FIBSEM Mitochondria Segmentation
Pre-trained model for the Deep Learning Prediction module detecting mitochondria in a Focused Ion Beam Scanning Electron Microscope (FIBSEM) data set.
This Xtra contains a pre-trained model for detecting mitochondria in a Focused Ion Beam Scanning Electron Microscope (FIBSEM) data set. It is most useful for detecting bright mitochondria on a darker background.
The model is based on a convolutional neural network (CNN) with U-Net architecture and was trained on a FIBSEM data from human liver tissue.
The data was pre-processed with the BSE SEM denoiser model: (bse-sem-denoiser).
The model can be tested with the FIBSEM_Human_Liver_sample.tif sample data.
The downloadable archive contains the following files:
• Mitos_Adam_final.json = model architecture
• Mitos_Adam_final.hdf5 = trained weights
• Mitos_Adam_final.py = pre-processing functions required to connect the model to Avizo2D / Amira / Avizo grayscale images
• a test image (FIBSEM_Human_Liver_sample.tif)
The model can be applied to any grayscale image or volume, using the Deep Learning Prediction module.
Note that the training data was pre-processed with the BSE SEM denoiser model (bse-sem-denoiser). This is recommended before using this model to predict mitochondria.
Follow the example video to see the model applied in Avizo2D to the sample data set. Amira and Avizo users can follow similar steps using the Image Stack Processing module and workroom.
Data courtesy of Bruno Humbel, Caroline Kizilyaprak, and Jean Daraspe (Electron Microscopy Facility Lausanne University)