title: Automatic Segmentation of Polypoidal Choroidal Vasculopathy from Indocyanine Green Angiography Using Spatial and Temporal Patterns.
abstract: OBJECTIVE
  To develop a computer-aided diagnostic tool for automated detection and quantification of polypoidal regions in indocyanine green angiography (ICGA) images.
  
  
  METHODS
  The ICGA sequences of 59 polypoidal choroidal vasculopathy (PCV) treatment-naïve patients from five Asian countries (Hong Kong, Singapore, South Korea, Taiwan, and Thailand) were provided by the EVEREST study. The ground truth was provided by the reading center for the presence of polypoidal regions. The proposed detection algorithm used both temporal and spatial features to characterize the severity of polypoidal lesions in ICGA sequences. Leave-one-out cross validation was carried out so that each patient was used once as the validation sample. For each patient, a fixed detection threshold of 0.5 on the severity was applied to obtain sensitivity, specificity, and balanced accuracy with respect to the ground truth.
  
  
  RESULTS
  Our system achieved an average accuracy of 0.9126 (sensitivity = 0.9125, specificity = 0.9127) for detection of polyps in the 59 ICGA sequences. Among the total of 222 features extracted from ICGA sequence, the spatial variances exhibited best discriminative power in distinguishing between polyp and nonpolyp regions. The results also indicated the importance of combining spatial and temporal features to further improve detection accuracy.
  
  
  CONCLUSIONS
  The developed software provided a means of detecting and quantifying polypoidal regions in ICGA images for the first time.
  
  
  CONCLUSIONS
  This preliminary study demonstrated a computer-aided diagnostic tool, which enables objective evaluation of PCV and its progression. Ophthalmologists can easily visualize the polypoidal regions and obtain quantitative information about polyps by using the proposed system.
authors: Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 621, Taiwan.
authors: Wei-Yang
authors: WY
authors: Lin
authors: Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 621, Taiwan.
authors: Sheng-Chang
authors: SC
authors: Yang
authors: Department of Ophthalmology, Taipei Veterans General Hospital, Taiwan ; School of Medicine, National Yang Ming University, Taipei 11217, Taiwan.
authors: Shih-Jen
authors: SJ
authors: Chen
authors: Computer Science Department, Iona College, New Rochelle, New York, USA.
authors: Chia-Ling
authors: CL
authors: Tsai
authors: Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 621, Taiwan.
authors: Shuo-Zhao
authors: SZ
authors: Du
authors: National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore.
authors: Tock-Han
authors: TH
authors: Lim
keywords: Polypoidal choroidal vasculopathy; computer-aided diagnostic tool; indocyanine green angiography