DeepQIBC (Quantitative Image-Based Cytometry) is a program that aims to equip researchers with the ability to utilise deep learning algorithms to detect nuclei within multichannel microscopy images through a user-friendly interface.
To get started with DeepQIBC, see the setup guide
Here, you will find the required weights file mask_rcnn_nucleus.h5 to run DeepQIBC. Place this weights file in the DeepQIBC/weightssubfolder.
These weights were trained using black and white (DAPI) images from the Kaggle Data Science Bowl 2018 (Caicedo et al. Nature Methods, 2019).
Update notes:
- Fixed a bug with the medium computation method setting
- Added code to generate time plot