Resampling approaches for the quantitative analysis of spatially distributed cells  

作　　者：Giorgio Bertolazzi Michele Tumminello Gaia Morello Beatrice Belmonte Claudio Tripodo 

机构地区：[1]Department of Economics,Business and Statistics,University of Palermo,Palermo,Italy [2]Tumor Immunology Unit,Department of Health Sciences,University of Palermo,Palermo,Italy [3]Histopathology Unit,Institute of Molecular Oncology Foundation(IFOM)ETS-The AIRC Institute of Molecular Oncology,Milan,Italy

出　　处：《Data Intelligence》2024年第1期104-119,共16页数据智能（英文）

基　　金：This study has been supported by the Italian Ministry of Education,University and Research(MIUR)through the“PON Research and Innovation 2014–2020”to G.B.;by the National Biodiversity Future Center(NBFC)CN00000033(CUP UNIPA B73C22000790001),and through the OBIND project N.086202000366(CUP G29J18000700007)to M.T.;by the Italian Foundation for Cancer Research(AIRC)through the 5×1000 I.D.22759 Grant and AIRC Accelerator Award ID.24296 to C.T.;by the Italian Ministry of Education,University and Research(MIUR)Grant 2017K7FSYB to C.T.

摘　　要：Image segmentation is a crucial step in various image analysis pipelines and constitutes one of the cutting-edge areas of digital pathology.The advent of quantitative analysis has enabled the evaluation of millions of individual cells in tissues,allowing for the combined assessment of morphological features,biomarker expression,and spatial context.The recorded cells can be described as a point pattern process.However,the classical statistical approaches to point pattern processes prove unreliable in this context due to the presence of multiple irregularly-shaped interstitial cell-devoid spaces in the domain,which correspond to anatomical features(e.g.vessels,lipid vacuoles,glandular lumina)or tissue artefacts(e.g.tissue fractures),and whose coordinates are unknown.These interstitial spaces impede the accurate calculation of the domain area,resulting in biased clustering measurements.Moreover,the mistaken inclusion of empty regions of the domain can directly impact the results of hypothesis testing.The literature currently lacks any introduced bias correction method to address interstitial cell-devoid spaces.To address this gap,we propose novel resampling methods for testing spatial randomness and evaluating relationships among different cell populations.Our methods obviate the need for domain area estimation and provide non-biased clustering measurements.We created the SpaceR software(https://github.com/GBertolazzi/SpaceR)to enhance the accessibility of our methodologies.

关 键 词：Digital pathology spatial cytometry spatial clustering spatial randomness RESAMPLING 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]