Premium
Clustering methods applied in the detection of Ki67 hot‐spots in whole tumor slide images: An efficient way to characterize heterogeneous tissue‐based biomarkers
Author(s) -
Lopez Xavier Moles,
Debeir Olivier,
Maris Calliope,
Rorive Sandrine,
Roland Isabelle,
Saerens Marco,
Salmon Isabelle,
Decaestecker Christine
Publication year - 2012
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.22085
Subject(s) - cluster analysis , digitization , computer science , pattern recognition (psychology) , cytometry , artificial intelligence , high resolution , digital pathology , scanner , identification (biology) , computer vision , medicine , biology , flow cytometry , botany , remote sensing , immunology , geology
Whole‐slide scanners allow the digitization of an entire histological slide at very high resolution. This new acquisition technique opens a wide range of possibilities for addressing challenging image analysis problems, including the identification of tissue‐based biomarkers. In this study, we use whole‐slide scanner technology for imaging the proliferating activity patterns in tumor slides based on Ki67 immunohistochemistry. Faced with large images, pathologists require tools that can help them identify tumor regions that exhibit high proliferating activity, called “hot‐spots” (HSs). Pathologists need tools that can quantitatively characterize these HS patterns. To respond to this clinical need, the present study investigates various clustering methods with the aim of identifying Ki67 HSs in whole tumor slide images. This task requires a method capable of identifying an unknown number of clusters, which may be highly variable in terms of shape, size, and density. We developed a hybrid clustering method, referred to as Seedlink. Compared to manual HS selections by three pathologists, we show that Seedlink provides an efficient way of detecting Ki67 HSs and improves the agreement among pathologists when identifying HSs. © 2012 International Society for Advancement of Cytometry