A Deep Learning Pipeline for Nucleus Segmentation
Autor: | Sigal Shachar, Justin Kim, Laurent Ozbun, Tom Misteli, Iain D. C. Fraser, Luis M. Franco, Sun Jing, Manasi Gadkari, Prabhakar R. Gudla, Gianluca Pegoraro, Kyunghun Lee, George Zaki |
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Rok vydání: | 2020 |
Předmět: |
0301 basic medicine
Histology Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Machine learning computer.software_genre Article Pathology and Forensic Medicine Image (mathematics) 03 medical and health sciences 0302 clinical medicine Deep Learning Image Processing Computer-Assisted Preprocessor Segmentation 030304 developmental biology Cell Nucleus 0303 health sciences business.industry Deep learning Cell Biology Image segmentation Pipeline (software) 030104 developmental biology Workflow 030220 oncology & carcinogenesis Artificial intelligence Transfer of learning business computer 030217 neurology & neurosurgery |
Zdroj: | Cytometry A |
ISSN: | 1552-4930 |
Popis: | 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. |
Databáze: | OpenAIRE |
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