PySMACKS

github.com/molcretb/pysmacks
Active1updated 1 week ago
Python
MIT

Python computational framework for analysis of single-molecule FRET data

Sourced from

  • GitHubgithub.com/molcretb/pysmacks
  • bio.toolspysmacks

Related resources

Community-driven model zoo and deployment infrastructure for AI-powered bioimage analysis, enabling standardized sharing, validation, and cross-platform execution of deep learning models across Fiji, Ilastik, napari, and other scientific imaging tools (EPFL, EMBL, and global collaborators, actively maintained)

Active391 week ago
Jupyter Notebook
MIT

Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells.

Active362 months ago
R
GPL-2.0+

S3segmenter is a Matlab-based set of functions that generates single cell (nuclei and cytoplasm) label masks.

Idle39 months ago
Python

Community Terrestrial Systems Model (includes the Community Land Model of CESM)

Desktop viewer for microscopy and whole slide pathology images on Windows and macOS. Opens whole slide scanner formats (Aperio SVS, Hamamatsu NDPI, MIRAX MRXS, Leica SCN, Ventana BIF) alongside acquisition formats (Zeiss CZI, Nikon ND2, DICOM, TIFF/OME-TIFF) in a single application, providing pyramid navigation of gigapixel images, metadata inspection, calibrated measurements, annotations, and on-device segmentation and object counting.