Towards pixel-to-pixel deep nucleus detection in microscopy images.

Journal: BMC bioinformatics
Published Date: Sep 14, 2019

Abstract

BACKGROUND: Nucleus is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed.

Authors

  • Fuyong Xing
  • Yuanpu Xie
  • Xiaoshuang Shi
    Shandong Industrial Engineering Laboratory of Biogas Production & Utilization, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China. E-mail: lujun@qibebt.ac.cn (Jun Lu), yangzm@qibebt.ac.cn (Zhiman Yang).
  • Pingjun Chen
    J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States.
  • Zizhao Zhang
    Department of Computer and Information Science and Engineering, University of Florida, FL 32611, USA.
  • Lin Yang
    National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China.

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