Fluo-N3DH-SIM+
└───code
└───results (synthetically degraded segmentation masks stored will be stored here)
```
### 2) run code
- run create_synth_segm_data.py to create synthetically
- run create_synth_segm_data.py to create synthetically degraded segmentation mask images
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