Open source Python library for building bioimage analysis pipelines
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Updated
May 23, 2024 - Jupyter Notebook
Open source Python library for building bioimage analysis pipelines
Object Detection, Object Segmentation, and Pose Detection with Tracking Using the Official Models of Ultralytics YOLOv8.
🚗 | UNet implementation using PyTorch | CARVANA Dataset | Car Segmentation
Multi-platform, free open source software for visualization and image computing.
Self configuring and adapting vision transformer for segmentation of 3d images
deep learning for image processing including classification and object-detection etc.
Cornerstone is a set of JavaScript libraries that can be used to build web-based medical imaging applications. It provides a framework to build radiology applications such as the OHIF Viewer.
An annotation and instance segmentation-based multiple animal tracking and behavior analysis package.
DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.
A uniform library wrapper for input from V4L2,Freenect,OpenNI,OpenNI2,DepthSense,Intel Realsense,OpenGL simulations and other types of video and depth input..
SimpleITK: a layer built on top of the Insight Toolkit (ITK), intended to simplify and facilitate ITK's use in rapid prototyping, education and interpreted languages.
A Matlab software package to do 2D cell segmentation.
MONAI Label is an intelligent open source image labeling and learning tool.
3D medical imaging reconstruction software
A generalizable application framework for segmentation, regression, and classification using PyTorch
RawHash is the first mechanism that can accurately and efficiently map raw nanopore signals to large reference genomes (e.g., a human reference genome) in real-time without using powerful computational resources (e.g., GPUs). Described by Firtina et al. (published at https://academic.oup.com/bioinformatics/article/39/Supplement_1/i297/7210440)
Template to be used as a starting point for creating a custom 3D Slicer application
Development of machine learning model for instance segmentation of nuclei cells for Kaggle Data Science Bowl 2018 challenge
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