Early detection of retinopathy of prematurity stage using deep learning approach

Supriti Mulay; Keerthi Ram; Mohanasankar Sivaprakasam; Anand Vinekar

doi:10.1117/12.2512719

13 March 2019 Early detection of retinopathy of prematurity stage using deep learning approach

Supriti Mulay, Keerthi Ram, Mohanasankar Sivaprakasam, Anand Vinekar

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109502Z (2019) https://doi.org/10.1117/12.2512719
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Retinopathy of Prematurity (ROP) is a fibrovascular proliferative disorder, which affects the developing peripheral retinal vasculature of premature infants. Early detection of ROP is possible in stage 1 and stage 2 characterized by demarcation line and ridge with width which separates vascularised retina and the peripheral retina. To detect demarcation line/ ridge from neonatal retinal images is a complex task because of low contrast images. In this paper we focus on detection of ridge, the important landmark in ROP diagnosis, using Convolutional Neural Network(CNN). Our contribution is to use a CNN-based model Mask R-CNN for demarcation line/ridge detection allowing clinicians to detect ROP stage 2 better. The proposed system applies a pre-processing step of image enhancement to overcome poor image quality. In this study we use labelled neonatal images and we explore the use of CNN to localize ridge in these images. We used a dataset of 220 images of 45 babies from the KIDROP project. The system was trained on 175 retinal images with ground truth segmentation of ridge region. The system was tested on 45 images and reached detection accuracy of 0.88, showing that deep learning detection with pre-processing by image normalization allows robust detection of ROP in early stages.

Citation Download Citation

Supriti Mulay, Keerthi Ram, Mohanasankar Sivaprakasam, and Anand Vinekar "Early detection of retinopathy of prematurity stage using deep learning approach", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502Z (13 March 2019); https://doi.org/10.1117/12.2512719

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