PT  - JOURNAL ARTICLE
AU  - Juan C. Caicedo
AU  - Jonathan Roth
AU  - Allen Goodman
AU  - Tim Becker
AU  - Kyle W. Karhohs
AU  - Claire McQuin
AU  - Shantanu Singh
AU  - Anne E. Carpenter
TI  - Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images
AID  - 10.1101/335216
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 335216
4099  - http://biorxiv.org/content/early/2018/05/31/335216.short
4100  - http://biorxiv.org/content/early/2018/05/31/335216.full
AB  - Identifying nuclei is often a critical first step in analyzing microscopy images of cells, and classical image processing algorithms are still commonly used for this task. Recent studies indicate that deep learning may yield superior accuracy, but its performance has not been evaluated for high-throughput nucleus segmentation in large collections of images. We compare two deep learning strategies for identifying nuclei in fluorescence microscopy images (U-Net and DeepCell) alongside a classical approach that does not use machine learning. We measure accuracy, types of errors, and computational complexity to benchmark these approaches on a large data set. We publicly release the set of 23,165 manually annotated nuclei and source code to reproduce the results. Our evaluation shows that U-Net outperforms both pixel-wise classification networks and classical algorithms. Although deep learning requires more computation and annotation time than classical algorithms, it improves accuracy and halves the number of errors.