Search Results for "image segmentation"

...The quality of machine learning models hinges on the quality of the data used to train them, but it is hard to manually identify all of the low-quality data in a big dataset. CleanVision helps you automatically identify common types of data issues lurking in image datasets. This package currently detects issues in the raw images themselves, making it a useful tool for any computer vision task such as: classification, segmentation, object detection, pose estimation, keypoint detection, generative modeling, etc.

...Datachain can persist features of Python objects returned by AI models, and enables vectorized analytical operations over them. The typical use cases are data curation, LLM analytics and validation, image segmentation, pose detection, and GenAI alignment. Datachain is especially helpful if batch operations can be optimized – for instance, when synchronous API calls can be parallelized or where an LLM API offers batch processing.

Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris. This course is being taught at as part of Master Year 2 Data Science IP-Paris. Note: press "P" to display the presenter's notes that include some comments and additional references. This lecture is built and maintained by Olivier Grisel and Charles Ollion.

GIMIAS is a workflow-oriented environment for solving advanced biomedical image computing and individualized simulation problems, which is extensible through the development of problem-specific plug-ins. In addition, GIMIAS provides an open source framework for efficient development of research and clinical software prototypes integrating contributions from the Physiome community while allowing business-friendly technology transfer and commercial product development.

BioImageXD - free open source software for analysis, processing and 3D rendering of multi dimensional, multi data channel, time series image data from microscopy and other sources.

ITK-SNAP is a software application used to segment structures in 3D medical images. It is the product of a decade-long collaboration between Paul Yushkevich, Ph.D., of the Penn Image Computing and Science Laboratory (PICSL) at the University of Pennsylvania, and Guido Gerig, Ph.D., of the Scientific Computing and Imaging Institute (SCI) at the University of Utah, whose vision was to create a tool that would be dedicated to a specific function, segmentation, and would be easy to use and learn. ITK-SNAP is free, open-source, and multi-platform.

MITO Leap Motion Fork is a fork of the Medical Imaging TOolkit. It integrate the Leap SDK so that you can interact with the application using hand gestures.

The 2P_Tools.jar is a series of hopefully useful Fiji Plugins to evaluate data acquired by two-photon imaging. It supports multiple features like conversion to normalized image stacks, preprocessing and image segmentation.

"MITO - Medical Imaging TOolkit" is an open-source, Windows-based software architecture for advanced Medical Imaging. Main characteristics are: DICOM compliant, 2D/3D/S3D visualization, image segmentation and fusion, ROI, advanced 3D user interface

3D medical image platform for visualization and image processing. Segmentation with Levels sets. Surface reconstruction with marching Cubes, texture Mapping and Raycasting,DICOM support. Integration environment for VTK, ITK and vtkInria3D under wxWidgets

SMIT (Scientific & Medical Imaging Tool) assists in image analysis of 3D datasets. It supports image registration, image segmentation, 3D ROI analysis, and least square fitting. Its powerful OpenGL engine displays your data fast and accurately. Enjoy!

Open Source and Portable object oriented software for 3D digital image segmentation and visualization. Pipes and Filters Architechture. Implemented in C++, and uses FLTK, VTK, ITK, TCL (plugins), and pipeline filters implemented in XML. DICOM support.

The 3D Slicer is freely available, open-source software for visualization, registration, segmentation, and quantification of medical image data. Follow the link for more info: http://slicer.org