Inferring statistical properties of 3D cell geometry from 2D slices

被引:9
|
作者
Sharp, Tristan A. [1 ]
Merkel, Matthias [2 ]
Manning, M. Lisa [2 ,3 ]
Liu, Andrea J. [1 ]
机构
[1] Univ Penn, Dept Phys & Astron, Philadelphia, PA 19104 USA
[2] Syracuse Univ, Phys Dept, Syracuse, NY USA
[3] Syracuse Biomat Inst, Syracuse, NY USA
来源
PLOS ONE | 2019年 / 14卷 / 02期
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
GRAIN-SIZE; RECONSTRUCTION; SEGMENTATION; DYNAMICS; MEDIA; MODEL;
D O I
10.1371/journal.pone.0209892
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Although cell shape can reflect the mechanical and biochemical properties of the cell and its environment, quantification of 3D cell shapes within 3D tissues remains difficult, typically requiring digital reconstruction from a stack of 2D images. We investigate a simple alternative technique to extract information about the 3D shapes of cells in a tissue; this technique connects the ensemble of 3D shapes in the tissue with the distribution of 2D shapes observed in independent 2D slices. Using cell vertex model geometries, we find that the distribution of 2D shapes allows clear determination of the mean value of a 3D shape index. We analyze the errors that may arise in practice in the estimation of the mean 3D shape index from 2D imagery and find that typically only a few dozen cells in 2D imagery are required to reduce uncertainty below 2%. Even though we developed the method for isotropic animal tissues, we demonstrate it on an anisotropic plant tissue. This framework could also be naturally extended to estimate additional 3D geometric features and quantify their uncertainty in other materials.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] From 2D images to 3D face geometry
    Lengagne, R
    Tarel, JP
    Monga, O
    PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION, 1996, : 301 - 306
  • [2] 3D IMAGE RECONSTRUCTION FROM 2D CT SLICES
    Kamencay, Patrik
    Zachariasova, Martina
    Hudec, Robert
    Benco, Miroslav
    Radil, Roman
    2014 3DTV-CONFERENCE: THE TRUE VISION - CAPTURE, TRANSMISSION AND DISPLAY OF 3D VIDEO (3DTV-CON), 2014,
  • [3] Statistical characterisation of inclusions in metals: from 2D to 3D
    Cetin, A.
    Naess, A.
    MATERIALS SCIENCE AND TECHNOLOGY, 2012, 28 (08) : 965 - 970
  • [4] 3D Reconstruction From 2D CineMRI Orthogonal Slices: A Feasibility Study
    Paganelli, C.
    Lee, D.
    Kipritidis, J.
    Greer, P.
    Riboldi, M.
    Keall, P.
    MEDICAL PHYSICS, 2015, 42 (06) : 3606 - 3607
  • [5] A Simple Method for 3D Thyroid Reconstruction from 2D Ultrasound Slices
    Ciora, Radu Adrian
    Neamtu, Bogdan
    Sofariu, Ciprian
    Dospinescu, Cristina
    Barbu, Andreea
    Banciu, Daniel Dumitriu
    2019 E-HEALTH AND BIOENGINEERING CONFERENCE (EHB), 2019,
  • [6] From 2D to 3D
    Steven De Feyter
    Nature Chemistry, 2011, 3 (1) : 14 - 15
  • [7] GEOMETRY AND FOAMS - 2D DYNAMICS AND 3D STATICS
    AVRON, JE
    LEVINE, D
    PHYSICAL REVIEW LETTERS, 1992, 69 (01) : 208 - 211
  • [8] SEMANTIC CONSTRAINT MODELER FOR 2D AND 3D GEOMETRY
    JIAO Guofang LIU Shenquan CAD LabInstitute of Computing Technology Academia SinicaBeijing PRChina
    Computer Aided Drafting,Design and Manufacturing, 1992, Design and Manufacturing.1992 (01) : 46 - 57
  • [9] 3D Molecular Geometry Analysis with 2D Graphs
    Xu, Zhao
    Xie, Yaochen
    Luo, Youzhi
    Zhang, Xuan
    Xu, Xinyi
    Liu, Meng
    Dickerson, Kaleb
    Deng, Cheng
    Nakata, Maho
    Ji, Shuiwang
    PROCEEDINGS OF THE 2024 SIAM INTERNATIONAL CONFERENCE ON DATA MINING, SDM, 2024, : 343 - 351
  • [10] SEMANTIC CONSTRAINT MODELER FOR 2D AND 3D GEOMETRY
    JIAO Guofang LIU Shenquan CAD Lab.
    CADDM, 1992, (01) : 46 - 57