Automated cell lineage reconstruction using label-free 4D microscopy

Here we describe embGAN, a deep learning pipeline that addresses the challenge of automated cell detection and tracking in label-free 3D time-lapse imaging. The embGAN requires no manual data annotation for training, learns robust detections that exhibits a high degree of scale invariance and generalizes well to images acquired in multiple labs on multiple instruments., Images were acquired using an Olympus IX83 inverted frame equipped with a UPLSAPO60xs2 objective, a Visitech iSIM multipoint confocal scanner, ASI MX2000XYZ stage, and a Hamamatsu Orca Fusion camera. The mCherry channel of JIM113 was acquired using 594 nm excitation and a 605 nm long-pass emission filter using 150 ms exposures and a laser power that was empirically tuned to not cause any qualitative developmental delays versus un-imaged control embryos and maintain a ~100% hatch rate for imaged embryos. Embryos were imaged every 60 seconds with a 750 nm z spacing. DIC images were acquired with the Visitech scanner in brightfield bypass mode, a 50 ms camera exposure and the LED light source tuned to not generate any saturated pixels in the image. DIC illumination was generated using an Olympus UCD8 manual condenser equipped with a U525 oil immersion 1.4 NA top lens and a DICTHR tilt-shift slider. Images were acquired using a micro-manager and cropped and converted to individual tiff volu..., , # Data from: Automated cell lineage reconstruction using label-free 4D microscopy

[https://doi.org/10.5061/dryad.zcrjdfnkz](https://doi.org/10.5061/dryad.zcrjdfnkz)

The dataset contains training data for developing and evaluating deep learning approaches to automated cell detection and tracking in 3D DIC images.

## Description of the data and file structure

Data is split into folders containing test and training data. Each folder contains 2D 8-bit images in TIF format with matching pairs of images acquired using DIC or fluorescence microscopy where cell nuclei are labeled with mCherry fluorescence. For training embGAN using our codebase, labels for the training and test sets must first be generated using the provided stardist.py script ([https://github.com/shahlab-ucla/embGAN)](https://github.com/shahlab-ucla/embGAN)

## Code/Software

This data was acquired for use with our embGAN codebase ([https://github.com/shahlab-ucla/embGAN)](https://github.com/shahlab-ucla/embGAN) but can ea...