Cilia are micro-scopic hairlike structures that protrude from literally every cell in your body. They beat in regular, rhythmic patterns to perform myriad tasks, from moving nutrients in to moving irritants out to amplifying cell-cell signaling pathways to generating calcium fluid flow in early cell differentiation. Cilia, and their beating patterns, are increasingly being implicated in a wide variety of syndromes that affected multiple organs.
The first step would be to extract features from data.
The data consists of videos (in the form of 100 consecutive frames). As each sample is a video, we can make use of spatial as well as temporal features.
The features we used are:
We pass the extracted features through a U-NET.
The data are all available on GCP: gs://uga-dsp/project2
-
Data: Contains a bunch of folders (325 of them), named as hashes, each of which contains 100 consecutive frames of a gray scale video of cilia.
-
Mask: Masks contains a number of PNG images (211 of them), named as hashes (cor-responding to the sub folders of data), that identify regions of the corresponding videos where cilia is.
- Python-3.6
- keras
- open-cv
- runs a folder of cilia videos in parallel extracting each feature as directed
python feature_engineering.py -i \<input directory\> -o \<output directory\> -f \<feature name\> [-g | if you want greyscale frame added]
python feature_engineering.py -h for help
- runs a folder of cilia video pngs converting them to npy files
python png_to_npy.py -i \<input directory\> -o \<output directory\> [-s | to save while processing to conserve ram]
python png_to_npy.py -h for help
- Unet based model to get predictions using the extracted features.
- Create a data folder containing the features, masks, training hash list and testing hash list.
- Follow the given structure:
data
--features
--|--<hash_1>.npy
--|--<hash_2>.npy
--|--.....
--|--<hash_n>.npy
--masks
--|--<mask_1>.png
--|--<mask_2>.png
--|--.....
--|--<mask_n>.png
--train.txt
--test.txt
model.py -d \<data directory>\ -e \<number of epochs>\ -v \<train-validation split>\
Required parameters:
<data directory> Path to the data folder created as per the specifictions.
Optional parameters:
<number of epochs> Number of epochs to train for. Defaults to 100.
<train-validation split> Ratio to split data. Enter 1 to use all data for training. Defaults to 0.7
Predictions will be stored in data/predictions
| Features Used | IOU |
|---|---|
| Beat Frequency + 1 frame | 15% |
| Optical flow | 24.5% |
| Variance Fluctuations, Beat Frequency, Optical Flow + 1 frame | 34.4% |
- Sumer Singh
- Andrew Durden
- Anant Tripathi
There are no specific guidlines for contibuting. If you see something that could be improved, send a pull request! If you think something should be done differently (or is just-plain-broken), please create an issue.
[1] https://docs.opencv.org/3.4/d7/d8b/tutorial_py_lucas_kanade.html
[2] https://towardsdatascience.com/image-pre-processing-c1aec0be3edf
[3] https://arxiv.org/abs/1505.04597
[4] https://github.com/quinngroup/cilia-cbf
This Project is under the MIT License. For more details visit License file here: https://github.com/dsp-uga/Taylor-P2/blob/master/LICENSE