3D vertex model allowing scutoids
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Updated
Jun 7, 2024 - Python
3D vertex model allowing scutoids
Multiple particle tracking in dense 3D particle fields complemented with dynamic regions of interest and trackability inferences for the automated exploration of large volumetric sequences.
Detect morphological motifs, such as blebs, filopodia, and lamellipodia, from 3D images of surfaces, particularly images of cell surfaces.
Pipeline for inference of Granger-causal relations in molecular systems to study actin regulation in lamellipodia
u-inferforce (Traction Force Microscopy) is a MATLAB software that reconstructs traction forces of cells adhered on elastic gel doped with beads.
Python code used to detect and track blebs frame-to-frame, and to analyze bleb size statistics before and after photoactivation.
a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
Python implementation of the cellular automata model corresponding to Lange, Schmied et. al.
A Jupyter notebook to identify collective cell signalling in raw microscopy images
Complete analysis code for Bland et al., 2024
Functions for proteomic analysis and more
A Matlab software package to do 2D cell segmentation.
A Matlab package for segmentation of filament and the orientation.
Multiple-particle tracking designed to (1) track dense particle fields, (2) close gaps in particle trajectories resulting from detection failure, and (3) capture particle merging and splitting events resulting from occlusion or genuine aggregation and dissociation events
Interactive deep learning whole-cell segmentation and thresholding using partial annotations
Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22, CVPR'22, MICCAI'23, Histopathology'23, MICCAI'24]
Analyze local cell edge motions (e.g. protrusion and retraction) and to locally sample intracellular fluorescence signals in 2D fluorescence microscopy data.
This focal adhesion package is a software that tracks, segments, classifies and analyzes the time series of focal- and nascent adhesions in an adhesion time-lapse images.
Processing of raw ratiometric biosensor images (for example based on FRET) into fully corrected "ratio maps" or "activation maps" — images showing the localized activation of the biosensor.
This repository contains a Python application using OpenCV for detecting mitosis in images. Aimed at researchers and biologists, it provides tools to automate the counting and identification of mitotic figures in microscopic images, supporting studies in cellular biology and medical diagnostics.
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