Open Source Infrastructure for Automatic Cell Segmentation

TL;DR

This paper introduces an open-source deep learning infrastructure based on UNet for automated cell segmentation, integrated into DeepChem, providing a user-friendly tool that is accurate, versatile, and accessible for biological research.

Contribution

It presents a new open-source implementation of UNet for cell segmentation within DeepChem, improving accessibility and benchmarking its robustness across datasets.

Findings

High accuracy across multiple datasets

Robust performance under different imaging conditions

Enhanced accessibility for researchers

Abstract

Automated cell segmentation is crucial for various biological and medical applications, facilitating tasks like cell counting, morphology analysis, and drug discovery. However, manual segmentation is time-consuming and prone to subjectivity, necessitating robust automated methods. This paper presents open-source infrastructure, utilizing the UNet model, a deep-learning architecture noted for its effectiveness in image segmentation tasks. This implementation is integrated into the open-source DeepChem package, enhancing accessibility and usability for researchers and practitioners. The resulting tool offers a convenient and user-friendly interface, reducing the barrier to entry for cell segmentation while maintaining high accuracy. Additionally, we benchmark this model against various datasets, demonstrating its robustness and versatility across different imaging conditions and cell types.