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615854a · Feb 10, 2023

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LIVECell dataset

LIVECell dataset

Abstract

Light microscopy combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena. Accurate segmentation of individual cells in images enables exploration of complex biological questions, but can require sophisticated imaging processing pipelines in cases of low contrast and high object density. Deep learning-based methods are considered state-of-the-art for image segmentation but typically require vast amounts of annotated data, for which there is no suitable resource available in the field of label-free cellular imaging. Here, we present LIVECell, a large, high-quality, manually annotated and expert-validated dataset of phase-contrast images, consisting of over 1.6 million cells from a diverse set of cell morphologies and culture densities. To further demonstrate its use, we train convolutional neural network-based models using LIVECell and evaluate model segmentation accuracy with a proposed a suite of benchmarks.

Run demo

$ docker compose exec dethub python tools/image_demo.py configs/projects/livecell/demo/A172_Phase_A7_1_00d00h00m_1.tif configs/projects/livecell/yolox/yolox_s_livecell.py --weights https://github.com/okotaku/dethub-weights/releases/download/v0.0.1/yolox_s_livecell-b3f4347f.pth --out-dir configs/projects/livecell/demo/result

plot

Prepare datasets

  1. Download LIVECell dataset from https://github.com/sartorius-research/LIVECell
wget http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/images.zip
  1. Download coco format json kaggle datasets.
kaggle datasets download takuok/livecellcocoformat

*We prepared coco format files from this script. Original annotation files from dataset from https://github.com/sartorius-research/LIVECell

wget http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell/livecell_coco_train.json
wget http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell/livecell_coco_val.json
wget http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell/livecell_coco_test.json
  1. Unzip the files as follows
data/livecell
├── images
├── livecell_coco_train_8class.json
├── livecell_coco_val_8class.json
└── livecell_coco_test_8class.json

Run train

Set env variables

$ export DATA_DIR=/path/to/data

Start a docker container

$ docker compose up -d dethub

Run train

# single gpu
$ docker compose exec dethub mim train mmdet configs/projects/livecell/yolox/yolox_s_livecell.py
# multi gpus
$ docker compose exec dethub mim train mmdet configs/projects/livecell/yolox/yolox_s_livecell.py --gpus 2 --launcher pytorch

Acknowledgement

Kaggle Sartorius - Cell Instance Segmentation 1st place solution