This repository contains Jupyter notebooks for analyzing and visualizing tracking, spot detection, and nuclei segmentation data from the Kurppa dataset.
Implements a deep learning-based pipeline for nuclei detection:
- Loads raw microscopy images
- Applies pretrained segmentation model
- Outputs masks and postprocessed features
- Can be integrated with tracking and spot analysis workflows
Performs analysis of track data, including:
- Filtering and preprocessing of trajectories
- Calculating metrics like speed, displacement, and direction
- Summary statistics and initial visualizations
Analyzes spot detection data:
- Aggregates spot-level features
- Calculates statistical descriptors
- Compares spot distributions across experimental conditions
Generates visualizations for spot analysis results:
- Box plots, histograms, and scatter plots
- Supports grouped comparisons
- Prepares publication-ready figures
To run the notebooks in Google Colab, click on the "Open in Colab" badges above.
Make sure to upload your dataset or mount your Google Drive as needed.