Automated detection and analysis of fluorescent in situ hybridization spots depicted in digital microscopic images of Pap-smear specimens.
Fluorescence in situ hybridization (FISH) technology has been widely recognized as a promising molecular and biomedical optical imaging tool to screen and diagnose cervical cancer. However, manual FISH analysis is time-consuming and may introduce large inter-reader variability. In this study, a computerized scheme is developed and tested. It automatically detects and analyzes FISH spots depicted on microscopic fluorescence images. The scheme includes two stages: (1) a feature-based classification rule to detect useful interphase cells, and (2) a knowledge-based expert classifier to identify splitting FISH spots and improve the accuracy of counting independent FISH spots. The scheme then classifies detected analyzable cells as normal or abnormal. In this study, 150 FISH images were acquired from Pap-smear specimens and examined by both an experienced cytogeneticist and the scheme. The results showed that (1) the agreement between the cytogeneticist and the scheme was 96.9% in classifying between analyzable and unanalyzable cells (Kappa=0.917), and (2) agreements in detecting normal and abnormal cells based on FISH spots were 90.5% and 95.8% with Kappa=0.867. This study demonstrated the feasibility of automated FISH analysis, which may potentially improve detection efficiency and produce more accurate and consistent results than manual FISH analysis.
Wang
Xingwei
X
University of Oklahoma, School of Electrical and Computer Engineering, Center for Bioengineering, Norman, Oklahma 73019, USA.
Zheng
Bin
B
Li
Shibo
S
Zhang
Roy
R
Mulvihill
John J
JJ
Chen
Wei R
WR
Liu
Hong
H
D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D017404:In Situ Hybridization, Fluorescence / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D065006:Papanicolaou Test; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D002583:Uterine Cervical Neoplasms / Q000473:pathology*; D014626:Vaginal Smears / Q000379:methods*