Encoder-Decoder Cell and Nuclei segmentation models
ImageJ macro for the analysis of foci (e.g. DNA damage) in nuclei/cells
The code implementation for cell segmentation
Intel OpenVINO extension for QuPath, a digital pathology platform
Package using StarDist and Python that performs object detection and spatial analysis on H&E images
Instance & Semantic сегментация клеток и ядер на цитологических изображениях (мазок Папаниколау)
Pipeline meant to segment and classify organoids, or any other blob-like structures (star-convex polygons). Microscopy images can be easily annotated in QuPath and automatically processed afterwards to count the class distribution within each image using this pipeline (TIF files will be converted to grayscale)
This repository provides deep learning models trained on a large dataset of Pancreatic Ductal Adenocarcinoma Organoids co-cultured with immune cells.
Quantification of Myogenic Differentiation using Deep Learning
Fiji/ImageJ plugin for classifying nucleoid objects by overlap with a mitochondrial mask. Input images must be spatially calibrated in µm.
Cell segmentations using Stardist and Watershed method.
Biomedical computer vision pipeline for nuclei segmentation, instance detection and morphology, benchmarking custom U-Net, StarDist and Cellpose-SAM.
StarDist + U-Net instance segmentation of tree crowns in aerial imagery for forest carbon monitoring.
Bioimage analysis for microscopy images and 3D stacks using Java computer vision and Python deep-learning segmentation.
Fiji/ImageJ plugin for StarDist-based 3D object counting, measurement, maps, and batch processing.
script to create labels (for training a deep neural network) based on nucleus segmentation using StarDist (still experimental)
GPU-accelerated Python pipeline for image-based spatial omics, built on Sopa and SpatialData
Lipid-droplet instance segmentation and morphology analysis in fluorescence CLSM images
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