QuPath - Open-source bioimage analysis for research

Integrated Cell project implemented in pytorch

Shape-Out has been superseded by Shape-Out 2.

Track 2D cell motion and mitosis in time-lapse microscopy

manipulation and analysis of phase microscopy data

Implementation of HIDE as proposed in Völkl et al (2025), HIDE: Hierarchical cell-type Deconvolution

This remote module contains classes related to segmentation of biological cells. It has classes to represent cells' shape, color, and growth state. It also has classes to represent a cell genome, whose expression is modeled by differential equations.

A web-based application for Cell Shape Analysis.

High Intensity Peak Analysis for detecting High Intensity Cells

A small tool in python to read the bright-field image data and the phase image data recovered from a Digital holographic microscope (DHM) and segment the nuclei to calculate physical parameters like roughness and volume.

Visualize similar cells on a 2D plot using t-SNE

ImageJ macro for neurite analysis in high content

GO Webapp for the HIPA Tool

Advanced cell seeding efficiency analysis using CellPose AI segmentation with GPU acceleration. Automated classification of seed cells, fixed cells, dead cells, and debris with real-time progress tracking and comprehensive visualization outputs.

HIPA Tool GUI

A script to plot traces of muscle contractions from video recordings. This is part of my ongoing PhD work.

Convert experiment dataset of cells into one csv format.

Deep Learning based segmentation and tracking to understand the dynamics and morphology of cells over time.

Repository for publication outlining a number of computational models for complex wound analysis in both mice and humans.