TY - JOUR
T1 - Plus disease in retinopathy of prematurity
T2 - Convolutional neural network performance using a combined neural network and feature extraction approach
AU - maging Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium
AU - Yildiz, Veysi M.
AU - Tian, Peng
AU - Yildiz, Ilkay
AU - Brown, James M.
AU - Kalpathy-Cramer, Jayashree
AU - Dy, Jennifer
AU - Ioannidis, Stratis
AU - Erdogmus, Deniz
AU - Ostmo, Susan
AU - Kim, Sang Jin
AU - Chan, R. V.Paul
AU - Campbell, J. Peter
AU - Chiang, Michael F.
N1 - Funding Information:
This project was supported by grants R01EY19474, K12EY027720, and P30EY10572 from the National Institutes of Health; by grants SCH-1622679, SCH-1622542, and SCH-1622536 from the National Science Foundation; and by unrestricted departmental funding and a Career Development Award from Research to Prevent Blindness (JPC).
Funding Information:
The Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium includes the following: Michael F. Chiang, Susan Ostmo,. This project was supported by grants R01EY19474, K12EY027720, and P30EY10572 from the National Institutes of Health; by grants SCH-1622679, SCH-1622542, and SCH-1622536 from the National Science Foundation; and by unrestricted departmental funding and a Career Development Award from Research to Prevent Blindness (JPC). Sang Jin Kim, Kemal Sonmez, and J. Peter Campbell (Oregon Health & Science University, Portland, OR); R. V. Paul Chan and Karyn Jonas (University of Illinois at Chicago, Chicago, IL); Jason Horowitz, Osode Coki, Cheryl-Ann Eccles, and Leora Sarna (Columbia University, New York, NY); Anton Orlin (Weill Cornell Medical College, New York, NY); Audina Berrocal and Catherin Negron (Bascom Palmer Eye Institute, Miami, FL); Kimberly Denser, Kristi Cumming, Tammy Osentoski, Tammy Check, and Mary Zajechowski (William Beaumont Hospital, Royal Oak, MI); Thomas Lee, Evan Kruger, and Kathryn McGovern (Children?s Hospital Los Angeles, Los Angeles, CA); Charles Simmons, Raghu Murthy, and Sharon Galvis (Cedars Sinai Hospital, Los Angeles, CA); Jerome Rotter, Ida Chen, Xiaohui Li, Kent Taylor, and Kaye Roll (Los Angeles Biomedical Research Institute, Los Angeles, CA); Jayashree Kalpathy-Cramer (Massachusetts General Hospital, Boston, MA); Deniz Erdogmus and Stratis Ioannidis (Northeastern University, Boston, MA); Maria Ana Martinez-Castellanos, Samantha Salinas-Longoria, Rafael Romero, Andrea Arriola, Francisco Olguin-Manriquez, Miroslava Meraz-Gutierrez, Carlos M. Dulanto-Reinoso, and Cristina Montero-Mendoza (Asociacion para Evitar la Ceguera en Mexico, Mexico City, Mexico).
Publisher Copyright:
© 2020 The Authors.
PY - 2020
Y1 - 2020
N2 - Purpose: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels, is the most important feature to determine treatment-requiring ROP. We aimed to create a complete, publicly available and feature-extraction-based pipeline, I-ROP ASSIST, that achieves convolutional neural network (CNN)-like performance when diagnosing plus disease from retinal images. Methods: We developed two datasets containing 100 and 5512 posterior retinal images, respectively. After segmenting retinal vessels, we detected the vessel centerlines. Then, we extracted features relevant to ROP, including tortuosity and dilation measures, and used these features in the classifiers including logistic regression, support vector machine and neural networks to assess a severity score for the input. We tested our system with fivefold cross-validation and calculated the area under the curve (AUC) metric for each classifier and dataset. Results: For predicting plus versus not-plus categories, we achieved 99% and 94% AUC on the first and second datasets, respectively. For predicting pre-plus or worse versus normal categories, we achieved 99% and 88% AUC on the first and second datasets, respectively. The CNN method achieved 98% and 94% for predicting two categories on the second dataset. Conclusions: Our system combining automatic retinal vessel segmentation, tracing, feature extraction and classification is able to diagnose plus disease in ROP with CNN-like performance. Translational Relevance: The high performance of I-ROP ASSIST suggests potential applications in automated and objective diagnosis of plus disease.
AB - Purpose: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels, is the most important feature to determine treatment-requiring ROP. We aimed to create a complete, publicly available and feature-extraction-based pipeline, I-ROP ASSIST, that achieves convolutional neural network (CNN)-like performance when diagnosing plus disease from retinal images. Methods: We developed two datasets containing 100 and 5512 posterior retinal images, respectively. After segmenting retinal vessels, we detected the vessel centerlines. Then, we extracted features relevant to ROP, including tortuosity and dilation measures, and used these features in the classifiers including logistic regression, support vector machine and neural networks to assess a severity score for the input. We tested our system with fivefold cross-validation and calculated the area under the curve (AUC) metric for each classifier and dataset. Results: For predicting plus versus not-plus categories, we achieved 99% and 94% AUC on the first and second datasets, respectively. For predicting pre-plus or worse versus normal categories, we achieved 99% and 88% AUC on the first and second datasets, respectively. The CNN method achieved 98% and 94% for predicting two categories on the second dataset. Conclusions: Our system combining automatic retinal vessel segmentation, tracing, feature extraction and classification is able to diagnose plus disease in ROP with CNN-like performance. Translational Relevance: The high performance of I-ROP ASSIST suggests potential applications in automated and objective diagnosis of plus disease.
KW - CNN
KW - Feature-based
KW - ROP
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U2 - 10.1167/tvst.9.2.10
DO - 10.1167/tvst.9.2.10
M3 - Article
AN - SCOPUS:85082195588
VL - 9
JO - Translational Vision Science and Technology
JF - Translational Vision Science and Technology
SN - 2164-2591
IS - 2
M1 - 10
ER -