Ankit Vaghela

Ankit Vaghela

Machine Learning Engineer at Amazon

Cilia segmentation

3 minute read

Cilia Segmentation

This repository contains several algorithms for cilia segmentation and other support scripts for further experiments. Although Cilia segmentation is 3 label segmentation, we modify the mask to contain only cilia and background. We used three different approaches to solve the problen:

1) Optical Flow 2) Unet 3) Tiramisu

Data

Data are sequences of frame images. There are 325 videos in total each with 100 consecutive frames of a grayscale video of cilia. 211 of them are for training and 114 for testing.

Each training video also has one mask image with 3 labels. Background pixels are labeled 0, cells are labeled 1 and cilia are labeled as 2.

The output is one mask for each testing video with pixels of cilia being labeled as 2. a

Getting Started

Below are instructions for installing and using this package.

Prerequisites

Environment Setup

You can use the REQUIREMENTS.txt file to setup the environment. The routine is as below:

conda create --name MY_ENV --file REQUIREMENTS.txt
source activate MY_ENV

Execution Step

python3 -m hastings.__main__ <args>

The following arguments are supported by our model:

  • model : Specify the model you want to use. Ex: –model=”unet”
  • mode : Specify if you want to fit the model or predict. Ex: –mode=”fit”
  • operation : Specify if you want to preprocess Ex: –operation=”preprocess”
  • image_path : Specify the path of numpy array of prepared image data Ex: –image_path=”/home/ubuntu/img.npy”
  • mask_path : Specify the path of numpy array of prepared mask data Ex: –mask_path=”/home/ubuntu/mask.npy”
  • model_path: Specify the path of trained model Ex: –model_path=”/home/ubuntu/model.h5”
  • save_path: Specify the path where you want to save result (Model while fitting, predictions while predicting, numpy arrays while predicting. The path is only required till the directory. Ex: –save_path=”/home/ubuntu/models/”
  • file_path : Specify the path to the train.txt or test.txt files which contain the hash values of folders/ Ex: –file_path=”/home/ubuntu/data/train.txt”
  • data_path : Specify the path to the data folder which contains each hash folder. Ex: –data_path=”/home/ubuntu/data/train/”
  • num_images: Specify the number of images to consider for training or testing from each hash folder. Ex: –numImages=16

Run the testing

  • Testing all modules 
    ./setup.py test
  • Testing one module 
    python -m pytest tests/[file you want to test]
  • Testing one function under one module 
    python -m pytest tests/[file you want to test]::[function name]

Evaluation

Evaluation of the output masks is done by checking IoU. An illustration for calculating IoU is show asa below.

Test results

Algorithm Parameter value IoU
Optical Flow threshold = 110 16.02
Optical Flow threshold = 100 17.73
Optical Flow threshold = 90 18.97
Optical Flow threshold = 85 19.34
Optical Flow threshold = 81 19.48142
Optical Flow threshold = 80 19.48875
Optical Flow threshold = 79 19.48135
Optical Flow threshold = 78 19.47
Optical Flow threshold = 75 19.40
Optical Flow threshold = 70 19.00

Accuracy Score

The score below is based on the Autolab grader used by Dr.Shannon Quinn for the course.

  • optical Flow : 19.48875
  • Unet : 4.6
  • Tiramisu : Couldn’t train or test on the whole dataset.

Project link:

link: https://github.com/ankit-vaghela30/Cilia-Segmentation

Wiki link:

I recommend checking the wiki of the project for more details wiki: https://github.com/ankit-vaghela30/Cilia-Segmentation/wiki

Authors

(Ordered alphabetically)

See CONTRIBUTORS file for more details.

References

[1] Olaf Ronneberger, Philipp Fischer, Thomas Brox; U-Net: Convolutional Networks for Biomedical Image Segmentation; arXiv:1505.04597

[2] Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, Yoshua Bengio; The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation; arXiv:1611.09326

[3] Aleksander Klibisz, Derek Rose, Matthew Eicholtz, Jay Blundon and Stanislav Zakharenko; Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks; https://arxiv.org/pdf/1707.06314.pdf

[4] OpenCV optical flow; https://docs.opencv.org/3.3.1/d7/d8b/tutorial_py_lucas_kanade.html

[5] Jeremy Howard, Fast.ai deep learning tutorial, https://github.com/fastai/courses/blob/master/deeplearning2/tiramisu-keras.ipynb

[6] Team Canady Unet model, https://github.com/dsp-uga/Canady/blob/master/unet.py

License

This project is licensed under the MIT License - see the LICENSE file for details.

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