RT Journal Article SR Electronic T1 Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images JF bioRxiv FD Cold Spring Harbor Laboratory SP 335216 DO 10.1101/335216 A1 Juan C. Caicedo A1 Jonathan Roth A1 Allen Goodman A1 Tim Becker A1 Kyle W. Karhohs A1 Claire McQuin A1 Shantanu Singh A1 Anne E. Carpenter YR 2018 UL http://biorxiv.org/content/early/2018/05/31/335216.abstract 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.