/3D-Convolutional-Neural-Network-for-Medical-Image-Segmentation

Project for Neural Networks exam.

Primary LanguageJupyter Notebook

3D-Convolutional-Neural-Network-for-Medical-Image-Segmentation

This project was developed for the Neural Networks exam, academic year 2021/2022, University La Sapienza of Rome.

Getting Started

The goal of the first task of this project is to modify the classical U-shaped architecture of the convolutional neural network for MRI (U-Net). In the second task was provided a TensorFlow implementation of my network to work with a breast cancer dataset (BUSI).

First task

The dataset used for this task is IXI Tiny. The original IXI dataset is a collection of 600 MR brain images from normal, healthy subjects. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization.
In the upsampling part we have also a large number of feature channels, which allow the network to propagate context information to higher resolution layers.

Second task

The dataset used for this task is BUSI. Is possible to see how the network implemented for the first tisk is able to achieve really good results also for the segmentation of breast cancer images.

Installing

In this repository there are 2 Colab Notebooks. All the code can be runned using Google Drive on every browser.
You have simply to paste all the Notebooks in your Drive.

Results

First task

Second task

Author

References

U-Net: Convolutional Networks for Biomedical Image Segmentation

@article{DBLP:journals/corr/RonnebergerFB15,
  author    = {Olaf Ronneberger and
               Philipp Fischer and
               Thomas Brox},
  title     = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
  journal   = {CoRR},
  volume    = {abs/1505.04597},
  year      = {2015},
  url       = {http://arxiv.org/abs/1505.04597},
  eprinttype = {arXiv},
  eprint    = {1505.04597},
  timestamp = {Mon, 13 Aug 2018 16:46:52 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/RonnebergerFB15.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
}

Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

@ARTICLE{2021arXiv210505537C,
       author = {{Cao}, Hu and {Wang}, Yueyue and {Chen}, Joy and {Jiang}, Dongsheng and {Zhang}, Xiaopeng and {Tian}, Qi and {Wang}, Manning},
        title = "{Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation}",
      journal = {arXiv e-prints},
     keywords = {Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition},
         year = 2021,
        month = may,
          eid = {arXiv:2105.05537},
        pages = {arXiv:2105.05537},
archivePrefix = {arXiv},
       eprint = {2105.05537},
 primaryClass = {eess.IV},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210505537C},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
}