Goal: Designing an algorithm that learns how to segment cilia.

Background: 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. Cilia, and their beating patterns, are increasingly being implicated in a wide variety of syndromes that affected multiple organs.

Example: Image (left) and Mask (right)

Our team developed two models to solve this classification problem

• located in the Unet_v2.ipynb file

U-Net: Convolutional Networks for Biomedical Image Segmentation

"The architecture consists of a contracting path to capturecontext and a symmetric expanding path that enables precise localiza-tion. We show that such a network can be trained end-to-end from veryfew images and outperforms the prior best method (a sliding-windowconvolutional network) on the ISBI challenge for segmentation of neu-ronal structures in electron microscopic stacks..."

• located in branch 'ruili'-> Run_model.ipynb

Please see our Contributors file for more details.

• Clint Morris

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