/DEEP-TIMING

Deep Learning Solution to TIMING (Time-lapse Microscopy in Nanowell Grids) Project

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DEEP-TIMING

Deep Learning Solution to TIMING (Time-lapse Microscopy in Nanowell Grids) Project created and maintained by Single-Cell Lab, FARSight Group, STIM Laboratory and HULA Lab at University of Houston, Houston, Texas.

Introduction

Deep learning algorithms show top performance on image classification and object detection tasks over general-purpose image domains (IMAGENET, MSCOCO). This repository explores the performance of state-of-art image classifier and object detector on a different domain consisting of time-lapse microscopy images collected in TIMING project. Extended from the initial exploration, we created annotated TIMING datasets efficiently using man-in-the-loop fashion and our customized annotation tool, to support the fine-tuning of the deep learning models. And an end-to-end TIMING pipeline is developed to detect, track and quantify cells with robustness and flexibility.

Highlights

  • High-quality annotated TIMING Data. Leveraging unsupervised algorithm and man-in-the-loop fashion, we generated more than 5,000 nanowell images with cell bounding box annotations, 180 sequences of time-lapse nanowell frames with bounding box as well as track ID annotations and 72,000 cropped cell patches with apoptosis status (positive/negative) annotations. Annotated data can be found in the Deep TIMING Supplementary Materials folder.

  • Cell detection leveraging phase-contrast channel and Faster R-CNN algorithm. Trained state-of-art cell detector using Faster R-CNN model. Tested cell detection performance using different combination of input channels.

  • Label-free apoptosis classification using CNN and LSTM Models Implemented state-of-art convolutional neural net classifier. Used reccurent model (LSTM) to improve sequence classification performance considering temporal dependency.

  • Customized visualization tool from TIMING2-board

## Requirements:
  • 64-bit computer with at least 2GHz processor running Windows, Linux or Mac
  • CUDA-enabled GPU, memory >= 8 GB recommended
  • Hard drive storage >= 2TB, solid-state hard drive strongly recommended

Installation

(1) Download this repository and put the folder say /HOME_DIRECTORY/DEEP-TIMING/

(2) Download auxiliary modules and test data, and copy the folders to /HOME_DIRECTORY/DEEP-TIMING/ and to /HOME_DIRECTORY/DEEP-TIMING/DATA/raw/. (create folder DATA and subdirectory /raw/ and /results respectively.)

(3) Download and install Anaconda

(4) Create the environments for TIMING2-pipeline and TIMING2-board, open Anaconda Prompt, change to DEEP-TIMING home directory /HOME_DIRECTORY/DEEP-TIMING/, and type python setup_env.py

(5) Set up DT-pipeline, in the prompt, type activate DT-pipeline, and then type python setup_DT_pipeline.py

(6) Open another Anaconda Prompt, change to DEEP-TIMING home directory, type activate DT-board, and then type python setup_DT_board.py (independent from step 5)

(7) Have a cup of coffee, will be ready in several minutes.

Usage

1.DT-Pipeline wil do cell detection, tracking and feature calculation with phase contrast channels; follow the steps in Deep-TIMING-Pipeline-Demo.ipynb;

2.The evaluation data, scripts are included in the Supplementary Materials. And the evaluation steps can be fould here

Contact

Email: hlu9@uh.edu

License

This code is free for non-commerical use only