kreshuklab/panseg
A tool for cell instance aware segmentation in densely packed 3D volumetric images
This tool helps biologists and researchers accurately identify and separate individual cells within dense 3D microscope images, particularly for plant and animal tissues. You input high-resolution confocal or light sheet microscopy images, and it outputs segmented images where each cell is clearly distinguished. This is ideal for scientists studying cell structures and growth in biological samples.
124 stars.
Use this if you need to precisely count and analyze individual cells in crowded 3D volumetric images from microscopy experiments.
Not ideal if your images are 2D or if you're working with non-biological samples.
Stars
124
Forks
41
Language
Python
License
MIT
Category
Last pushed
Mar 26, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/computer-vision/kreshuklab/panseg"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related tools
PytorchConnectomics/pytorch_connectomics
PyTorch Connectomics: segmentation toolbox for EM connectomics
segments-ai/segments-ai
Segments.ai Python SDK
afermg/cp_measure
Morphological features from images and masks made easy.
oarriaga/paz
Hierarchical perception library in Python for pose estimation, object detection, instance...
JdeRobot/PerceptionMetrics
A toolkit designed to unify and streamline the evaluation of object detection and segmentation...