-
Notifications
You must be signed in to change notification settings - Fork 0
Home
Shophine edited this page Apr 23, 2021
·
3 revisions
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 into 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, we will find 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 are.
- Also within the parent folder are two text files: train.txt and test.txt. They contain the names, one per line, of the videos in each dataset. Correspondingly, you will only find masks in the masks folder for those named in train.txt; the others, you’ll need to predict (and are on AutoLab)! The training/testing split is 65 / 35, which equates to about 211 videos for training and 114 for testing.
- 2 corresponds to cilia (what you want to predict!)
- 1 corresponds to a cell
- 0 corresponds to background (neither a cell nor cilia)