Synopsis
Project Title: Automating the Segmentation of X-ray Images with Deep Neural Networks
Team Member: Shuli Sun S232245, Søren Blatt Bendtsen s164521, Pantelis Apostolidis s230697, Ioannis Louvis s222556
Motivation & Background: It is a time-consuming and error-prone process to segment the result images from the X-ray physics experiments. So, we want to build a deep-learning model to do it.
Milestones:
Week 1:
Read through references (Image segmentation, UNet, reports by supervisor)
Read in the data to Python / Pytorch
Visualize a few images
See if any type of data preparation / cleaning is needed
Week 2:
Have access to GPU (using the HPC guide uploaded by the TA’s)
Start to build a simple model to train the data (Study for Networks like VGGNET, UNet)
Encoder, Decoder
Week 3 – 4:
Finetune model and hyperparameters
Try different techniques
Neural Network
Week 5:
Work on Poster Session
Write report
Week 6 – 7:
Finetune model
Finish report
References:
De Angelis, S. et al. Three-dimensional characterization of nickel coarsening in solid oxide cells via ex-situ ptychographic nano-tomography. Journal of Power Sources 383, 72–79 (2018).
De Angelis, S. et al. Ex-situ tracking solid oxide cell electrode microstructural evolution in a redox cycle by high resolution ptychographic nanotomography. Journal of Power Sources 360, 520–527 (2017).