standalone

Description

The method proposed in this paper is a robust combination of multi-task learning and unsupervised domain adaptation for segmenting amoeboid cells in microscopy. This end-to-end framework provides a consolidated mechanism to harness the potential of multi-task learning to isolate and segment clustered cells from low contrast brightfield images, and it simultaneously leverages deep domain adaptation to segment fluorescent cells without explicit pixel-level re- annotation of the data.

The entry-point to the codebase is the main.py file. The user has the option to

  • Train the network on their own dataset
  • Load a pre-trained model and use that for inference on their own data
  • NoteThe provided pretrained model was trained on 256x256 images. Results on different resolutions could require fine-tuning This model is trained (supervised) on brightfield, and domain adapted to fluorescence data. The results are saved as 'inference.png'
has function
daman
Description

OrganoSeg is an open-source software that integrates segmentation, filtering, and analysis for breast-cancer spheroid and colon and colorectal-cancer organoid morphologies.

Figure 2 in OrganoSeg Scientific Reports publication
Description

Fractal is a framework to process high-content imaging data at scale and prepare it for interactive visualization. Fractal provides distributed workflows that convert TBs of image data into OME-Zarr files. The platform then processes the 3D image data by applying tasks like illumination correction, maximum intensity projection, 3D segmentation using cellpose and measurements using napari workflows. The pyramidal OME-Zarr files enable interactive visualization in the napari viewer.

need a thumbnail
Description

The Incucyte® Base Analysis Software provides a guided interface and purpose-built tools, which include the process of acquiring, viewing, analyzing and sharing images of living cells.

need a thumbnail
Description

FluoGAN is a fluorescence image deconvolution software combining the knowledge of acquisition physical model with gan. It takes a fluctuating sequence of blurred, undersampled and noisy images of the sample of interest  fixed sample as input from wide field or confocal and returns a super resolved image.

FluoGan