title: Quantitative diagnosis of cervical neoplasia using fluorescence lifetime imaging on haematoxylin and eosin stained tissue sections.
abstract: The use of conventional fluorescence microscopy for characterizing tissue pathological states is limited by overlapping spectra and the dependence on excitation power and fluorophore concentration. Fluorescence lifetime imaging microscopy (FLIM) can overcome these limitations due to its insensitivity to fluorophore concentration, excitation power and spectral similarity. This study investigates the diagnosis of early cervical cancer using FLIM and a neural network extreme learning machine classifier. A concurrently high sensitivity and specificity of 92.8% and 80.2%, respectively, were achieved. The results suggest that the proposed technique can be used to supplement the traditional histopathological examination of early cervical cancer.
authors.affiliation: Nanyang Technological University, School of Electrical & Electronic Engineering, Singapore 639798.
authors.forename: Jun
authors.initials: J
authors.lastname: Gu
authors.forename: Chit Yaw
authors.initials: CY
authors.lastname: Fu
authors.forename: Beng Koon
authors.initials: BK
authors.lastname: Ng
authors.forename: Sirajudeen so
authors.initials: Ss
authors.lastname: Gulam Razul
authors.forename: Soo Kim
authors.initials: SK
authors.lastname: Lim
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003287:Contrast Media / Q000032:analysis; D004801:Eosine Yellowish-(YS) / Q000032:analysis; D005069:Evaluation Studies as Topic; D005260:Female; D005456:Fluorescent Dyes / Q000032:analysis; D006416:Hematoxylin / Q000032:analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D009361:Neoplasm Invasiveness; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013194:Staining and Labeling / Q000379:methods; D002583:Uterine Cervical Neoplasms / Q000737:chemistry / Q000473:pathology*
chemical_list: D003287:Contrast Media; D005456:Fluorescent Dyes; D004801:Eosine Yellowish-(YS); D006416:Hematoxylin
keywords: FLIM; cervical cancer; epithelium; extreme learning machine; fluorescence lifetime