Premium
A Deep Learning Pipeline for Nucleus Segmentation
Author(s) -
Zaki George,
Gudla Prabhakar R.,
Lee Kyunghun,
Kim Justin,
Ozbun Laurent,
Shachar Sigal,
Gadkari Manasi,
Sun Jing,
Fraser Iain D. C.,
Franco Luis M.,
Misteli Tom,
Pegoraro Gianluca
Publication year - 2020
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.24257
Subject(s) - deep learning , computer science , pipeline (software) , segmentation , artificial intelligence , preprocessor , workflow , transfer of learning , image segmentation , machine learning , pattern recognition (psychology) , database , programming language
Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datasets that have been augmented, we have designed a computational pipeline to systematically compare different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the composition, size, and preprocessing of the training image dataset, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, off‐the‐shelf deep learning models pretrained on large image datasets. We envision a practical scenario where deep learning nuclear segmentation models trained in this way can be shared across a laboratory, facility, or institution, and continuously improved by training them on progressively larger and varied image datasets. Our work provides computational tools and a practical framework for deep learning‐based biological image segmentation using small annotated image datasets. Published [2020]. This article is a U.S. Government work and is in the public domain in the USA