This repository contains an implementation of the UNet architecture for live cell segmentation using the LIVECell dataset.
Light microscopy is a cheap, accessible, non-invasive modality that, when combined with well-established protocols of two-dimensional cell culture, facilitates high-throughput quantitative imaging to study biological phenomena. Accurate segmentation of individual cells enables the exploration of complex biological questions, but this requires sophisticated imaging processing pipelines due to the low contrast and high object density.
Deep learning-based methods are considered state-of-the-art for most computer vision problems but require vast amounts of annotated data, for which there is no suitable resource available in the field of label-free cellular imaging. To address this gap, we present LIVECell, a high-quality, manually annotated, and expert-validated dataset that is the largest of its kind to date, consisting of over 1.6 million cells from a diverse set of cell morphologies and culture densities.
- main.ipynb: The Jupyter notebook containing the entire code for the UNet implementation and live cell segmentation.
The LIVECell dataset is used in this project. LIVECell is a high-quality, manually annotated, and expert-validated dataset consisting of over 1.6 million cells from a diverse set of cell morphologies and culture densities.