Unet for Medical Image Segmentation using TF 2.x

This example demonstrates the use of U-net model for retinal blood vessel segmentation on tensorflow 2.x

This jupyter notebook presents all requirements needed to achieve pixel-level semantic segmentation using images. This specific use case is on retinal image blood vessel segmentation as explained in the blog series:

https://www.youtube.com/watch?v=1kiPy2tvECs&list=PLQKflBw-kPeecjC345saTF2YrfImciCrr

This specific implementation models the U-net implemenation in the research paper: Roychowdhury S. Few Shot Learning Framework to Reduce Inter-observer Variability in Medical Images. IEEE ICPR, 2021. https://arxiv.org/abs/2008.02952

Step 1: Library requirements

Download the library requirements "requirements.yml" and ensure your python environment is compatible.

Step 2: Train and Test Data

Download the STARE vessel data

From the page https://cecas.clemson.edu/~ahoover/stare/probing/index.html
Download 20 images (top row) followed by the labelled vessels by Adam Hoover (second row).
Unpack the images and labels in a local folder.
Create the following folder structure in your local folder:

    ./STARE/
            │
            └───train
            |    └───images
            |    └───GT
            │
            └───test
                ├──images
                └───GT

The following images are used to train: [image0001 image0002 image0003 image0004 image0005 image0044 image0077 image0081 image0082 image0139 ]
Place these images under ./STARE/train/images/ folder and the hand labelled images under ./STARE/train/GT folder
All remaining images are placed under ./STARE/test/images/ and the respective hand labelled images are under ./STARE/test/GT/

Step 3: Get the code

Download the U-net_TF2_retinal_image_segmentation_STARE.ipynb and run it.

Step 4: Results:

The U-net with Depth 4 is trained using 10 images, loss function of binary-crossentropy, Adam optimizer and augmented significnatly using the keras imagedata generator.
The tensorboard graphs are as follows:

The segmentation perfromances on test images are:

Precision= 0.7557, Recall= 0.8493 IoU= 0.6648 acc= 0.9606 F1= 0.7979

Sample examples are: Predicted output:
Other outputs under STARE(Copy)/test/pred/

fpvillani/Unet-using-TF2