{"title":"Multiscale spatial segregation analysis in digital images of biofilms","authors":"Iztok Dogsa, Ines Mandic-Mulec","doi":"10.1016/j.bioflm.2023.100157","DOIUrl":null,"url":null,"abstract":"<div><p>Quantifying the degree of spatial segregation of two bacterial strains in mixed biofilms is an important topic in microbiology. Spatial segregation is dependent on spatial scale as two strains may appear to be well mixed if observed from a distance, but a closer look can reveal strong separation. Typically, this information is encoded in a digital image that represents the binary system, e.g., a microscopy image of a two species biofilm. To decode spatial segregation information, we have developed quantitative measures for evaluating the degree of the spatial scale-dependent segregation of two bacterial strains in a digital image. The constructed algorithm is based on the new segregation measures and overcomes drawbacks of existing approaches for biofilm segregation analysis. The new approach is implemented in a freely available software and was successfully applied to biofilms of two strains and bacterial suspensions for detection of the different spatial scale-dependent segregation levels.</p></div>","PeriodicalId":55844,"journal":{"name":"Biofilm","volume":null,"pages":null},"PeriodicalIF":5.9000,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/50/16/main.PMC10542597.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biofilm","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590207523000540","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Quantifying the degree of spatial segregation of two bacterial strains in mixed biofilms is an important topic in microbiology. Spatial segregation is dependent on spatial scale as two strains may appear to be well mixed if observed from a distance, but a closer look can reveal strong separation. Typically, this information is encoded in a digital image that represents the binary system, e.g., a microscopy image of a two species biofilm. To decode spatial segregation information, we have developed quantitative measures for evaluating the degree of the spatial scale-dependent segregation of two bacterial strains in a digital image. The constructed algorithm is based on the new segregation measures and overcomes drawbacks of existing approaches for biofilm segregation analysis. The new approach is implemented in a freely available software and was successfully applied to biofilms of two strains and bacterial suspensions for detection of the different spatial scale-dependent segregation levels.