Details

Autor(en) / Beteiligte

• Yang, Linfeng
• Ghosh, Rajarshi P
• Franklin, J. Matthew
• Chen, Simon
• You, Chenyu
• Narayan, Raja R
• Melcher, Marc L
• Liphardt, Jan T
• Asthagiri, Anand R.

Titel

NuSeT: A deep learning tool for reliably separating and analyzing crowded cells

Ist Teil von

• PLoS computational biology, 2020-09, Vol.16 (9), p.e1008193-e1008193

Ort / Verlag

San Francisco: Public Library of Science

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Segmenting cell nuclei within microscopy images is a ubiquitous task in biological research and clinical applications. Unfortunately, segmenting low-contrast overlapping objects that may be tightly packed is a major bottleneck in standard deep learning-based models. We report a Nuclear Segmentation Tool (NuSeT) based on deep learning that accurately segments nuclei across multiple types of fluorescence imaging data. Using a hybrid network consisting of U-Net and Region Proposal Networks (RPN), followed by a watershed step, we have achieved superior performance in detecting and delineating nuclear boundaries in 2D and 3D images of varying complexities. By using foreground normalization and additional training on synthetic images containing non-cellular artifacts, NuSeT improves nuclear detection and reduces false positives. NuSeT addresses common challenges in nuclear segmentation such as variability in nuclear signal and shape, limited training sample size, and sample preparation artifacts. Compared to other segmentation models, NuSeT consistently fares better in generating accurate segmentation masks and assigning boundaries for touching nuclei.