This project is implemented as a part of the Data Science Practicum (CSCI 8360) course at the University of Georgia. The goal was to develop a video segmentation pipeline that identifies the regions of the videos containing cilia as accurately as possible. Cilia are microscopic 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.
The data are all available on GCP: gs://uga-dsp/project3
In that parent folder, there are two subfolders: data and masks.
- Data contains a bunch of folders (325 of them), named as hashes, each of which contains 100 consecutive frames of a grayscale video of cilia.
- Masks contain a number of PNG images (211 of them), named as hashes (corresponding to the subfolders of data), that identify regions of the corresponding videos where cilia is.
- Also within the parent folder are two text files: train.txt and test.txt
- Google Cloud Platform
- Tensorflow
- Python 3.6+
- Jupyter Notebook is a popular application that enables you to edit, run and share Python code into a web view. It allows you to modify and re-execute parts of your code in a very flexible way.
Please see CONTRIBUTORS file for more details.
(Ordered alphabatically)
This project is licensed under the MIT License - see the LICENSE file for the details.
- U-Net
- It is a Convolution Neural Networks for Biomedical Image Segmentation
- Best architecture to work with less training samples(images)
- U-Net Model
- PyTorch - RCNN with Pytorch
- Semantic Segmentation
- U-Net - Implementation of deep learning framework -- Unet, using Keras
- Image Segmentation using U-Net - Youtube tutorial
- Learn How to Train U-Net On Your Dataset - Medium Blog
- Semantic Segmentation Pipeline - Prerocess the image and segmentation masks with augmentations on the CPU.
- Pretrained CNN (VGG16 - imagenet) features for semantic segmentation using Random Forest - Youtube tutorial