Automated ROP Diagnostic System based on Comparisons and U-Net Segmentation
Authors: 
Peng Tian, Jennifer Dy, Deniz Erdogmus, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis IoannidisAuthors Info & Claims
Pages 33 - 36
Abstract
Retinopathy of Prematurity (ROP) is a disease affecting premature infants and may lead to childhood blindness. Due to lack of trained ophthalmologists, developing a fully automated ROP diagnostic system can significantly benefit the infants affected by ROP. Based on manually segmented features, previous work produces severity scores for ROP with excellent prediction accuracy. However, when automated segmentation employed, a significant accuracy drop is observed. In this paper, we show that U-Net segmentation, which is automated, comes at no accuracy loss.
References
[1]
Esra Ataer-Cansizoglu, Veronica Bolon-Canedo, J Peter Campbell, Alican Bozkurt, Deniz Erdogmus, Jayashree Kalpathy-Cramer, Samir Patel, Karyn Jonas, RV Paul Chan, Susan Ostmo, 2015. Computer-based image analysis for plus disease diagnosis in retinopathy of prematurity: performance of the “i-ROP” system and image features associated with expert diagnosis. TVST 4, 6 (2015), 5–5.
[2]
Hannah Blencowe, Joy E Lawn, Thomas Vazquez, Alistair Fielder, and Clare Gilbert. 2013. Preterm-associated visual impairment and estimates of retinopathy of prematurity at regional and global levels for 2010. Pediatric 74(2013), 35–49.
[3]
Ralph Allan Bradley and Milton E Terry. 1952. Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons. Biometrika 39, 3/4 (1952).
[4]
James M Brown, J Peter Campbell, Andrew Beers, Ken Chang, Susan Ostmo, RV Paul Chan, Jennifer Dy, Deniz Erdogmus, Stratis Ioannidis, Jayashree Kalpathy-Cramer, 2018. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA ophthalmology 136, 7 (2018), 803–810.
[5]
Cryotherapy for Retinopathy of Prematurity Cooperative Group and others. 1988. Multicenter Trial of Cryotherapy for Retinopathy of Prematurity: Preliminary Results. Archives of Ophthalmology 106, 4 (1988), 471–479.
[6]
Alejandro F Frangi, Wiro J Niessen, Koen L Vincken, and Max A Viergever. 1998. Multiscale vessel enhancement filtering. In MICCAI. Springer, 130–137.
[7]
James A Hanley and Barbara J McNeil. 1982. The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) curve.Radiology 143, 1 (1982).
[8]
M Elizabeth Hartnett and John S Penn. 2012. Mechanisms and Management of Retinopathy of Prematurity. New England Journal of Medicine 367, 26 (2012).
[9]
Ann Hellström, Lois EH Smith, and Olaf Dammann. 2013. Retinopathy of Prematurity. The lancet 382, 9902 (2013), 1445–1457.
[10]
International Committee for the Classification of Retinopathy of Prematurity. 2005. The international classification of retinopathy of prematurity revisited. JAMA Ophthalmology 123, 7 (2005), 991–999.
[11]
Jonathan Javitt, Ronald Dei Cas, and Yen-pin Chiang. 1993. Cost–Effectiveness of Screening and Cryotherapy for Threshold Retinopathy of Prematurity. Pediatrics 91, 5 (1993), 859–866.
[12]
Jayashree Kalpathy-Cramer, J Peter Campbell, Deniz Erdoğmuş, Peng Tian, Dharanish Kedarisetti, Chace Moleta, James D Reynolds, Kelly Hutcheson, Michael J Shapiro, Michael X Repka, 2016. Plus Disease in Retinopathy of Prematurity: Improving Diagnosis by Ranking Disease Severity and Using Quantitative Image Analysis. Ophthalmology 123, 11 (2016), 2345–2351.
[13]
Pietro Perona and Jitendra Malik. 1990. Scale-space and edge detection using anisotropic diffusion. TPAMI 12, 7 (1990), 629–639.
[14]
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In MICCAI. Springer, 234–241.
[15]
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In CVPR. 1–9.
[16]
Peng Tian, Yuan Guo, Jayashree Kalpathy-Cramer, Susan Ostmo, John Peter Campbell, Michael F Chiang, Jennifer Dy, Deniz Erdogmus, and Stratis Ioannidis. 2019. A severity score for retinopathy of prematurity. In KDD. 1809–1819.
[17]
İlkay Yıldız, Peng Tian, Jennifer Dy, Deniz Erdoğmuş, James Brown, Jayashree Kalpathy-Cramer, Susan Ostmo, J Peter Campbell, Michael F Chiang, and Stratis Ioannidis. 2019. Classification and comparison via neural networks. Neural Networks 118(2019), 65–80.
[18]
Veysi M Yildiz, Peng Tian, Ilkay Yildiz, James M Brown, Jayashree Kalpathy-Cramer, Jennifer Dy, Stratis Ioannidis, Deniz Erdogmus, Susan Ostmo, Sang Jin Kim, 2020. Plus Disease in Retinopathy of Prematurity: Convolutional Neural Network Performance Using a Combined Neural Network and Feature Extraction Approach. TVST 9, 2 (2020), 10–10.
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Published: 29 June 2021
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PETRA '21
PETRA '21: The 14th PErvasive Technologies Related to Assistive Environments Conference
June 29 - July 2, 2021
Corfu, Greece
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