Integrative Analysis on Histopathological Image For Identifying Cellular Heterogeneity

被引:0
|
作者
Chang, Young Hwan [1 ]
Thibault, Guillaume [1 ]
Johnson, Brett [1 ]
Margolin, Adam [1 ]
Gray, Joe W. [1 ]
机构
[1] Oregon Hlth & Sci Univ, Biomed Engn, Portland, OR 97201 USA
来源
MEDICAL IMAGING 2017: DIGITAL PATHOLOGY | 2017年 / 10140卷
关键词
Spatial Pattern analysis; Heterogeneity; H&E stained image;
D O I
10.1117/12.2250428
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
This study has brought together image processing, clustering and spatial pattern analysis to quantitatively analyze hematoxylin and eosin-stained (H&E) tissue sections. A mixture of tumor and normal cells (intratumoral heterogeneity) as well as complex tissue architectures of most samples complicate the interpretation of their cytological profiles. To address these challenges, we develop a simple but effective methodology for quantitative analysis for H&E section. We adopt comparative analyses of spatial point patterns to characterize spatial distribution of different nuclei types and complement cellular characteristics analysis. We demonstrate that tumor and normal cell regions exhibit significant differences of lymphocytes spatial distribution or lymphocyte infiltration pattern.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Deep learning for cellular image analysis
    Moen, Erick
    Bannon, Dylan
    Kudo, Takamasa
    Graf, William
    Covert, Markus
    Van Valen, David
    NATURE METHODS, 2019, 16 (12) : 1233 - 1246
  • [32] Deep learning for cellular image analysis
    Erick Moen
    Dylan Bannon
    Takamasa Kudo
    William Graf
    Markus Covert
    David Van Valen
    Nature Methods, 2019, 16 : 1233 - 1246
  • [33] The conundrum posed by cellular heterogeneity in analysis of human neuroblastoma
    Ross, RA
    Spengler, BA
    JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE, 2004, 96 (16): : 1192 - 1193
  • [34] Biomedical Image Analysis at the Cellular Level
    Acton, Scott
    2008 INTERNATIONAL MACHINE VISION AND IMAGE PROCESSING CONFERENCE, PROCEEDINGS, 2008, : 27 - 27
  • [35] Identifying and Measuring Heterogeneity Across the Studies in Meta-Analysis
    Zhao, Jia-Guo
    JOURNAL OF HAND SURGERY-AMERICAN VOLUME, 2013, 38A (07): : 1449 - 1450
  • [36] Image Analysis for Identifying Mosquito Breeding Grounds
    Mehra, Maanit
    Bagri, Aditya
    Jiang, Xiaofan
    Ortiz, Jorge
    2016 IEEE INTERNATIONAL CONFERENCE ON SENSING, COMMUNICATION AND NETWORKING (SECON WORKSHOPS), 2016,
  • [37] Identifying damaged soybeans by color image analysis
    Shatadal, P
    Tan, J
    APPLIED ENGINEERING IN AGRICULTURE, 2003, 19 (01) : 65 - 69
  • [38] Integrative review; identifying the evidence base for policymaking and analysis in health care
    Kennedy, Catriona
    O'Reilly, Pauline
    O'Connell, Rhona
    O'Leary, Denise
    Fealy, Gerard
    Hegarty, Josephine-Mary
    Brady, Anne-Marie
    Nicholson, Emma
    McNamara, Martin
    Casey, Mary
    JOURNAL OF ADVANCED NURSING, 2019, 75 (12) : 3231 - 3245
  • [39] Histopathological Image in Hematology
    Yu, Guohua
    Huang, Xin
    Huo, Yuqing
    Zhang, Tingguo
    Gao, Zifen
    TURKISH JOURNAL OF HEMATOLOGY, 2015, 32 (01) : 98 - 99
  • [40] Integrative single-cell metabolomics and phenotypic profiling reveals metabolic heterogeneity of cellular oxidation and senescence
    Wang, Ziyi
    Ge, Siyuan
    Liao, Tiepeng
    Yuan, Man
    Qian, Wenwei
    Chen, Qi
    Liang, Wei
    Cheng, Xiawei
    Zhou, Qinghua
    Ju, Zhenyu
    Zhu, Hongying
    Xiong, Wei
    NATURE COMMUNICATIONS, 2025, 16 (01)