I have tried to explain and study the nuts and bolts of this fascinating methodology the researchers have used in the paper mentioned below. and I have tried to summarize the functions in a ipynb notebook. I hope this will help.

Introduction

Link to the reference paper: End-to-end Learning of Convolutional Neural Net and Dynamic Programming for Left Ventricle Segmentation

Link to the mother code: https://github.com/minhnhat93/EDPCNN

Requirements

• Numpy
• Pytorch >= 0.4
• TensorboardX
• Shapely
• Matplotlib
• Scipy
• Scikit-image
• Opencv for python
• nibabel
• h5py
• warmup-scheduler

How to run

• you can create a folder called preproc_data and download the dataset hdf5 file from this Google Drive link.
• simply run the cells of demo_UNet_LV_Segment.ipynb and demo_EDPCNN.ipynb to run the experiments. you can edit the hyperparameters in the args Series cell.

Note

• This code only works on GPUs, preferrably NVIDIA ones with at least 10GB of VRAM. For GPUs with less VRAM, lowering the batch size may help.
• Due to the non-deterministic nature of large matrices reduction operations on GPU, the results over multiple runs will be slightly different but they usually have very similar loss curves and final performance.
• Sometime the training of the original U-Net may diverge and never go above 20% dice score on train set with only 10 images, simply restart the run script if this occurs.

Result

• Look into the report EDPCNN_project_report.pdf to find the results of the experiments and the details of the theoretical backbone and details of the ACDC dataset.
• One can save the predicted masks for the test data using unet and EDPCNN, into the folders UNet_predictions and EDPCNN_predictions by running the last cells of the ipynb notebooks.
• The loss and dice score curves can be found in the visualize folder.