Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: A comprehensive review

被引：325
作者：
Xing F. [1 ]
Yang L. [1 ]
机构：
[1] Department of Electrical and Computer Engineering, J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, 32611, FL
来源：
IEEE Reviews in Biomedical Engineering | 2016年 / 9卷
基金：
美国国家卫生研究院;
关键词：
Cell; detection; digital pathology; histopathology; microscopy images; nucleus; segmentation;
D O I：
10.1109/RBME.2016.2515127
中图分类号：
学科分类号：
摘要：
Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation. © 2008-2011 IEEE.
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收藏
页码：234 / 263
页数：29
相关论文
共 178 条
  • [1] Rojo M.G., Punys V., Slodkowska J., Schrader T., Daniel C., Blobel B., Digital pathology in Europe: Coordinating patient care and research efforts, Stud. Health Technol. Inform., 150, pp. 997-1001, (2009)
  • [2] Rojo M.G., State of the art and trends for digital pathology, Stud. Health Technol. Inform., 179, pp. 15-28, (2012)
  • [3] May M., A better lens on disease: Computerized pathology slides may help doctors make faster and more accurate diagnoses, Sci. Amer., 302, pp. 74-77, (2010)
  • [4] Katouzian A., Angelini E.D., Carlier S.G., Suri J.S., Navab N., Laine A.F., A state-of-the-art review on segmentation algorithms in intravascular ultrasound (IVUS) images, IEEE Trans. Inf. Technol. Biomed., 16, 5, pp. 823-834, (2012)
  • [5] Principe J.C., Brockmeier A.J., Representing and decomposing neural potential signals, Current Opinion Neurobiol., 31, pp. 13-17, (2015)
  • [6] Yang L., Qi X., Xing F., Kurc T., Saltz J., Foran D.J., Parallel content-based sub-image retrieval using hierarchical searching, Bioinformatics, 30, 7, pp. 996-1002, (2014)
  • [7] Lopez C., Lejeune M., Bosch R., Korzynska A., Garcia-Rojo M., Salvado M.T., Alvaro T., Callau C., Roso A., Jaen J., Digital image analysis in breast cancer: An example of an automated methodology and the effects of image compression, Stud. Health Technol. Inform., 179, pp. 155-171, (2012)
  • [8] Foran D.J., Et al., Imageminer: A software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology, J. Amer. Med. Inform. Assoc., 8, 4, pp. 403-415, (2011)
  • [9] Yang L., Chen W., Meer P., Salaru G., Goodell L.A., Berstis V., Foran D.J., Virtual microscopy and grid-enabled decision support for large-scale analysis of imaged pathology specimens, IEEE Trans. Inf. Technol. Biomed., 13, 4, pp. 636-644, (2009)
  • [10] Yang L., Chen W., Meer P., Salaru G., Feldman M.D., Foran D.J., High throughput analysis of breast cancer specimens on the grid, Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervention, 4791, pp. 617-625, (2007)
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