Applied Microscopy

  • Volume 51
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  • Pages.4.1-4.12
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  • 2021
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  • 2287-5123(pISSN)
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  • 2287-4445(eISSN)
Korean Society of Microscopy (한국현미경학회)
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Automated 3D scoring of fluorescence in situ hybridization (FISH) using a confocal whole slide imaging scanner

  • Ziv Frankenstein (Department of Pathology, Memorial Sloan Kettering Cancer Center) ;
  • Naohiro Uraoka (Department of Pathology, Memorial Sloan Kettering Cancer Center) ;
  • Umut Aypar (Department of Pathology, Memorial Sloan Kettering Cancer Center) ;
  • Ruth Aryeequaye (Department of Pathology, Memorial Sloan Kettering Cancer Center) ;
  • Mamta Rao (Department of Pathology, Memorial Sloan Kettering Cancer Center) ;
  • Meera Hameed (Department of Pathology, Memorial Sloan Kettering Cancer Center) ;
  • Yanming Zhang (Department of Pathology, Memorial Sloan Kettering Cancer Center) ;
  • Yukako Yagi (Department of Pathology, Memorial Sloan Kettering Cancer Center)
  • Received : 2021.02.16
  • Accepted : 2021.03.29
  • Published : 2021.12.31
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Abstract

Fluorescence in situ hybridization (FISH) is a technique to visualize specific DNA/RNA sequences within the cell nuclei and provide the presence, location and structural integrity of genes on chromosomes. A confocal Whole Slide Imaging (WSI) scanner technology has superior depth resolution compared to wide-field fluorescence imaging. Confocal WSI has the ability to perform serial optical sections with specimen imaging, which is critical for 3D tissue reconstruction for volumetric spatial analysis. The standard clinical manual scoring for FISH is labor-intensive, time-consuming and subjective. Application of multi-gene FISH analysis alongside 3D imaging, significantly increase the level of complexity required for an accurate 3D analysis. Therefore, the purpose of this study is to establish automated 3D FISH scoring for z-stack images from confocal WSI scanner. The algorithm and the application we developed, SHIMARIS PAFQ, successfully employs 3D calculations for clear individual cell nuclei segmentation, gene signals detection and distribution of break-apart probes signal patterns, including standard break-apart, and variant patterns due to truncation, and deletion, etc. The analysis was accurate and precise when compared with ground truth clinical manual counting and scoring reported in ten lymphoma and solid tumors cases. The algorithm and the application we developed, SHIMARIS PAFQ, is objective and more efficient than the conventional procedure. It enables the automated counting of more nuclei, precisely detecting additional abnormal signal variations in nuclei patterns and analyzes gigabyte multi-layer stacking imaging data of tissue samples from patients. Currently, we are developing a deep learning algorithm for automated tumor area detection to be integrated with SHIMARIS PAFQ.

Keywords

Acknowledgement

The authors would like to thank the National Institutes of Health/National Cancer Institute, the Warren Alpert Foundation, 3DHISTECH for the technical support and all the pathologists who provided their time and expertise in evaluating the images and providing useful comments.

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