Automatic nuclei segmentation and spatial FISH analysis for cancer detection.
Spatial analysis of gene localization using fluorescent in-situ hybridization (FISH) labeling is potentially a new method for early cancer detection. Current methodology relies heavily upon accurate segmentation of cell nuclei and FISH signals in tissue sections. While automatic FISH signal detection is a relatively simpler task, accurate nuclei segmentation is still a manual process which is fairly time consuming and subjective. Hence to use the methodology as a clinical application, it is necessary to automate all the steps involved in the process of spatial FISH signal analysis using fast, robust and accurate image processing techniques. In this work, we describe an intelligent framework for analyzing the FISH signals by coupling hybrid nuclei segmentation algorithm with pattern recognition algorithms to automatically identify well segmented nuclei. Automatic spatial statistical analysis of the FISH spots was carried out on the output from the image processing and pattern recognition unit. Results are encouraging and show that the method could evolve into a full fledged clinical application for cancer detection.
Nandy
Kaustav
K
Optical Microscopy and Analysis Laboratory, Advanced Technology program, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA. nandyk@ncifcrf.gov
Gudla
Prabhakar R
PR
Meaburn
Karen J
KJ
Misteli
Tom
T
Lockett
Stephen J
SJ
D001331:Automation; D002467:Cell Nucleus / Q000473:pathology*; D006801:Humans; D017404:In Situ Hybridization, Fluorescence / Q000379:methods*; D007211:Indoles / Q000378:metabolism; D009369:Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D010363:Pattern Recognition, Automated
D007211:Indoles; C007293:DAPI